A Review of Machine Learning and IoT in Smart Transportation
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paper_title: The role of big data in smart city

paper_content:

We provide a vision of big data analytics to support smart cities.We proposed future business model with the aim of managing big data for smart city.We identify and discuss business and technological research challenges.We provide a description of existing communication technologies used in smart cities. The expansion of big data and the evolution of Internet of Things (IoT) technologies have played an important role in the feasibility of smart city initiatives. Big data offer the potential for cities to obtain valuable insights from a large amount of data collected through various sources, and the IoT allows the integration of sensors, radio-frequency identification, and Bluetooth in the real-world environment using highly networked services. The combination of the IoT and big data is an unexplored research area that has brought new and interesting challenges for achieving the goal of future smart cities. These new challenges focus primarily on problems related to business and technology that enable cities to actualize the vision, principles, and requirements of the applications of smart cities by realizing the main smart environment characteristics. In this paper, we describe the state-of-the-art communication technologies and smart-based applications used within the context of smart cities. The visions of big data analytics to support smart cities are discussed by focusing on how big data can fundamentally change urban populations at different levels. Moreover, a future business model of big data for smart cities is proposed, and the business and technological research challenges are identified. This study can serve as a benchmark for researchers and industries for the future progress and development of smart cities in the context of big data.
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paper_title: Sensor Mania ! The Internet of Things , Wearable Computing , Objective Metrics , and the Quantified Self 2 . 0

paper_content:

The number of devices on the Internet exceeded the number of people on the Internet in 2008, and is estimated to reach 50 billion in 2020. A wide-ranging Internet of Things (IOT) ecosystem is emerging to support the process of connecting real-world objects like buildings, roads, household appliances, and human bodies to the Internet via sensors and microprocessor chips that record and transmit data such as sound waves, temperature, movement, and other variables. The explosion in Internet-connected sensors means that new classes of technical capability and application are being created. More granular 24/7 quantified monitoring is leading to a deeper understanding of the internal and external worlds encountered by humans. New data literacy behaviors such as correlation assessment, anomaly detection, and high-frequency data processing are developing as humans adapt to the different kinds of data flows enabled by the IOT. The IOT ecosystem has four critical functional steps: data creation, information generation, meaning-making, and action-taking. This paper provides a comprehensive review of the current and rapidly emerging ecosystem of the Internet of Things (IOT).
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paper_title: Choice of effective messaging protocols for IoT systems: MQTT, CoAP, AMQP and HTTP

paper_content:

The standard and real-time communication technology is an unalloyed inevitability for the development of Internet of Things (IoT) applications. However, the selection of a standard and effective messaging protocol is a challenging and daunting task for any organisation because it depends on the nature of the IoT system and its messaging requirements. Copious messaging protocols have been developed and employed by various organisations based on their requirements in the last two decades. Though, none of them is able to support all messaging requirements of all types of IoT systems. Messaging protocol is an ongoing dilemma for the IoT industry; consequently, it is important to understand the pros and cons of the widely accepted and emerging messaging protocols for IoT systems to determine their best-fit scenarios. Therefore, this paper presents an evaluation of the four established messaging protocols MQTT, CoAP, AMQP and HTTP for IoT systems. Firstly, it presents the broad comparison among these messaging protocols to introduce their characteristics comparatively. Afterwards, it performs a further in-depth and relative analysis based on some interrelated criteria to gain insight into their strengths and limitations. Thus, based on this detailed evaluation, the user can decide their appropriate usage in various IoT systems according to their requirements and suitability.
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paper_title: From Databases to Big Data

paper_content:

There is a tremendous amount of buzz around the concept of "big data." In this article, the author discusses the origins of this trend, the relationship between big data and traditional databases and data processing platforms, and some of the new challenges that big data presents.
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paper_title: Cognitive Internet of Things: A New Paradigm Beyond Connection

paper_content:

Current research on Internet of Things (IoT) mainly focuses on how to enable general objects to see, hear, and smell the physical world for themselves, and make them connected to share the observations. In this paper, we argue that only connected is not enough, beyond that, general objects should have the capability to learn, think, and understand both physical and social worlds by themselves. This practical need impels us to develop a new paradigm, named cognitive Internet of Things (CIoT), to empower the current IoT with a “brain” for high-level intelligence. Specifically, we first present a comprehensive definition for CIoT, primarily inspired by the effectiveness of human cognition. Then, we propose an operational framework of CIoT, which mainly characterizes the interactions among five fundamental cognitive tasks: perception-action cycle, massive data analytics, semantic derivation and knowledge discovery, intelligent decision-making, and on-demand service provisioning. Furthermore, we provide a systematic tutorial on key enabling techniques involved in the cognitive tasks. In addition, we also discuss the design of proper performance metrics on evaluating the enabling techniques. Last but not the least, we present the research challenges and open issues ahead. Building on the present work and potentially fruitful future studies, CIoT has the capability to bridge the physical world (with objects, resources, etc.) and the social world (with human demand, social behavior, etc.), and enhance smart resource allocation, automatic network operation, and intelligent service provisioning.
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paper_title: Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

paper_content:

Abstract With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic traffic planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K -means separately, the proposed transportation planning is based on the real-time IoT and GIS data, which is processed by DBN and K -means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K -means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic traffic network planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal planning of traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.
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paper_title: IoT-based multifunctional Scalable real-time Enhanced Road Side Unit for Intelligent Transportation Systems

paper_content:

This paper presents the design of a modular, Scalable Enhanced Road Side Unit for use as part of a comprehensive Intelligent Transportation System based on the concept of Internet of Things (IoT). The proposed unit is designed to collect information using a variety of sensors and an on-board camera. The collected information can then be uploaded to a central server for actions such as speed limit adjustment, metering routes to reduce vehicle congestion and emissions, and issuing weather advisory warnings. Communication between an individual unit and the central server are performed using existing wireless cellular networks. Each module also contains an RF module for communication with both other nearby units and other parties equipped with an appropriate receiver module. Lab testing with an initial prototype confirmed the feasibility of the proposed communication links and evaluation of the current real-time software implementation showed an average total CPU resource usage of 34%, giving room to expand functionality with additional tasks.
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paper_title: Robust Parking Space Detection Considering Inter-Space Correlation

paper_content:

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class support vector machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov random field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.
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paper_title: A Cross Layer Protocol for Traffic Management in Social Internet of Vehicles

paper_content:

Abstract One of the important members of Internet of Things (IoT); transportation vehicles are utilizing sudden advancements of communication technology. Now days, to make the Social IoT (SIoT) a reality there are many on-going world-wide research initiatives and standardization efforts. Internet of Vehicles (IoV) is an extension to the concept of SIoT that proposes Social Internet of Vehicles (SIoV). SIoV is an example of SIoT, where Smart vehicles are the objects of SIoT that builds social relationship and interchanges information to enhance the driving knowledge and gives various services to the drivers. In this paper, the proposed Vehicular Social Networks based on the VIoT consist of a large number of sensors that transmit data wirelessly. However, the very high heterogeneity in hardware capabilities of things and QoS requirements for different applications limits the performance of classical layered protocol solutions and the existing cross-layer solutions for wireless sensor networks. The results show that the proposed VSNP (Vehicular Social Network Protocol) for VIoT based on WSN solution can achieve a global communication optimum and outperforms existing layered solutions. The novel cross-layer module is a primary step towards providing efficient traffic management and reliable vehicle to vehicle communication in the SIoT. We have proposed an algorithm to control traffic congestion and road safety for VIoT.
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paper_title: IoT-Based Smart Parking for Smart Cities

paper_content:

The advance in computing brought also solutions to problems that canimprove citizen’s life. One significant problem involves parking vehicles,which is related to traffic congestion, air pollution and driver stress. Dueto that fact and the increase in the number of vehicles, it becomesnecessary a smart parking proposal that mitigates those problems andinteracts with the drivers. In this paper we will describe a smart parkingsystem able to gather quantitative information and provide it trough anextensible platform to drivers and other smart city applications. Theproposed system is implemented disposing ultrasonic sensors, a web service,the FIWARE platform, and a mobile application to Android devices, on athree-layer architecture. The paper also aims to present the results of aproof of concept, which shows that this approach is appropriate formonitoring parking systems and exhibit the parking lot situation at anygiven moment. Thus, it is the first step to propose a comprehensive solutionfor smart cities.
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paper_title: SUMO – Simulation of Urban MObility An Overview

paper_content:

SUMO is an open source traffic simulation package including net import and demand modeling components. We describe the current state of the package as well as future developments and extensions. SUMO helps to investigate several research topics e.g. route choice and traffic light algorithm or simulating vehicular communication. Therefore the framework is used in different projects to simulate automatic driving or traffic management strategies.
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paper_title: Identification of Road Surface Conditions using IoT Sensors and Machine Learning

paper_content:

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.
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paper_title: An extended set of Haar-like features for rapid object detection

paper_content:

Recently Viola et al. [2001] have introduced a rapid object detection. scheme based on a boosted cascade of simple feature classifiers. In this paper we introduce a novel set of rotated Haar-like features. These novel features significantly enrich the simple features of Viola et al. and can also be calculated efficiently. With these new rotated features our sample face detector shows off on average a 10% lower false alarm rate at a given hit rate. We also present a novel post optimization procedure for a given boosted cascade improving on average the false alarm rate further by 12.5%.
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paper_title: Ensemble Methods: Foundations and Algorithms

paper_content:

An up-to-date, self-contained introduction to a state-of-the-art machine learning approach, Ensemble Methods: Foundations and Algorithms shows how these accurate methods are used in real-world tasks. It gives you the necessary groundwork to carry out further research in this evolving field. After presenting background and terminology, the book covers the main algorithms and theories, including Boosting, Bagging, Random Forest, averaging and voting schemes, the Stacking method, mixture of experts, and diversity measures. It also discusses multiclass extension, noise tolerance, error-ambiguity and bias-variance decompositions, and recent progress in information theoretic diversity. Moving on to more advanced topics, the author explains how to achieve better performance through ensemble pruning and how to generate better clustering results by combining multiple clusterings. In addition, he describes developments of ensemble methods in semi-supervised learning, active learning, cost-sensitive learning, class-imbalance learning, and comprehensibility enhancement.
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paper_title: Traffic Flow Forecasting for Urban Work Zones

paper_content:

None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long-term and short-term traffic flow forecasting applications were investigated. Long-term forecast involves forecasting 24 h in advance using historical traffic data, and short-term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real-time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e., I-270, and a signalized arterial, i.e., MO-141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long-term and short-term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look-back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look-back intervals at the current location only.
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paper_title: Traffic accident detection using random forest classifier

paper_content:

The Internet of Things (IoT) has been growing in recent years with the improvements in several different applications in the military, marine, intelligent transportation, smart health, smart grid, smart home and smart city domains. Although IoT brings significant advantages over traditional information and communication (ICT) technologies for Intelligent Transportation Systems (ITS), these applications are still very rare. Although there is a continuous improvement in road and vehicle safety, as well as improvements in IoT, the road traffic accidents have been increasing over the last decades. Therefore, it is necessary to find an effective way to reduce the frequency and severity of traffic accidents. Hence, this paper presents an intelligent traffic accident detection system in which vehicles exchange their microscopic vehicle variables with each other. The proposed system uses simulated data collected from vehicular ad-hoc networks (VANETs) based on the speeds and coordinates of the vehicles and then, it sends traffic alerts to the drivers. Furthermore, it shows how machine learning methods can be exploited to detect accidents on freeways in ITS. It is shown that if position and velocity values of every vehicle are given, vehicles' behavior could be analyzed and accidents can be detected easily. Supervised machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forests (RF) are implemented on traffic data to develop a model to distinguish accident cases from normal cases. The performance of RF algorithm, in terms of its accuracy, was found superior to ANN and SVM algorithms. RF algorithm has showed better performance with 91.56% accuracy than SVM with 88.71% and ANN with 90.02% accuracy.
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paper_title: Bayesian Network Classifiers

paper_content:

Bayesian network classifiers are used extensively both in academia and in a wide range of industrial applications and domains. This article first reviews the main concepts behind statistical pattern classifiers and Bayesian networks, including the main methods for the automated induction of these models. It then continues with a description of the application of Bayesian networks as statistical pattern classifiers. It discusses the advantages of Bayesian network classifiers over other types of classifiers, and provides up-to-date references covering the material and examples of applications. Readers with basic knowledge of statistics will acquire the necessary background to explore more technical papers and decide on whether Bayesian network classifiers are the right technology for their particular problem and domain. ::: ::: ::: Keywords: ::: ::: pattern recognition; ::: pattern classification; ::: statistics; ::: machine learning; ::: Bayesian networks; ::: graphical models; ::: model fitting; ::: maximum likelihood; ::: Bayesian methods
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paper_title: Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

paper_content:

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.
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paper_title: Vehicle motion intention reasoning using cooperative perception on urban road

paper_content:

As autonomous vehicles are venturing on the urban road, reasoning other vehicles' motion intention is always essential for anticipatory decision-making. This paper considers a new class of motion intention reasoning problem using cooperative perception. Specifically, thanks to the extended perception range contributed by cooperative perception, the authors can explicitly consider the behavior correlations among multiple vehicles, where each vehicle's motion intention can be inferred using both the temporal observations and the other vehicles' motion intention expectation. As such, a Simplified variant of Coupled Hidden Markov Model (S-CHMM) is proposed for motion intention inference. The experiment on an autonomous vehicle shows that the proposed algorithm can improve the motion intention accuracy and provide efficient overtaking triggering for autonomous driving on urban road.
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paper_title: Robust Parking Space Detection Considering Inter-Space Correlation

paper_content:

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class support vector machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov random field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.
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paper_title: A tutorial on hidden Markov models and selected applications in speech recognition

paper_content:

This tutorial provides an overview of the basic theory of hidden Markov models (HMMs) as originated by L.E. Baum and T. Petrie (1966) and gives practical details on methods of implementation of the theory along with a description of selected applications of the theory to distinct problems in speech recognition. Results from a number of original sources are combined to provide a single source of acquiring the background required to pursue further this area of research. The author first reviews the theory of discrete Markov chains and shows how the concept of hidden states, where the observation is a probabilistic function of the state, can be used effectively. The theory is illustrated with two simple examples, namely coin-tossing, and the classic balls-in-urns system. Three fundamental problems of HMMs are noted and several practical techniques for solving these problems are given. The various types of HMMs that have been studied, including ergodic as well as left-right models, are described. >
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paper_title: Study of Group Route Optimization for IoT Enabled Urban Transportation Network

paper_content:

Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. Although there are several factors that may cause traffic congestion, inefficiency in traffic management is always the main issue. Additionally, the most traditional methods of resolving traffic congestion or rerouting algorithm are mainly designed for individuals' benefits, by simply planning a driver's route based on minimum travel time or shortest path accordingly. There is lack of consideration in group benefit or urban development. However, with the development of technologies in Internet of Things (IoT), vehicle to vehicle (V2V) or Vehicle to Infrastructure (V2I) communications, group based routing becomes achievable. Instead of optimizing the routing path for individual drivers, this paper studies how to develop a new method to provide new routing method based on vehicles' similarities in a specific urban's transportation environment.
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paper_title: Traffic Flow Forecasting for Urban Work Zones

paper_content:

None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long-term and short-term traffic flow forecasting applications were investigated. Long-term forecast involves forecasting 24 h in advance using historical traffic data, and short-term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real-time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e., I-270, and a signalized arterial, i.e., MO-141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long-term and short-term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look-back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look-back intervals at the current location only.
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paper_title: Data clustering: 50 years beyond K-means

paper_content:

Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into a system of ranked taxa: domain, kingdom, phylum, class, etc. Cluster analysis is the formal study of methods and algorithms for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is to find structure in data and is therefore exploratory in nature. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, K-means, was first published in 1955. In spite of the fact that K-means was proposed over 50 years ago and thousands of clustering algorithms have been published since then, K-means is still widely used. This speaks to the difficulty in designing a general purpose clustering algorithm and the ill-posed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semi-supervised clustering, ensemble clustering, simultaneous feature selection during data clustering, and large scale data clustering.
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paper_title: Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

paper_content:

Abstract With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic traffic planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K -means separately, the proposed transportation planning is based on the real-time IoT and GIS data, which is processed by DBN and K -means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K -means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic traffic network planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal planning of traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.
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paper_title: Short-term traffic prediction using fuzzy c-means and cellular automata in a wide-area road network

paper_content:

In this paper, we propose a short-term prediction method for forecasting traffic in a time-series manner for up to one hour ahead for all roads in a wide-area road network. The results of our research enable traffic to be simulated for a wide-area road network based on actual traffic data by combining fuzzy clustering and cellular automata. On application to an actual road network with 3,405 links, the proposed technique was found to be superior to the nearest neighborhood method for traffic prediction at times of congestion outbreak and alleviation and heavy congestion.
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paper_title: An intelligent smartphone based approach using IoT for ensuring safe driving

paper_content:

Road accident is a widespread problem all over the world. The number of the vehicle is increasing rapidly. Therefore, the probability of an accident is also increasing. So in this paper, we propose an IoT-based system for providing safe driving. It will collect data using smartphone and show the driver about the condition of the road. We have developed an Android based application which will collect data from a vehicle and send it to its nearest IoT-Fog server for processing the data quickly. We also developed two algorithms using k-means clustering approach which find the location of road anomalies and accident prone area. The result will be shown in Google map of driver's smartphone. This information will be stored in cloud for further use. We have tested our proposed system through statistical as well as experimental evaluations. Simulation result shows that our approach can provide better performance compared to state-of-the-art methods.
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paper_title: Machine learning approach for predicting bumps on road

paper_content:

In today's days, due to increase in number of vehicles the probability of accidents are also increasing. The user should be aware of the road circumstances for safety purpose. Several methods requires installing dedicated hardware in vehicle which are expensive. so we have designed a Smart-phone based method which uses a Accelerometer and GPS sensors to analyze the road conditions. The designed system is called as Bumps Detection System(BDS) which uses Accelerometer for pothole detection and GPS for plotting the location of potholes on Google Map. Drivers will be informed in advance about count of potholes on road. we have assumed some threshold values on z-axis(Experimentally Derived)while designing the system. To justify these threshold values we have used a machine learning approach. The k means clustering algorithm is applied on the training data to build a model. Random forest classifier is used to evaluate this model on the test data for better prediction.
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paper_title: Data clustering: a review

paper_content:

Clustering is the unsupervised classification of patterns (observations, data items, or feature vectors) into groups (clusters). The clustering problem has been addressed in many contexts and by researchers in many disciplines; this reflects its broad appeal and usefulness as one of the steps in exploratory data analysis. However, clustering is a difficult problem combinatorially, and differences in assumptions and contexts in different communities has made the transfer of useful generic concepts and methodologies slow to occur. This paper presents an overviewof pattern clustering methods from a statistical pattern recognition perspective, with a goal of providing useful advice and references to fundamental concepts accessible to the broad community of clustering practitioners. We present a taxonomy of clustering techniques, and identify cross-cutting themes and recent advances. We also describe some important applications of clustering algorithms such as image segmentation, object recognition, and information retrieval.
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paper_title: Backpropagation through time: what it does and how to do it

paper_content:

Basic backpropagation, which is a simple method now being widely used in areas like pattern recognition and fault diagnosis, is reviewed. The basic equations for backpropagation through time, and applications to areas like pattern recognition involving dynamic systems, systems identification, and control are discussed. Further extensions of this method, to deal with systems other than neural networks, systems involving simultaneous equations, or true recurrent networks, and other practical issues arising with the method are described. Pseudocode is provided to clarify the algorithms. The chain rule for ordered derivatives-the theorem which underlies backpropagation-is briefly discussed. The focus is on designing a simpler version of backpropagation which can be translated into computer code and applied directly by neutral network users. >
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paper_title: Traffic Flow Forecasting for Urban Work Zones

paper_content:

None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long-term and short-term traffic flow forecasting applications were investigated. Long-term forecast involves forecasting 24 h in advance using historical traffic data, and short-term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real-time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e., I-270, and a signalized arterial, i.e., MO-141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long-term and short-term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look-back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look-back intervals at the current location only.
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paper_title: Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

paper_content:

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.
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paper_title: Variation Based Online Travel Time Prediction Using Clustered Neural Networks

paper_content:

This paper proposes a variation-based online travel time prediction approach using clustered Neural Networks with traffic vectors extracted from raw detector data as the input variables. Different from previous studies, the proposed approach decomposes the corridor travel time into two parts: 1) the base term, which is predicted by a fuzzy membership-value-weighted average of the clustered historical data to reflect the primary traffic pattern in the corridor; and 2) the variation term, which is predicted through the calibrated cluster-based artificial neural network model to capture the actual traffic fluctuation. To evaluate the effectiveness of the proposed approach, this paper has conducted intensive numerical experiments with simulated data from the microscopic simulator CORSIM. Experimental results under various traffic volume levels have revealed the potentials for the proposed method to be applied in online corridor travel time prediction.
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paper_title: What Is Machine Learning

paper_content:

Machine learning is an evolving branch of computational algorithms that are designed to emulate human intelligence by learning from the surrounding environment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biology to biomedical and medical applications. More than half of the patients with cancer receive ionizing radiation (radiotherapy) as part of their treatment, and it is the main treatment modality at advanced stages of local disease. Radiotherapy involves a large set of processes that not only span the period from consultation to treatment but also extend beyond that to ensure that the patients have received the prescribed radiation dose and are responding well. The degrees of the complexity of these processes can vary and may involve several stages of sophisticated human-machine interactions and decision making, which would naturally invite the use of machine learning algorithms into optimizing and automating these processes including but not limited to radiation physics quality assurance, contouring and treatment planning, image-guided radiotherapy, respiratory motion management, treatment response modeling, and outcomes prediction. The ability of machine learning algorithms to learn from current context and generalize into unseen tasks would allow improvements in both the safety and efficacy of radiotherapy practice leading to better outcomes.
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paper_title: A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence

paper_content:

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.
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paper_title: Autonomously detecting and classifying traffic accident hotspots

paper_content:

The number of road traffic fatalities has been steadily increasing since 2001 and is currently the eighth leading cause of death globally, with the loss of life of 1.2 million people each year according to the World Health Organization (WHO) [11]. In addition, the National Highway Traffic Safety Administration (NHTSA) reported that the number of deaths from traffic accidents in the USA increased by 7% from 2014 to 2015, rising to 35,092 fatalities [4]. Amid growing humanitarian concerns of so many injuries and fatalities worldwide, the Department of Transport issued a call to action encouraging the continuous research into different approaches that can improve the situation. As such, there are various research studies which are geared towards how in-vehicle systems can encourage drivers to adapt their driving behaviour and help to reduce the amount of both fatal and non-fatal traffic accidents. Typically, these systems aim to prevent a collision with an upcoming vehicle or pedestrian by providing warnings to drivers, and latest studies demonstrate promising evidence that these systems can indeed have significant positive effects [2, 9, 10]. However, the vast majority of studies have focused on simulation experiments [3, 8] and controlled lab experiments [6, 13]. We have recently contributed to this field by bringing an in-vehicle warning system into a field studying setting, utilising real world location analytics on traffic accident hotspots to generate in-vehicle warnings [7]. Going one step further, the benefit of ubiquitously detecting dangerous locations from data gathered by connected vehicles, and using these locations as a source for in-vehicle warnings, has widely not been addressed in this growing domain and is the focus of our research.
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paper_title: Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

paper_content:

Abstract With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic traffic planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K -means separately, the proposed transportation planning is based on the real-time IoT and GIS data, which is processed by DBN and K -means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K -means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic traffic network planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal planning of traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.
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paper_title: Traffic Flow Prediction With Big Data: A Deep Learning Approach

paper_content:

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.
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paper_title: A Stacked Autoencoder-Based Deep Neural Network for Achieving Gearbox Fault Diagnosis

paper_content:

Machinery fault diagnosis is pretty vital in modern manufacturing industry since an early detection can avoid some dangerous situations. Among various diagnosis methods, data-driven approaches are gaining popularity with the widespread development of data analysis techniques. In this research, an effective deep learning method known as stacked autoencoders (SAEs) is proposed to solve gearbox fault diagnosis. The proposed method can directly extract salient features from frequency-domain signals and eliminate the exhausted use of handcrafted features. Furthermore, to reduce the overfitting problem in training process and improve the performance for small training set, dropout technique and ReLU activation function are introduced into SAEs. Two gearbox datasets are employed to conform the effectiveness of the proposed method; the result indicates that the proposed method can not only achieve significant improvement but also is superior to the raw SAEs and some other traditional methods.
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paper_title: Multiple Object Recognition with Visual Attention

paper_content:

We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.
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paper_title: Going deeper with convolutions

paper_content:

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.
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paper_title: Deep learning

paper_content:

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.
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paper_title: Deep learning for decentralized parking lot occupancy detection

paper_content:

We propose an effective CNN architecture for visual parking occupancy detection.The CNN architecture is small enough to run on smart cameras.The proposed solution performs and generalizes better than other SotA approaches.We provide a new training/validation dataset for parking occupancy detection. A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.
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paper_title: Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

paper_content:

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.
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paper_title: A study on development of the blind spot detection system for the IoT-based smart connected car

paper_content:

The Internet of things (IoT) is an emerging topic in a variety of industry fields such as manufacturing, engineering, automobile, etc. Especially, many automobile companies are competitively introducing a smart-connected car in conjunction with the IoT technology. For instance, some recent vehicles support autonomous driving using cameras and radar-based safety features such as pre-collision prevention system, lane departure alert with a steering assistant function, etc. Based on this example of the IoT-based smart connected car, we actually developed the camera-based blind spot assistance system. The power / serializer board and camera board consisting of a number of sensors, processors, and other hardware components were developed by our own system interface design, and we confirmed that the developed system successfully met quantitative evaluation criteria through testing. Furthermore, we are proposing improvement plans and an idea of object detection in the blind spot based on a deep learning methodology.
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paper_title: PKLot-A robust dataset for parking lot classification

paper_content:

Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.
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paper_title: ImageNet classification with deep convolutional neural networks

paper_content:

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
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paper_title: Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

paper_content:

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.
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paper_title: Identification of Road Surface Conditions using IoT Sensors and Machine Learning

paper_content:

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.
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paper_title: Instance-based learning algorithms

paper_content:

Storing and using specific instances improves the performance of several supervised learning algorithms. These include algorithms that learn decision trees, classification rules, and distributed networks. However, no investigation has analyzed algorithms that use only specific instances to solve incremental learning tasks. In this paper, we describe a framework and methodology, called instance-based learning, that generates classification predictions using only specific instances. Instance-based learning algorithms do not maintain a set of abstractions derived from specific instances. This approach extends the nearest neighbor algorithm, which has large storage requirements. We describe how storage requirements can be significantly reduced with, at most, minor sacrifices in learning rate and classification accuracy. While the storage-reducing algorithm performs well on several real-world databases, its performance degrades rapidly with the level of attribute noise in training instances. Therefore, we extended it with a significance test to distinguish noisy instances. This extended algorithm's performance degrades gracefully with increasing noise levels and compares favorably with a noise-tolerant decision tree algorithm.
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paper_title: A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence

paper_content:

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.
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paper_title: Identification of Road Surface Conditions using IoT Sensors and Machine Learning

paper_content:

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.
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paper_title: Traffic accident detection using random forest classifier

paper_content:

The Internet of Things (IoT) has been growing in recent years with the improvements in several different applications in the military, marine, intelligent transportation, smart health, smart grid, smart home and smart city domains. Although IoT brings significant advantages over traditional information and communication (ICT) technologies for Intelligent Transportation Systems (ITS), these applications are still very rare. Although there is a continuous improvement in road and vehicle safety, as well as improvements in IoT, the road traffic accidents have been increasing over the last decades. Therefore, it is necessary to find an effective way to reduce the frequency and severity of traffic accidents. Hence, this paper presents an intelligent traffic accident detection system in which vehicles exchange their microscopic vehicle variables with each other. The proposed system uses simulated data collected from vehicular ad-hoc networks (VANETs) based on the speeds and coordinates of the vehicles and then, it sends traffic alerts to the drivers. Furthermore, it shows how machine learning methods can be exploited to detect accidents on freeways in ITS. It is shown that if position and velocity values of every vehicle are given, vehicles' behavior could be analyzed and accidents can be detected easily. Supervised machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forests (RF) are implemented on traffic data to develop a model to distinguish accident cases from normal cases. The performance of RF algorithm, in terms of its accuracy, was found superior to ANN and SVM algorithms. RF algorithm has showed better performance with 91.56% accuracy than SVM with 88.71% and ANN with 90.02% accuracy.
---
paper_title: PKLot-A robust dataset for parking lot classification

paper_content:

Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.
---
paper_title: Robust Parking Space Detection Considering Inter-Space Correlation

paper_content:

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class support vector machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov random field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.
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paper_title: Crowdsourcing and Stigmergic Approaches for (Swarm) Intelligent Transportation Systems

paper_content:

In the last decades, the impact of Information and Communication Technologies (ICT) on transportation systems radically changed them, identifying in the Intelligent Transportation Systems (ITS) a new research area. A problem often addressed in ITS is vehicle routing, for which plenty of solutions have been already defined in literature. Vehicle routing problems are usually NP hard, therefore these are mainly heuristic solutions. A requirement for them is to be deployed and run in navigation systems, ready to react to sudden changes in a (quasi) real-time way. Hence, to reduce the latency is still an open issue, not only depending on the complexity of the solution but also on other parameters, such as the traffic update latency in traffic-aware vehicle routing. A way to solve them is by exploiting distributed, collaborative approaches, establishing a proper collaboration platform and algorithms able to use it. Mobile Crowdsensing, on the one hand, and collective and swarm intelligence approaches, on the other, can fill this gap. This paper is a first attempt in this direction, aiming at defining a new class of (swarm) the Intelligent Transportation Systems (SITS), on top of a crowdsourcing-based infrastructure.
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paper_title: Optimization of real-time traffic network assignment based on IoT data using DBN and clustering model in smart city

paper_content:

Abstract With the rapid development of the information age, smart city has gradually become the mainstream of urban construction. Dynamic transportation assignment has attracted more interest in the smart city construction under the new era of the Internet of things (IoT) because the urban road traffic is the heart of many problems in many fields, such as in the case of city congestion and processing center planning system. In this paper, we analyzed the processing center’s economic indexes and optimized the dynamic transportation network assignment based on continuous big IoT input database, and a high performance computing model is proposed for the dynamic traffic planning. Specifically, while the previous methods exploited the geographical information system (GIS) or K -means separately, the proposed transportation planning is based on the real-time IoT and GIS data, which is processed by DBN and K -means to make the final solution close to the practice and meet the requirements of high performance computing and economic cost. which is regarded as the key target index. Moreover, considering the large data characteristic of real-time online stream, the deep belief network (DBN) model is built to preprocess the data to improve the clustering effect of the K -means. This study works on the example case of hotel service centers problem in Tianjin to evaluate the optimal dynamic traffic network planning result. The experiment test has proved that based on the performance of super high computing, the model is precisely helpful for the optimal planning of traffic network under real time mass data situation and low cost, and promoting the construction and development of the smart city.
---
paper_title: Traffic Flow Prediction With Big Data: A Deep Learning Approach

paper_content:

Accurate and timely traffic flow information is important for the successful deployment of intelligent transportation systems. Over the last few years, traffic data have been exploding, and we have truly entered the era of big data for transportation. Existing traffic flow prediction methods mainly use shallow traffic prediction models and are still unsatisfying for many real-world applications. This situation inspires us to rethink the traffic flow prediction problem based on deep architecture models with big traffic data. In this paper, a novel deep-learning-based traffic flow prediction method is proposed, which considers the spatial and temporal correlations inherently. A stacked autoencoder model is used to learn generic traffic flow features, and it is trained in a greedy layerwise fashion. To the best of our knowledge, this is the first time that a deep architecture model is applied using autoencoders as building blocks to represent traffic flow features for prediction. Moreover, experiments demonstrate that the proposed method for traffic flow prediction has superior performance.
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paper_title: A VANET-Based A* Route Planning Algorithm for Travelling Time- and Energy-Efficient GPS Navigation App

paper_content:

Most current navigation devices or apps, based on the global positioning system (GPS), only adopt static information for route planning. Although some are equipped with an RDS-TMC receiver to receive real-time traffic events, many of these real-time traffic events are irrelevant to the vehicle. This paper offers three major contributions. First, a vehicular-ad-hoc-network- (VANET-) based () route planning algorithm is proposed to calculate the route with the shortest travelling time or the lowest fuel consumption, depending on two real-time traffic information sources, which have not been used in traditional GPS navigation applications. The first traffic information source is the recorded traffic information of the road segment that the vehicle has passed through. It is further exchanged between vehicles through an IEEE 802.11p wireless link. The second traffic information is provided by Google Maps. A GPS navigation app is then implemented on the Android platform to realize . Finally, simulations for six route planning algorithms are executed by VANET simulator The ONE, in one congested and one noncongested time period, respectively. In summary, achieves significant reductions in both the average travelling time and fuel consumption of the planned route, as compared to traditional route planning algorithms.
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paper_title: Exploring crowdsourcing information to predict traffic-related impacts

paper_content:

Due to the increased public awareness on global climate change and other environmental problems, advanced strategies and tools are being developed and used to reduce the environmental impact of transport. The main objective of this paper is to explore the potential of using crowdsourcing information as an alternative or complementary source data to predict traffic-related impacts. Three main road connections to two important commercial areas in the city of Aveiro in Portugal, are examined. Driving patterns over different periods were collected using a probe vehicle equipped with a GNSS data logger and traffic volumes were counted during different days. The emissions estimation was based on the concept of Vehicle Specific Power (VSP), which has the capability to predict emissions during a trip through second-by-second vehicle dynamics data. Various tests were conducted in order to explore the potential correlations between these data sets and the information of the peak periods of a certain place that are provided by Google Maps. The findings of the study prove the potential of crowdsourcing information and shows that ICT technologies can be used to estimate emissions and traffic-related impacts.
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paper_title: Crowdsourced Road Navigation: Concept, Design, and Implementation

paper_content:

Map services and applications have been studied extensively within the mobile computing community in the past two decades, starting from standalone GPS receivers and then moving toward connected smart terminals with live digital maps of transportation networks and even real-time traffic. More recently, with the deep penetration of modern 3G/4G networking and social networking, the crowd intelligence from the social community has been explored toward crowdsourced navigation. In this article, we first provide an overview of the past and present road navigation technologies. We then discuss very recent advances in crowd intelligence, identifying the unique challenges and opportunities therein. We further present a case study that utilizes the crowdsourced driving information to combat the last mile puzzle for road navigation.
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paper_title: Traffic Flow Forecasting for Urban Work Zones

paper_content:

None of numerous existing traffic flow forecasting models focus on work zones. Work zone events create conditions that are different from both normal operating conditions and incident conditions. In this paper, four models were developed for forecasting traffic flow for planned work zone events. The four models are random forest, regression tree, multilayer feedforward neural network, and nonparametric regression. Both long-term and short-term traffic flow forecasting applications were investigated. Long-term forecast involves forecasting 24 h in advance using historical traffic data, and short-term forecasts involves forecasting 1 h and 45, 30, and 15 min in advance using real-time temporal and spatial traffic data. Models were evaluated using data from work zone events on two types of roadways, a freeway, i.e., I-270, and a signalized arterial, i.e., MO-141, in St. Louis, MO, USA. The results showed that the random forest model yielded the most accurate long-term and short-term work zone traffic flow forecasts. For freeway data, the most influential variables were the latest interval's look-back traffic flows at the upstream, downstream, and current locations. For arterial data, the most influential variables were the traffic flows from the three look-back intervals at the current location only.
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paper_title: IoT-based multifunctional Scalable real-time Enhanced Road Side Unit for Intelligent Transportation Systems

paper_content:

This paper presents the design of a modular, Scalable Enhanced Road Side Unit for use as part of a comprehensive Intelligent Transportation System based on the concept of Internet of Things (IoT). The proposed unit is designed to collect information using a variety of sensors and an on-board camera. The collected information can then be uploaded to a central server for actions such as speed limit adjustment, metering routes to reduce vehicle congestion and emissions, and issuing weather advisory warnings. Communication between an individual unit and the central server are performed using existing wireless cellular networks. Each module also contains an RF module for communication with both other nearby units and other parties equipped with an appropriate receiver module. Lab testing with an initial prototype confirmed the feasibility of the proposed communication links and evaluation of the current real-time software implementation showed an average total CPU resource usage of 34%, giving room to expand functionality with additional tasks.
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paper_title: Short-term traffic predictions on large urban traffic networks: Applications of network-based machine learning models and dynamic traffic assignment models

paper_content:

The paper discusses the issues to face in applications of short-term traffic predictions on urban road networks and the opportunities provided by explicit and implicit models. Different specifications of Bayesian Networks and Artificial Neural Networks are applied for prediction of road link speed and are tested on a large floating car data set. Moreover, two traffic assignment models of different complexity are applied on a sub-area of the road network of Rome and validated on the same floating car data set.
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paper_title: Study of Group Route Optimization for IoT Enabled Urban Transportation Network

paper_content:

Traffic congestion is always a major issue in urban planning, especially when the vehicles in the roadway keep growing and the local authorities are lack of solutions to manage or distribute the traffics in the city. Although there are several factors that may cause traffic congestion, inefficiency in traffic management is always the main issue. Additionally, the most traditional methods of resolving traffic congestion or rerouting algorithm are mainly designed for individuals' benefits, by simply planning a driver's route based on minimum travel time or shortest path accordingly. There is lack of consideration in group benefit or urban development. However, with the development of technologies in Internet of Things (IoT), vehicle to vehicle (V2V) or Vehicle to Infrastructure (V2I) communications, group based routing becomes achievable. Instead of optimizing the routing path for individual drivers, this paper studies how to develop a new method to provide new routing method based on vehicles' similarities in a specific urban's transportation environment.
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paper_title: Variation Based Online Travel Time Prediction Using Clustered Neural Networks

paper_content:

This paper proposes a variation-based online travel time prediction approach using clustered Neural Networks with traffic vectors extracted from raw detector data as the input variables. Different from previous studies, the proposed approach decomposes the corridor travel time into two parts: 1) the base term, which is predicted by a fuzzy membership-value-weighted average of the clustered historical data to reflect the primary traffic pattern in the corridor; and 2) the variation term, which is predicted through the calibrated cluster-based artificial neural network model to capture the actual traffic fluctuation. To evaluate the effectiveness of the proposed approach, this paper has conducted intensive numerical experiments with simulated data from the microscopic simulator CORSIM. Experimental results under various traffic volume levels have revealed the potentials for the proposed method to be applied in online corridor travel time prediction.
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paper_title: A smart parking system based on NB-IoT and third-party payment platform

paper_content:

In recent years, with the increasing number of vehicles and insufficient parking spaces, the urban traffic congestion has become a great challenge that cannot be neglected. In order to mitigate problems such as high power consumption of sensor node and high deployment costs of wireless network, a smart parking system is proposed in this paper. In the proposed system, the data of the sensor node is transmitted by Narrowband Internet of Things (NB-IoT) module, which is a new cellular technology introduced for Low-Power Wide-Area (LPWA) applications. Basic information management, charge management, sensor node surveillance, task management and business intelligence modules are implemented on the cloud server. With integrated third-party payment platform and parking guide service, the mobile application developed for drivers is easy and convenient to use. Currently, the proposed system has been deployed in two cities to improve the utilization of existing parking facilities effectively.
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paper_title: Reliability analysis of an IoT-based smart parking application for smart cities

paper_content:

The proliferation of Internet of Things (IoT) devices demands robust structures for processing and storing trillion gigabytes of data. Part of those data can be used for evaluating the solution's effectiveness and to improve it. Hence, a reliability analysis based on the solution's produced data is important to validate it and find weaknesses to solve. Therefore, in this paper we process the data generated by a sensor-based smart parking system to present a reliability analysis. This system is implemented placing ultrasonic sensors, a web service, the FIWARE platform, and a mobile application to Android devices, in a three-layer architecture. We evaluate how many errors occur in a certain time window, the success and error rates and the most common kind of error produced. Finally, we conclude that even with some instability our sensing approach is effective with a worst case success rate greater than 96%. Moreover, we find out that our system works in an open environment with tropical climate conditions.
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paper_title: A navigation and reservation based smart parking platform using genetic optimization for smart cities

paper_content:

With the development of technology, smart devices are becoming more common in everyday life. The development of devices that can connect to the Internet and transmit data has been a source of inspiration for smart city designs. The common problem in our cities is the difficulty of finding free parking slots. The parking problem causes traffic to congest and people who go to work are looking for a place. In this study, a navigation and reservation based parking proposal system was developed for smart cities. The proposed method involves the development of small devices that send data to the internet using the internet of things (IoT) technology. The free parking space closest to the current location is found by genetic algorithm. The proposed method is tested for different scenarios and accurate results are obtained.
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paper_title: Deep learning for decentralized parking lot occupancy detection

paper_content:

We propose an effective CNN architecture for visual parking occupancy detection.The CNN architecture is small enough to run on smart cameras.The proposed solution performs and generalizes better than other SotA approaches.We provide a new training/validation dataset for parking occupancy detection. A smart camera is a vision system capable of extracting application-specific information from the captured images. The paper proposes a decentralized and efficient solution for visual parking lot occupancy detection based on a deep Convolutional Neural Network (CNN) specifically designed for smart cameras. This solution is compared with state-of-the-art approaches using two visual datasets: PKLot, already existing in literature, and CNRPark-EXT. The former is an existing dataset, that allowed us to exhaustively compare with previous works. The latter dataset has been created in the context of this research, accumulating data across various seasons of the year, to test our approach in particularly challenging situations, exhibiting occlusions, and diverse and difficult viewpoints. This dataset is public available to the scientific community and is another contribution of our research. Our experiments show that our solution outperforms and generalizes the best performing approaches on both datasets. The performance of our proposed CNN architecture on the parking lot occupancy detection task, is comparable to the well-known AlexNet, which is three orders of magnitude larger.
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paper_title: PKLot-A robust dataset for parking lot classification

paper_content:

Outdoor parking lot vacancy detection systems have attracted a great deal of attention in the last decade due the large number of practical applications. However, a common problem that researchers in this field very often face is the lack of a representative dataset to perform their experiments. To mitigate this difficulty, in this paper we introduce a new parking lot dataset composed of 695,899 images captured from two parking lots with three different camera views. The acquisition protocol allows obtaining static images showing illumination variance related to sunny, overcast and rainy days. We believe that researchers will find this dataset a very useful tool since it allows future benchmarking and evaluation. The dataset is currently available for research purposes upon request. To gain a better insight into this dataset we have evaluated two textural descriptors, Local Binary Patterns and Local Phase Quantization, with a Support Vector Machine classifier to detect parking lot vacancy. In the experiments where the same view was used for both training and testing, we have reached outstanding recognition rates, greater than 99%. The main challenge, though, lies in building a general classifier that is able to detect parking spaces from the parking lots that were not used for training. In this sense, the best result achieved by the texture-based classifier was about 89%. The observed drop in terms of performance shows that additional investigation is necessary to create classification schemes less dependent on the training set. Other researchers can use these results as a baseline performance when testing their own algorithms on this dataset.
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paper_title: Robust Parking Space Detection Considering Inter-Space Correlation

paper_content:

A major problem in metropolitan areas is searching for parking spaces. In this paper, we propose a novel method for parking space detection. Given input video captured by a camera, we can distinguish the empty spaces from the occupied spaces by using an 8-class support vector machine (SVM) classifier with probabilistic outputs. Considering the inter-space correlation, the outputs of the SVM classifier are fused together using a Markov random field (MRF) framework. The result is much improved detection performance, even when there are significant occlusion and shadowing effects in the scene. Experimental results are given to show the robustness of the proposed approach.
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paper_title: ASPIRE: An Agent-Oriented Smart Parking Recommendation System for Smart Cities

paper_content:

An Agent-oriented Smart Parking Recommendation System for Smart Cities (ASPIRE) using Internet of Things (IoT) is proposed in which significant consideration is given to the driver's parking preferences such as type of parking sought, the maximum price that the parker is willing to pay, and the maximum time to walk from parking space to the destination that the parker can tolerate. ASPIRE is a lightweight, scalable, and adaptable system that automates the workflow of reserving the best parking spots. It employs a novel parking brokerma software agent that leverages valuable route planning information from other traffic information services, and promotes road-safety by minimizing the need for parkers to interact with their smartphones or onboard units, thus lowering distracted driving. The main contribution of this paper is the novel adoption of lightweight Analytic Hierarchy Process (AHP) in order to determine the best parking space. In addition, the parking record collected by ASPIRE can provide local governments with statistics for building future parking lots. ASPIRE's parking network and architecture, system workflow, strategy model, and simulation highlight the importance of the desirable feature such as satellite navigation, indoor navigation, event data recording, parking reference, traffic and weather information, and parking reservations.
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paper_title: Smart Car Parking Management System Using IoT

paper_content:

Proliferation in the number of vehicles is leading to problems of vehicles parking at an appropriate place especially the car parking. This indirectly leads to traffic congestion. This is because of the fact that current transportation infrastructure and car park facility are unable to cope with the arrival of large number of vehicles on the road. To alleviate the aforementioned problem, authors proposed a Smart Parking Management System that helps users to automatically find a free parking space with a smaller amount. Smart Parking involves use of Ultrasonic sensor, Arduino Uno, ESP8266-01 Wi-Fi Module, Cloud server. IOT based new parking platform enable to connect, analyze and automate data gathered from devices and execute smart parking possible. Smart parking would enable vehicle occupancy, monitoring and managing of available parking space in real-time that reducing the environmental pollution. Proposed system provides optimize usage of parking space and get considerable revenue generation.
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paper_title: Smart lighting: Intelligent and weather adaptive lighting in street lights using IOT

paper_content:

A smart road lighting framework conforms to light yield in the view of use and inhabitance, i.e., computerizing grouping of pedestrians, cyclist, cars and public transports. The smart road light administration proposes the establishment of the remote based framework to remotely track and control the genuine vitality utilization of the road lights and take suitable vitality utilization decrease measures through power molding and control. The road light controller ought to be introduced on the shaft lights which comprise of microcontroller alongside different sensor and remote module. The road light controller introduced into the city light shaft will control LED road lighting relying upon the movement stream, convey information between every road light.
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paper_title: SSL: Smart Street Lamp Based on Fog Computing for Smarter Cities

paper_content:

Both safety and energy conservation are very important advantages of smart cities. Namely, the city street lamp is correlated with both safety and energy conservation. Therefore, a street lamp is an indispensable part of the smart cities. However, current street lamps have lack of smart characteristics, which increases both danger and energy consumption. In order to address these problems, a smart street lamp (SSL) based on the fog computing for smarter cities is proposed in this paper. The advantages of the proposed SSL are as follows: 1) fine management, because every street lamp can be operated independently; 2) dynamic brightness adjustment, all street lamps can be adjusted dynamically; and 3) autonomous alarm on abnormal states, each street lamp can report the abnormal status independently, such as broken, stolen, and so on. The experimental results showed that the proposed SSL can improve the energy efficiency and reduce danger.
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paper_title: Autonomously detecting and classifying traffic accident hotspots

paper_content:

The number of road traffic fatalities has been steadily increasing since 2001 and is currently the eighth leading cause of death globally, with the loss of life of 1.2 million people each year according to the World Health Organization (WHO) [11]. In addition, the National Highway Traffic Safety Administration (NHTSA) reported that the number of deaths from traffic accidents in the USA increased by 7% from 2014 to 2015, rising to 35,092 fatalities [4]. Amid growing humanitarian concerns of so many injuries and fatalities worldwide, the Department of Transport issued a call to action encouraging the continuous research into different approaches that can improve the situation. As such, there are various research studies which are geared towards how in-vehicle systems can encourage drivers to adapt their driving behaviour and help to reduce the amount of both fatal and non-fatal traffic accidents. Typically, these systems aim to prevent a collision with an upcoming vehicle or pedestrian by providing warnings to drivers, and latest studies demonstrate promising evidence that these systems can indeed have significant positive effects [2, 9, 10]. However, the vast majority of studies have focused on simulation experiments [3, 8] and controlled lab experiments [6, 13]. We have recently contributed to this field by bringing an in-vehicle warning system into a field studying setting, utilising real world location analytics on traffic accident hotspots to generate in-vehicle warnings [7]. Going one step further, the benefit of ubiquitously detecting dangerous locations from data gathered by connected vehicles, and using these locations as a source for in-vehicle warnings, has widely not been addressed in this growing domain and is the focus of our research.
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paper_title: Vehicle motion intention reasoning using cooperative perception on urban road

paper_content:

As autonomous vehicles are venturing on the urban road, reasoning other vehicles' motion intention is always essential for anticipatory decision-making. This paper considers a new class of motion intention reasoning problem using cooperative perception. Specifically, thanks to the extended perception range contributed by cooperative perception, the authors can explicitly consider the behavior correlations among multiple vehicles, where each vehicle's motion intention can be inferred using both the temporal observations and the other vehicles' motion intention expectation. As such, a Simplified variant of Coupled Hidden Markov Model (S-CHMM) is proposed for motion intention inference. The experiment on an autonomous vehicle shows that the proposed algorithm can improve the motion intention accuracy and provide efficient overtaking triggering for autonomous driving on urban road.
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paper_title: An IoT Cloud System for Traffic Monitoring and Vehicular Accidents Prevention Based on Mobile Sensor Data Processing

paper_content:

The sudden traffic slowdown especially in fast scrolling roads and highways characterized by a scarce visibility is one of the major causes of accidents among motorized vehicles. It can be caused by other accidents, work-in-progress on roads, excessive motorized vehicles especially at peak times and so on. Typically, fixed traffic sensors installed on roads that interact with drivers’ mobile App through the 4G network can mitigate such a problem, but unfortunately not all roads and highways are equipped with such devices. In this paper, we discuss a possible alternative solution for addressing such an issue considering mobile traffic sensors directly installed in private and/or public transportation and volunteer vehicles. In this scenario a fast real-time processing of big traffic data is fundamental to prevent accidents. In particular, we discuss an IoT Cloud system for traffic monitoring and alert notification based on OpenGTS and MongoDB. Our IoT Cloud system, besides for private drivers, it is very useful for drivers of critical rescue vehicles such as ambulances. Experiments prove that our system provides acceptable response times that allows drivers to receive alert messages in useful time so as to avoid the risk of possible accidents.
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paper_title: Multiple Object Recognition with Visual Attention

paper_content:

We present an attention-based model for recognizing multiple objects in images. The proposed model is a deep recurrent neural network trained with reinforcement learning to attend to the most relevant regions of the input image. We show that the model learns to both localize and recognize multiple objects despite being given only class labels during training. We evaluate the model on the challenging task of transcribing house number sequences from Google Street View images and show that it is both more accurate than the state-of-the-art convolutional networks and uses fewer parameters and less computation.
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paper_title: Traffic accident detection using random forest classifier

paper_content:

The Internet of Things (IoT) has been growing in recent years with the improvements in several different applications in the military, marine, intelligent transportation, smart health, smart grid, smart home and smart city domains. Although IoT brings significant advantages over traditional information and communication (ICT) technologies for Intelligent Transportation Systems (ITS), these applications are still very rare. Although there is a continuous improvement in road and vehicle safety, as well as improvements in IoT, the road traffic accidents have been increasing over the last decades. Therefore, it is necessary to find an effective way to reduce the frequency and severity of traffic accidents. Hence, this paper presents an intelligent traffic accident detection system in which vehicles exchange their microscopic vehicle variables with each other. The proposed system uses simulated data collected from vehicular ad-hoc networks (VANETs) based on the speeds and coordinates of the vehicles and then, it sends traffic alerts to the drivers. Furthermore, it shows how machine learning methods can be exploited to detect accidents on freeways in ITS. It is shown that if position and velocity values of every vehicle are given, vehicles' behavior could be analyzed and accidents can be detected easily. Supervised machine learning algorithms such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), and Random Forests (RF) are implemented on traffic data to develop a model to distinguish accident cases from normal cases. The performance of RF algorithm, in terms of its accuracy, was found superior to ANN and SVM algorithms. RF algorithm has showed better performance with 91.56% accuracy than SVM with 88.71% and ANN with 90.02% accuracy.
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paper_title: A study on development of the blind spot detection system for the IoT-based smart connected car

paper_content:

The Internet of things (IoT) is an emerging topic in a variety of industry fields such as manufacturing, engineering, automobile, etc. Especially, many automobile companies are competitively introducing a smart-connected car in conjunction with the IoT technology. For instance, some recent vehicles support autonomous driving using cameras and radar-based safety features such as pre-collision prevention system, lane departure alert with a steering assistant function, etc. Based on this example of the IoT-based smart connected car, we actually developed the camera-based blind spot assistance system. The power / serializer board and camera board consisting of a number of sensors, processors, and other hardware components were developed by our own system interface design, and we confirmed that the developed system successfully met quantitative evaluation criteria through testing. Furthermore, we are proposing improvement plans and an idea of object detection in the blind spot based on a deep learning methodology.
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paper_title: A Real-Time Autonomous Highway Accident Detection Model Based on Big Data Processing and Computational Intelligence

paper_content:

Due to increasing urban population and growing number of motor vehicles, traffic congestion is becoming a major problem of the 21st century. One of the main reasons behind traffic congestion is accidents which can not only result in casualties and losses for the participants, but also in wasted and lost time for the others that are stuck behind the wheels. Early detection of an accident can save lives, provides quicker road openings, hence decreases wasted time and resources, and increases efficiency. In this study, we propose a preliminary real-time autonomous accident-detection system based on computational intelligence techniques. Istanbul City traffic-flow data for the year 2015 from various sensor locations are populated using big data processing methodologies. The extracted features are then fed into a nearest neighbor model, a regression tree, and a feed-forward neural network model. For the output, the possibility of an occurrence of an accident is predicted. The results indicate that even though the number of false alarms dominates the real accident cases, the system can still provide useful information that can be used for status verification and early reaction to possible accidents.
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paper_title: Identification of Road Surface Conditions using IoT Sensors and Machine Learning

paper_content:

The objective of this research is to collect and analyse road surface conditions in Malaysia using Internet-of-Things (IoT) sensors, together with the development of a machine learning model that can identify these conditions. This allows for the facilitation of low cost data acquisition and informed decision making in helping local authorities with repair and resource allocation. The conditions considered in this study include smooth surfaces, uneven surfaces, potholes, speed bumps, and rumble strips. Statistical features such as minimum, maximum, standard deviation, median, average, skewness, and kurtosis are considered, both time and frequency domain forms. Selection of features is performed using Ranker, Greedy Algorithm and Particle Swarm Optimisation (PSO), followed by classification using k-Nearest Neighbour (k-NN), Random Forest (RF), and Support Vector Machine (SVM) with linear and polynomial kernels. The model is able to achieve an accuracy of 99%, underlining the effectiveness of the model to identify these conditions.
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paper_title: An intelligent smartphone based approach using IoT for ensuring safe driving

paper_content:

Road accident is a widespread problem all over the world. The number of the vehicle is increasing rapidly. Therefore, the probability of an accident is also increasing. So in this paper, we propose an IoT-based system for providing safe driving. It will collect data using smartphone and show the driver about the condition of the road. We have developed an Android based application which will collect data from a vehicle and send it to its nearest IoT-Fog server for processing the data quickly. We also developed two algorithms using k-means clustering approach which find the location of road anomalies and accident prone area. The result will be shown in Google map of driver's smartphone. This information will be stored in cloud for further use. We have tested our proposed system through statistical as well as experimental evaluations. Simulation result shows that our approach can provide better performance compared to state-of-the-art methods.
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paper_title: Machine learning approach for predicting bumps on road

paper_content:

In today's days, due to increase in number of vehicles the probability of accidents are also increasing. The user should be aware of the road circumstances for safety purpose. Several methods requires installing dedicated hardware in vehicle which are expensive. so we have designed a Smart-phone based method which uses a Accelerometer and GPS sensors to analyze the road conditions. The designed system is called as Bumps Detection System(BDS) which uses Accelerometer for pothole detection and GPS for plotting the location of potholes on Google Map. Drivers will be informed in advance about count of potholes on road. we have assumed some threshold values on z-axis(Experimentally Derived)while designing the system. To justify these threshold values we have used a machine learning approach. The k means clustering algorithm is applied on the training data to build a model. Random forest classifier is used to evaluate this model on the test data for better prediction.
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paper_title: Deep Learning in Data-Driven Pavement Image Analysis and Automated Distress Detection: A Review

paper_content:

Deep learning, more specifically deep convolutional neural networks, is fast becoming a popular choice for computer vision-based automated pavement distress detection. While pavement image analysis has been extensively researched over the past three decades or so, recent ground-breaking achievements of deep learning algorithms in the areas of machine translation, speech recognition, and computer vision has sparked interest in the application of deep learning to automated detection of distresses in pavement images. This paper provides a narrative review of recently published studies in this field, highlighting the current achievements and challenges. A comparison of the deep learning software frameworks, network architecture, hyper-parameters employed by each study, and crack detection performance is provided, which is expected to provide a good foundation for driving further research on this important topic in the context of smart pavement or asset management systems. The review concludes with potential avenues for future research; especially in the application of deep learning to not only detect, but also characterize the type, extent, and severity of distresses from 2D and 3D pavement images.
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paper_title: IoT enabled intelligent bus transportation system

paper_content:

With the increasing size of urban population, cities are facing a diverse range of issues such as severe traffic congestion, deteriorating air quality, increasing road accidents, and exploding growth in number of private vehicles. At the same time, the share of public transport is decreasing. The reason is lack of dependable public transport facilities. With the development of Information technology, Internet of Things (IoT) is becoming a reality. IoT can assist in the integration of communication, control and information processing present in the public transportation system. This paper, deals with implementation of IoT in public transport system by providing information such as bus number, arrival time and number of persons on board to the end user. The cost effective prototype model developed collects data at the vehicle terminal, uploads the updated data to the server through the internet and makes data visible to the awaiting user. Key technologies used in the proposed work are RFID, GPS and controller with in-built Wi-Fi module. A prototype model of the proposed system has been developed and demonstrated.
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paper_title: A Vehicle-to-Vehicle Communication System Using Iot Approach

paper_content:

The world has witnessed ideas which practically changed the way, one lived on the basis of everyday technology. The interconnection between the things and making and making the system smart is one of them. Vehicles connected to the internet could be a level up in this game. This paper showcases a new idea of a vehicle to vehicle (v2v) connection using the internet, adding to the existing IoT connected vehicles solution. This system could ensure data transfers between to vehicles as they come in range hereby sharing real time road information as well as ensuring a safer and better driving condition for everyone. The communication through IoT has been developed using LabVIEW simulation.
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paper_title: A Cross Layer Protocol for Traffic Management in Social Internet of Vehicles

paper_content:

Abstract One of the important members of Internet of Things (IoT); transportation vehicles are utilizing sudden advancements of communication technology. Now days, to make the Social IoT (SIoT) a reality there are many on-going world-wide research initiatives and standardization efforts. Internet of Vehicles (IoV) is an extension to the concept of SIoT that proposes Social Internet of Vehicles (SIoV). SIoV is an example of SIoT, where Smart vehicles are the objects of SIoT that builds social relationship and interchanges information to enhance the driving knowledge and gives various services to the drivers. In this paper, the proposed Vehicular Social Networks based on the VIoT consist of a large number of sensors that transmit data wirelessly. However, the very high heterogeneity in hardware capabilities of things and QoS requirements for different applications limits the performance of classical layered protocol solutions and the existing cross-layer solutions for wireless sensor networks. The results show that the proposed VSNP (Vehicular Social Network Protocol) for VIoT based on WSN solution can achieve a global communication optimum and outperforms existing layered solutions. The novel cross-layer module is a primary step towards providing efficient traffic management and reliable vehicle to vehicle communication in the SIoT. We have proposed an algorithm to control traffic congestion and road safety for VIoT.
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Title: A Review of Machine Learning and IoT in Smart Transportation

Section 1: Introduction
Description 1: This section introduces the rise of IoT and its integration with Machine Learning techniques to enhance smart transportation systems.

Section 2: Machine Learning
Description 2: This section provides an overview of Machine Learning, including types of learning (supervised, unsupervised, semi-supervised, reinforcement) and their common applications.

Section 3: Machine Learning in IoT
Description 3: This section discusses the role and benefits of machine learning algorithms in IoT infrastructures, particularly for autonomous decision-making and big data analysis.

Section 4: Smart Transportation
Description 4: This section delves into the applications of IoT and ML in smart transportation, including ITS, navigation, traffic congestion management, smart parking, accident detection, and V2V communication.

Section 5: Machine Learning Algorithms
Description 5: This section categorizes and details various ML algorithms used in smart transportation applications (e.g., Ensemble methods, Bayesian, Markov Models, Decision Trees, Clustering, Artificial Neural Networks, Deep Learning, Instance-Based, Regression Analysis, Non-Probabilistic Linear Classification).

Section 6: ML and IoT Applications in Smart Transportation
Description 6: This section reviews and summarizes the use of ML and IoT in smart transportation challenges, categorized into route optimization, parking, smart lights, accident detection/prevention, road anomalies detection, and infrastructure.

Section 7: Discussion/Conclusions
Description 7: This section discusses the findings of the review, emphasizing the impact and potential future developments of ML and IoT in various smart transportation applications, highlighting underexplored areas for future research.