Detecting travel modes from smartphone-based travel surveys with continuous hidden Markov models
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paper_title: Household Travel Surveys in Context-Based Approach for Adjusting ITE Trip Generation Rates in Urban Contexts

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With household travel surveys (HTSs) to produce a regional-scale policy model, this research develops a methodology for predicting context-based vehicle-trip reductions applied to ITE's Trip Generation Handbook at a site-level development. This methodology may be used as a supplement to ITE trip generation rates, providing justification to vehicle-trip reductions based on known contextual vehicle mode splits. With the 2006 HTS of the Puget Sound Regional Council, Washington State, non-home-based trip ends were selected, and common built environment measures were extracted. A clustering analysis was then applied to all trip ends to determine clustered groups or contexts. With contexts, sociodemographic characteristics, and trip characteristics as model variables, a binary logistic model was developed to predict the mode split. Mode splits were then calculated for all context types (Type A through Type H) with the use of average built environment, sociodemographic characteristics, and trip characteristics v...
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paper_title: The impact of transportation demand management policies on commuting to college facilities: A case study at the University of Trieste, Italy

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Universities, like other types of public and private institutions, when located in a city, have both positive and negative impacts on the area where they are situated. On the one hand, they contribute to the prestige of the area; on the other hand, they are large generators/attractors of traffic. The ability to successfully balance the pros and cons of the urban location of these large traffic-generating institutions is crucial for their success and for the livability of the city. In this paper this issue has been analyzed selecting as a representative case the University of Trieste.
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paper_title: Processing Raw Data from Global Positioning Systems Without Additional Information

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Since the first Global Positioning System (GPS) studies in the mid-1990s, this method of surveying individual travel behavior has gained attention in transport research. Compared with classic travel survey methods, GPS studies offer researchers benefits of more accurate and reliable information. At the same time, the participants' burden is reduced substantially if the GPS data collection does not involve time-consuming questions. However, without additional information, such as modes and trip purposes, extensive postprocessing is required to derive data that can be used for analysis and model estimation. The corresponding procedures are an ongoing research issue. This paper describes a postprocessing procedure needing no input other than the most basic GPS raw data: three-dimensional positions and the corresponding time stamps. First, the data are thoroughly cleaned and smoothed. Second, trips and activities are determined. Third, the trips are segmented into single-mode stages, and the transport mode fo...
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paper_title: Detecting Travel Modes Using Particular Rules Combined with a Naïve Bayesian Classifier

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Travel modes are often detected from Global Positioning System (GPS) positioning data with two types of methods, i.e., particular rules and advanced classification methods. The former is intuitive and easily applied, while the latter incorporates relatively preferable generalization and flexibility. However, studies touching the combination of these two methods are limited. In this study, the authors employ particular rules to detect subway trips and then utilize a naive Bayesian classifier with sequential forward selection procedures to detect the remaining modes. Depending on four representative attributes included in the optimal attribute set, more than 98% of subway trips are correctly identified and a total accuracy of 94.85% is achieved. Notably, more than 91% of bus trips and car trips are accurately identified in either training set or test set. The results provide one with a new perspective to select classification methods in detecting travel modes and even other travel characteristics in GPS travel surveys. In addition, a high distinction between car trips and bus trips without geographic information system (GIS) sources of bus networks indicates that GIS sources can be replaced to a certain extent by extracting reasonable attributes.
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paper_title: Extracting Places and Activities from GPS Traces Using Hierarchical Conditional Random Fields

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Learning patterns of human behavior from sensor data is extremely important for high-level activity inference. This paper describes how to extract a person's activities and significant places from traces of GPS data. The system uses hierarchically structured conditional random fields to generate a consistent model of a person's activities and places. In contrast to existing techniques, this approach takes the high-level context into account in order to detect the significant places of a person. Experiments show significant improvements over existing techniques. Furthermore, they indicate that the proposed system is able to robustly estimate a person's activities using a model that is trained from data collected by other persons.
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paper_title: Transportation mode detection using mobile phones and GIS information

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The transportation mode such as walking, cycling or on a train denotes an important characteristic of the mobile user's context. In this paper, we propose an approach to inferring a user's mode of transportation based on the GPS sensor on her mobile device and knowledge of the underlying transportation network. The transportation network information considered includes real time bus locations, spatial rail and spatial bus stop information. We identify and derive the relevant features related to transportation network information to improve classification effectiveness. This approach can achieve over 93.5% accuracy for inferring various transportation modes including: car, bus, aboveground train, walking, bike, and stationary. Our approach improves the accuracy of detection by 17% in comparison with the GPS only approach, and 9% in comparison with GPS with GIS models. The proposed approach is the first to distinguish between motorized transportation modes such as bus, car and aboveground train with such high accuracy. Additionally, if a user is travelling by bus, we provide further information about which particular bus the user is riding. Five different inference models including Bayesian Net, Decision Tree, Random Forest, Naive Bayesian and Multilayer Perceptron, are tested in the experiments. The final classification system is deployed and available to the public.
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paper_title: Assessing the accuracy of the Sydney Household Travel Survey with GPS

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Over the past few years, GPS has been used in a number of surveys in the US to assess the accuracy of household travel surveys. The results have been somewhat alarming in that most of these exercises have shown that the standard trip-based CATI survey conducted in the US under-reports travel by about 20–25%. It was decided to use GPS to assess the accuracy of the Sydney Household Travel Survey, a continuous survey conducted by face-to-face interviewing. The procedure used was for the interviewers to recruit households for the household travel survey in the normal manner, and then, if the household met certain criteria, to endeavour to recruit the household to also undertake a GPS survey. A small sample of about 50 households was obtained, and GPS devices successfully retrieved that measured data on the same day as the travel diary was completed. In addition, participants in the GPS survey completed a prompted recall survey a week or two later, using maps and tabulations of travel obtained from the GPS devices, to identify mode, purpose and occupancy for trips measured by the GPS, and also to check for accuracy in defining trip ends and total number of trips. Based on the analysis of the GPS compared to the diary results, it was found that respondents under-reported their travel by about 7%, which is much less than in the US CATI results. Respondents were also found to under-report travel distances and over-report travel times. There was also a high incidence of non-reporting for VKT.
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paper_title: An open-source tool to identify active travel from hip-worn accelerometer, GPS and GIS data

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Increases in physical activity through active travel have the potential to have large beneficial effects on populations, through both better health outcomes and reduced motorized traffic. However accurately identifying travel mode in large datasets is problematic. Here we provide an open source tool to quantify time spent stationary and in four travel modes(walking, cycling, train, motorised vehicle) from accelerometer measured physical activity data, combined with GPS and GIS data. The Examining Neighbourhood Activities in Built Living Environments in London study evaluates the effect of the built environment on health behaviours, including physical activity. Participants wore accelerometers and GPS receivers on the hip for 7 days. We time-matched accelerometer and GPS, and then extracted data from the commutes of 326 adult participants, using stated commute times and modes, which were manually checked to confirm stated travel mode. This yielded examples of five travel modes: walking, cycling, motorised vehicle, train and stationary. We used this example data to train a gradient boosted tree, a form of supervised machine learning algorithm, on each data point (131,537 points), rather than on journeys. Accuracy during training was assessed using five-fold cross-validation. We also manually identified the travel behaviour of both 21 participants from ENABLE London (402,749 points), and 10 participants from a separate study (STAMP-2, 210,936 points), who were not included in the training data. We compared our predictions against this manual identification to further test accuracy and test generalisability. Applying the algorithm, we correctly identified travel mode 97.3% of the time in cross-validation (mean sensitivity 96.3%, mean active travel sensitivity 94.6%). We showed 96.0% agreement between manual identification and prediction of 21 individuals’ travel modes (mean sensitivity 92.3%, mean active travel sensitivity 84.9%) and 96.5% agreement between the STAMP-2 study and predictions (mean sensitivity 85.5%, mean active travel sensitivity 78.9%). We present a generalizable tool that identifies time spent stationary and time spent walking with very high precision, time spent in trains or vehicles with good precision, and time spent cycling with moderate precisionIn studies where both accelerometer and GPS data are available this tool complements analyses of physical activity, showing whether differences in PA may be explained by differences in travel mode. All code necessary to replicate, fit and predict to other datasets is provided to facilitate use by other researchers.
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paper_title: Identifying activities and trips with GPS data

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This study designs a process for identifying trips and activities based on global positioning system (GPS) survey data. The proposed identification process is composed of four steps, namely determining status segments, detecting activities, identifying trips, and recognising short-time activities. The results indicate that the proposed algorithm shows a high level of identification accuracy compared with the travel diaries reported in the paper-form travel survey. By providing the identification method of the short-time activities, this study resolves the problem of overlooking short-time activities in conventional travel surveys and increases the accuracy of trip detection. This work also facilitates the study of the spatial and temporal distributions of short-time activities related to travel behaviours such as temporary parking. By proposing a method for identifying trips and activities from GPS data, the findings provide a research scheme for detecting other travel information based on GPS data such as travel mode and trip purpose, reasonable decisions for urban transportation planning and management.
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paper_title: Real-time trip purpose prediction using online location-based search and discovery services

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Abstract The use of smartphone technology is increasingly considered a state-of-the-art practice in travel data collection. Researchers have investigated various methods to automatically predict trip characteristics based upon locational and other smartphone sensing data. Of the trip characteristics being studied, trip purpose prediction has received relatively less attention. This research develops trip purpose prediction models based upon online location-based search and discovery services (specifically, Google Places API) and a limited set of trip data that are usually available upon the completion of the trip. The models have the potential to be integrated with smartphone technology to produce real-time trip purpose prediction. We use a recent, large-scale travel behavior survey that is augmented by downloaded Google Places information on each trip destination to develop and validate the models. Two statistical and machine learning prediction approaches are used, including nested logit and random forest methods. Both sets of models show that Google Places information is a useful predictor of trip purpose in situations where activity- and person-related information is uncollectable, missing, or unreliable. Even when activity- and person-related information is available, incorporating Google Places information provides incremental improvements in trip purpose prediction.
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paper_title: Quantifying the Difference Between Self-Reported and Global Positioning Systems-Measured Journey Durations: A Systematic Review

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Accurate measurement of travel behaviour is vital for transport planning, modelling, public health epidemiology, and assessing the impact of travel interventions. Self-reported diaries and questionnaires are traditionally used as measurement tools; advances in Global Positioning Systems (GPS) technology allow for comparison. This review aimed to identify and report about studies comparing self-reported and GPS-measured journey durations. We systematically searched, appraised, and analysed published and unpublished articles from electronic databases, reference lists, bibliographies, and websites up to December 2012. Included studies used GPS and self-report to investigate trip duration. The average trip duration from each measure was compared and an aggregated, pooled estimate of the difference, weighted by number of trips, was calculated. We found 12 results from eight eligible studies. All studies showed self-reported journey times were greater than GPS-measured times. The difference between self-report and GPS times ranged from over-reporting of +2.2 to +13.5 minutes per journey. The aggregated, pooled estimate of the difference, weighted by number of trips, was over-report of +4.4 minutes (+28.6%). Studies comparing self-reported and GPS-measured journey duration have shown self-reported to be consistently over-reported across the study sample. Our findings suggest that when using self-reported journey behaviour, the journey durations should be treated as an over-estimation.
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paper_title: A Review of GPS Trajectories Classification Based on Transportation Mode

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GPS trajectories generated by moving objects provide researchers with an excellent resource for revealing patterns of human activities. Relevant research based on GPS trajectories includes the fields of location-based services, transportation science, and urban studies among others. Research relating to how to obtain GPS data (e.g., GPS data acquisition, GPS data processing) is receiving significant attention because of the availability of GPS data collecting platforms. One such problem is the GPS data classification based on transportation mode. The challenge of classifying trajectories by transportation mode has approached detecting different modes of movement through the application of several strategies. From a GPS data acquisition point of view, this paper macroscopically classifies the transportation mode of GPS data into single-mode and mixed-mode. That means GPS trajectories collected based on one type of transportation mode are regarded as single-mode data; otherwise it is considered as mixed-mode data. The one big difference of classification strategy between single-mode and mixed-mode GPS data is whether we need to recognize the transition points or activity episodes first. Based on this, we systematically review existing classification methods for single-mode and mixed-mode GPS data and introduce the contributions of these methods as well as discuss their unresolved issues to provide directions for future studies in this field. Based on this review and the transportation application at hand, researchers can select the most appropriate method and endeavor to improve them.
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paper_title: Comparison of Advanced GPS Data Imputation Algorithms for Detection of Transportation Mode and Activity Episode

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GPS (Global Positioning System) data collection has been increasingly considered as an alternative to traditional travel survey methods to collect activity-travel data. Algorithms varying from the informal ad-hoc decision rules to advanced machine learning methods have been applied to extract activity-travel patterns from GPS traces. However, the accuracy of these different algorithms is difficult to compare as it depends on the spatial context in which GPS is used, the number of identified transportation modes, type of input variables and data used for validation. The aim of this paper therefore is to systematically compare the relative performance of different algorithms for the detection of transportation modes and activity episode. In particular, the naive Bayesian, Bayesian network, logistic regression, multilayer perceptron, support vector machine, decision table and C4.5 algorithms are selected and compared for the same data according to the overall error rates and hit ratios. Results show that the Bayesian network has a better performance than the other algorithms in terms of the percentage of correctly identified instances and Kappa values for both the training data and test data in the sense that Bayesian network is relatively efficient and generalizable in the context of GPS data imputation.
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paper_title: A comparative study of machine learning classifiers for modeling travel mode choice

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A comparison of 7 classifiers for travel mode prediction is performed.Prediction accuracy and variable importance for each travel mode is investigated.Among the investigated classifiers, random forest performs best.Trip distance followed by the number of cars are the most important variables.The importance of other variables varies with travel mode and classifier. The analysis of travel mode choice is an important task in transportation planning and policy making in order to understand and predict travel demands. While advances in machine learning have led to numerous powerful classifiers, their usefulness for modeling travel mode choice remains largely unexplored. Using extensive Dutch travel diary data from the years 2010 to 2012, enriched with variables on the built and natural environment as well as on weather conditions, this study compares the predictive performance of seven selected machine learning classifiers for travel mode choice analysis and makes recommendations for model selection. In addition, it addresses the importance of different variables and how they relate to different travel modes. The results show that random forest performs significantly better than any other of the investigated classifiers, including the commonly used multinomial logit model. While trip distance is found to be the most important variable, the importance of the other variables varies with classifiers and travel modes. The importance of the meteorological variables is highest for support vector machine, while temperature is particularly important for predicting bicycle and public transport trips. The results suggest that the analysis of variable importance with respect to the different classifiers and travel modes is essential for a better understanding and effective modeling of peoples travel behavior.
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paper_title: Identifying activity-travel points from GPS-data with multiple moving windows

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Abstract Where GPS technology can precisely register the spatiotemporal elements of activity-travel behaviour, travel characteristics need to be imputed from the data. As such, throughout the last decade, a plethora of methods has been developed to identify trips and activities from raw GPS trajectories. However, rule-based methods that use dwell time for the classification of activity and trip episodes may disregard short activities. In addition, more advanced methods, such as machine learning classifiers, are typically used for travel mode recognition. This paper therefore aims to systematically compare the relative performance of four machine learning algorithms to classify GPS points into activity points and travel points. To account for the underlying spatial structure of the data, attribute information is supplemented by moving multiple spatial windows over preceding and succeeding points to derive local densities. Because ground truth is essential to evaluate the accuracy of the algorithms, a set of 200 artificial GPS activity-travel sequences with varying noise levels was generated to test the performance of the algorithms. The results indicate that especially the random forest algorithm can lead to high accuracies and that local densities are important variables to the accuracy of the classifiers.
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paper_title: MEILI: A travel diary collection, annotation and automation system

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The increased interest in the automation of travel diary collection, together with the ease of access to new artificial intelligence methods led scientists to explore the prerequisites to the autom ...
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paper_title: Using mobile phones to determine transportation modes

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As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6p when tested on a dataset obtained from sixteen individuals.
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paper_title: Commuting to college: The effectiveness and social efficiency of transportation demand management policies

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Commuting is the single largest impact a University has on the environment and represents a noticeable share of urban traffic, when the University is located within a city. There is a large amount of literature on which policies could reduce car use and improve the environmental and social sustainability of commuting to college. However, most studies focus, to the best of our knowledge, only on the effectiveness of such policies, disregarding their social efficiency, measured as the difference between the social costs and benefits. This paper presents an estimate of the effectiveness and the efficiency of nine hypothetical transport policies regarding the University of Trieste, Italy, on the basis of a transport demand model estimated via revealed and stated choice data. All policies but one are effective in reducing car use, but only six of them appear to be efficient. We find that fully subsidizing bus fares would be the most effective and efficient policy. However, it is doubtful whether fully subsidizing bus fares is financially sustainable. The second best policy would be a mix of bus subsidies and parking restrictions. In case of the University of Trieste, our model suggests the adoption of a policy mix based on a relatively low hourly parking tariff (€0.3 per hour) and the use of the parking revenues to subsidize the bus users. The methodology and the results presented in this paper can be used by the college Mobility Managers to design better transport policies.
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paper_title: Prediction of activity mode with global positioning system and accelerometer data.

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ABSTRACTPurpose:The primary aim of this pilot study was to assess how well the combination of global positioning system (GPS) and accelerometer data predicted different activity modes.Methods:Ten adults (seven male, three female; 23-51 yr) simultaneously wore a GPS unit and accelerometer during bout
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paper_title: Continuous earthquake detection and classification using discrete Hidden Markov Models

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SUMMARY ::: ::: We present a novel technique to solve the automatic detection and classification problem of earth tremor in a single step by using Hidden Markov Modelling (HMM). While this technique was originally developed in speech recognition, it already showed great promise when applied to volcano induced seismic signals. We apply the HMM classifier to a much simpler problem, that is, the detection and distance dependent classification of small to medium sized earthquakes. Using the smaller and possibly not perfect data set of earthquakes recorded with three stations of the Bavarian Earthquake Service enables us to better evaluate the advantages and disadvantages of the proposed algorithm and to compare the results with simple and widely used detection techniques (e.g. recursive short-term versus long-term average). Overall the performance of HMM shows good results in the pre-triggered classification tasks and reasonable results in the continuous case. The application of HMMs is illustrated step by step so it can be used as recipe for other applications. Special emphasize is given to the important problem of selecting the features, which best describe the properties of the different signals that are to be classified.
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paper_title: Off-line Recognition of Handwritten Arabic Words Using Multiple Hidden Markov Models

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Hidden Markov Models (HMM) have been used with some success in recognising printed Arabic words. In this paper, a complete scheme for unconstrained Arabic handwritten word recognition based on a Multiple discriminant Hidden Markov Models is presented and discussed. The overall engine of this combination of a global feature scheme with an HMM module, is a system able to classify Arabic-Handwritten words and has been tested on one hundred different writers. The system first attempts to remove some of the variation in the images that do not affect the identity of the handwritten word. Next, the system codes the skeleton and edge of the word such that feature information about the strokes in the skeleton is extracted. Then, a classification process based on a rule based classifier is used as that a global recognition engine to classify words into eight groups. Finally, for each group, the HMM approach is used for trial classification. The output is a word in the lexicon. A detailed experiment has been carried out, and successful recognition results are reported.
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paper_title: Online remaining useful lifetime prediction of proton exchange membrane fuel cells using a novel robust methodology

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Abstract This paper proposes a novel robust prognostic approach that contains three phases for degradation prediction of proton exchange membrane fuel cell (PEMFC) performance and its remaining useful lifetime (RUL) estimation. In the first detrending phase, a physical aging model (PAM) is used to remove the non-stationary trend in the original fuel cell degradation data. In the second filtering phase, the order of autoregressive and moving average (ARMA) model is determined by autocorrelation function (ACF), partial ACF and Akaike information criterion. The linear component in the stationary time series is then filtered by the identified ARMA model. In the third prediction phase, the remaining nonlinear pattern is used to train the time delay neural network (TDNN), in order to provide the final prediction result. Since the proposed prognostic approach uses appropriate methods to analyze and preprocess the original degradation data (i.e., the PAM maintains stationary trend, and then the identified ARMA filters linear component), the remaining nonlinear pattern of stationary time series can thus guarantee a good convergence performance of TDNN. In order to experimentally demonstrate the robustness and prediction accuracy of the proposed approach, degradation tests are performed using two types of PEMFC stack.
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paper_title: Using Smartphones to Perform Transportation Mode Determination at the Trip Level

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In this paper, the authors address the problem of determining the transportation mode at the trip level using mobile phones. The authors describe an algorithm that processes raw global positioning system (GPS) and accelerometer data to detect whether a person is standing, walking, biking, driving or taking public transit and further processes this data to determine the trip mode. This work contributes to existing research, which determines exact transportation modes at each time instant, by adding context to the mode data with trip definitions that are defined by the activity-based travel model specification for the San Francisco Bay Area. The authors describe the features extracted from GPS and accelerometer sensors used to classify modes and trips with machine learning algorithms. The authors present a test of the algorithm conducted in the San Francisco Bay Area consisting of 28 trips.
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paper_title: Supporting large-scale travel surveys with smartphones – A practical approach

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Abstract Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone’s positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a wide-spread basis and can complement existing travel survey methods.
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paper_title: A comparison between continuous and discrete density hidden Markov models for cursive handwriting recognition

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This paper presents the results of the comparison of continuous and discrete density hidden Markov models (HMMs) used for cursive handwriting recognition. For comparison, a subset of a large vocabulary (1000 word), writer-independent online handwriting recognition system for word and sentence recognition was used, which was developed at Duisburg University. This system has some unique features that are rarely found in other HMM-based character recognition systems, such as: (1) option between discrete, continuous, or hybrid modeling of HMM probability density distributions; (2) large vocabulary recognition based on either printed or cursive word or complete sentence input; (3) optimized HMM topology with an unusually large number of HMM states; and (4) use of multiple label streams for coding of handwritten information. Emphasis in this paper is on the comparison between continuous and discrete density HMMs, since this is still an open question in handwriting recognition, and is crucial for the future development of the system. However, in order to give a complete description of the basic system architecture, some of the above mentioned issues are also addressed. The surprising result of our investigation was the fact that discrete density models led to better results than continuous models, although this is generally not the case for HMM-based speech recognition systems. With the optimized system, a 70% word recognition rate was obtained for a challenging large-vocabulary, writer-independent sentence input task.
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paper_title: Speech recognition on an FPGA using Discrete and Continuous Hidden Markov models

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Speech recognition is a computationally demanding task, particularly the stage which uses Viterbi decoding for converting pre-processed speech data into words or sub-word units. Any device that can reduce the load on, for example, a PC's processor, is advantageous. Hence we present FPGA implementations of the decoder based alternately on discrete and continuous hidden Markov models (HMMs) representing monophones, and demonstrate that the discrete version can process speech nearly 5,000 times real time, using just 12% of the slices of a Xilinx Virtex XCV1000, but with a lower recognition rate than the continuous implementation, which is 75 times faster than real time, and occupies 45% of the same device.
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paper_title: Low sampling rate for physical activity recognition

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The monitoring of physical activity by acceleration sensors is very common. Smartphones and it's accessories (Smartwatch, wrist bands) are equipped with sensors and provide enough calculation power for data processing. Body worn mobile devices are recognizing various types of physical activities. The current concept consists of a very high sampling rate, the higher the sampling rate, the better the accuracy of classification. This strategy reduces the battery lifetime, especially for devices with limited physical dimensions, e.g. Smartwatches. Since sampling rate is a relevant factor for energy consumption, this work is analyzing the possibilities and performance of a very low sampling rate for physical activity recognition on Smartwatches. This work proposes the new concept of extremely low sampling rate for physical activity recognition.
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paper_title: An Integrated Stop-Mode Detection Algorithm for Real World Smartphone-Based Travel Survey

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Travel surveys form a key component of transportation planning by making transportation related data available to the planners. Smartphones are emerging as ideal tools for collecting detailed individual travel information, which motivated the authors to develop a smartphone-based travel survey system, Future Mobility Survey (FMS). Inferring people’s stops and modes of transportation is a critical and challenging problem in the FMS system, especially because time phased data collection method has been used to reduce the battery usage. In this paper, the authors propose a novel algorithm for integrated stop and travel mode detection using parsimonious real world data collected from smartphones through FMS. The authors use a two stage classification system to detect five modes of travel, viz., stop, walk, train, car, and bus. To improve accuracy of the classification the authors derive features from a fusion of data collected from GPS,GSM,Wi-Fi and Accelerometer sensors on-board the smartphones. The authors also propose new features based on contextual information and user’s historical data. Experimental results show that the algorithm can effectively perform stop/mode detection and is robust against noisy/incomplete data from the smartphones.
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paper_title: Using Smartphones to Perform Transportation Mode Determination at the Trip Level

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In this paper, the authors address the problem of determining the transportation mode at the trip level using mobile phones. The authors describe an algorithm that processes raw global positioning system (GPS) and accelerometer data to detect whether a person is standing, walking, biking, driving or taking public transit and further processes this data to determine the trip mode. This work contributes to existing research, which determines exact transportation modes at each time instant, by adding context to the mode data with trip definitions that are defined by the activity-based travel model specification for the San Francisco Bay Area. The authors describe the features extracted from GPS and accelerometer sensors used to classify modes and trips with machine learning algorithms. The authors present a test of the algorithm conducted in the San Francisco Bay Area consisting of 28 trips.
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paper_title: Application of GPS-Based Prompted Recall Methods in Two Household Travel Surveys

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GPS-based prompted recall surveys have recently become more prevalent in household travel surveys. This paper begins with a review of several GPS-based prompted recall surveys conducted over the past decade and then compares the approaches and designs of two GPS-based prompted recall surveys implemented within two large-scale metropolitan household travel surveys -- the first conducted in Jerusalem (Israel) and the second in the NYC metropolitan area (28 counties in New York, New Jersey, and Connecticut). Lessons learned and ideas for future enhancements are also discussed.
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paper_title: Development of a Global Positioning System Web-Based Prompted Recall Solution for Longitudinal Travel Surveys

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This paper details the development of a Global Positioning System collection solution for a longitudinal (10-week) survey of driving behavior in Sydney, Australia: the primary purpose is to investigate behavioral responses to variable rate charging regimes. The study calls for data to be transmitted regularly (wirelessly) to check the quality of data as they are being collected and provide the basis for a web-based prompted recall (PR) survey in which participants can view their trips, confirm details, and provide information on who was driving, number of passengers, and trip purpose. Following details of the technological setup, details are provided of the data processing issues involved and the development of the PR survey. Pilot testing of the approach on 30 motorists demonstrates that contrary to popular belief, highly accurate data of this nature can be collected for several weeks with little respondent burden.
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paper_title: Data selection in machine learning for identifying trip purposes and travel modes from longitudinal GPS data collection lasting for seasons

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Abstract Application of machine learning methods shows a popular attempt to identify the purpose of a trip and mode of travel on Global Positioning System (GPS) trajectory data. Data selection for the training and test sets is important in these methods. However, the feasibility and effects of choosing these data from different periods of the year are still unknown. This detail is particularly important since collecting data via GPS decreases the burden on participants to such an extent that it can last for seasons which may own distinct features. In order to bridge this gap, this paper employs Aslan & Zech’s test (AZ-test) and Random Forests (RF) successively to investigate the influence of data selection from different seasons for training and test sets. The dataset obtained in a city with distinct seasons, Hakodate, Japan, is used for our empirical analysis. The results of AZ-test suggest that explanatory variables of the two data sets from distinct seasons follow different distributions. Furthermore, it concludes that data set from two-seasons and data set from single season also follow different distributions. However, this test achieves some contradictory results in some cases. Due to this, RF is used to check how the accuracy varies in a further detail. RF confirms the findings by AZ-test in most cases. In addition, RF results show that including GIS features as explanatory variables has positive effect on the identification accuracy while including weather features has negative effect on the identification accuracy.
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paper_title: Global parameters sensitivity analysis and development of a two-dimensional real-time model of proton-exchange-membrane fuel cells

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This work is supported by European Commission H2020 grant ESPESA (H2020-TWINN-2015) EU Grant agreement No: 692224.
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paper_title: Automating mode detection for travel behaviour analysis by using global positioning systems-enabled mobile phones and neural networks

paper_content:

Travel surveys collect trip data such as origin, destination, mode, duration, distance and purpose of trips, as well as socioeconomic and demographic data for analysis. Transportation planners, policymakers, state departments of transportation, metropolitan planning organisations, industry professionals and academic researchers use survey data to better understand the current demand and performance of the transportation infrastructure, and to plan in preparation for future growth. Next-generation travel surveys will utilise global positioning systems (GPS) to collect trip data with minimal input from survey participants. Owing to their ubiquity, GPS-enabled mobile phones are developing into a promising survey tool. TRAC-IT is a mobile phone application that collects real-time GPS data and requires minimal input from the user for data such as trip purpose, mode and vehicle occupancy. To ease survey burden on participants and enable real-time, mode-specific location-based services, new techniques must be explored to derive more information directly from GPS data. As part of travel survey collection, TRAC-IT is able to passively determine trip mode using GPS-enabled mobile phones and neural networks. The mode detection technique presented in this article can be optimised using a critical point, pre-processing algorithm to reduce the size of required GPS datasets obtained from GPS-enabled mobile phones, thus reducing data collection costs while conserving precious mobile phone resources such as battery life.
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paper_title: Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

paper_content:

Abstract Over the past decade, interest has grown in using Global Positioning System (GPS) data to augment or even to replace traditional travel survey or activity diaries. If the full potential of this new class of data is to be realized, processing techniques will need to be standardized and automated to some degree. This paper develops a multinomial logit (MNL) model to impute travel mode from GPS and hip-mounted accelerometer data. The MNL model is the workhorse of travel demand modeling, but it has rarely been applied to GPS data processing. A web-based recall survey provided over 900 trips for estimation and 500 plus trips for validation from a larger multi-day GPS travel survey in Portland, Oregon. Special attention is given to the imputation of bicycle travel, the identification of which has been given little attention in the North American context. We also apply two existing non-MNL mode imputation models to our Portland data and to compare and test the broader transferability of specific techniques. We find that the MNL model as specified performs well overall, generally outperforming competing model forms on the Portland GPS data. Transit network data and accelerometer data significantly improve model fit for specific modes. Accelerometer data is found in particular to aid model fit for bicycling; however, external validation results were less clear. No benefit is found to segmenting models by traveler age, although not all age groups were covered by the sample. The MNL model shows strong potential for automated GPS processing and, as a commonly used transportation modeling technique, should be relatively easy to implement elsewhere.
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paper_title: Supporting large-scale travel surveys with smartphones – A practical approach

paper_content:

Abstract Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone’s positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a wide-spread basis and can complement existing travel survey methods.
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paper_title: Online Energy Management Strategy of Fuel Cell Hybrid Electric Vehicles: A Fractional-Order Extremum Seeking Method

paper_content:

In this paper, an online energy management control strategy is proposed based on a novel fractional-order extremum seeking (ES) method. The proposed method is an online adaptive optimization algorithm, which can be effectively used in the applications of fuel cell hybrid electric vehicles. Compared with the traditional integer-order ES method, the presented method uses Oustaloup approximation based fractional-order calculus in order to achieve faster convergence speed and higher robustness. A detailed mathematical analysis of the proposed method is presented to give a stability proof and shows how the fractional-order calculus improves the integer-order ES method. In order to support the stability analysis results and demonstrate the effectiveness and robustness of the proposed method, a hardware-in-the-loop test bench is developed to provide two experimental case studies. Experimental results show that, by using the presented fractional-order ES approach, the operation points of a proton exchange membrane fuel cell stack system can be effectively controlled in its maximum efficiency area. In addition, the fuel cell system durability can be improved.
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paper_title: Identifying travel mode from GPS trajectories through fuzzy pattern recognition

paper_content:

Although GPS-based travel survey has been studied by many, automated travel mode detection still remains a technical challenge. This paper proposed and tested a fuzzy approach to travel mode recognition from the GPS travel data collected from 32 volunteers for 142 days in Shanghai. Four speed-related fuzzy variables were selected to characterize five movement patterns (walk, bike, bus, rail, and rest) in the urban daily traffic. Fuzzy sets and membership functions were constructed for the patterns using self-reported sample data. A procedure of data cleaning and trip segmentation was performed to divide GPS trajectories into mode stages. The final step involved determining the travel mode of each mode stage through a min-max fuzzy operation. Evaluation results indicated that the approach handled the data uncertainty and vagueness rather well. It properly incorporated partial information from the fuzzy variables into the mode recognition process for accuracy enhancement.
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paper_title: Design of a linguistic statistical decoder for the recognition of continuous speech

paper_content:

Most current attempts at automatic speech recognition are formulated in an artificial intelligence framework. In this paper we approach the problem from an information-theoretic point of view. We describe the overall structure of a linguistic statistical decoder (LSD) for the recognition of continuous speech. The input to the decoder is a string of phonetic symbols estimated by an acoustic processor (AP). For each phonetic string, the decoder finds the most likely input sentence. The decoder consists of four major subparts: 1) a statistical model of the language being recognized; 2) a phonemic dictionary and statistical phonological rules characterizing the speaker; 3) a phonetic matching algorithm that computes the similarity between phonetic strings, using the performance characteristics of the AP; 4) a word level search control. The details of each of the subparts and their interaction during the decoding process are discussed.
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paper_title: Travel mode imputation using GPS and accelerometer data from a multi-day travel survey

paper_content:

Abstract Over the past decade, interest has grown in using Global Positioning System (GPS) data to augment or even to replace traditional travel survey or activity diaries. If the full potential of this new class of data is to be realized, processing techniques will need to be standardized and automated to some degree. This paper develops a multinomial logit (MNL) model to impute travel mode from GPS and hip-mounted accelerometer data. The MNL model is the workhorse of travel demand modeling, but it has rarely been applied to GPS data processing. A web-based recall survey provided over 900 trips for estimation and 500 plus trips for validation from a larger multi-day GPS travel survey in Portland, Oregon. Special attention is given to the imputation of bicycle travel, the identification of which has been given little attention in the North American context. We also apply two existing non-MNL mode imputation models to our Portland data and to compare and test the broader transferability of specific techniques. We find that the MNL model as specified performs well overall, generally outperforming competing model forms on the Portland GPS data. Transit network data and accelerometer data significantly improve model fit for specific modes. Accelerometer data is found in particular to aid model fit for bicycling; however, external validation results were less clear. No benefit is found to segmenting models by traveler age, although not all age groups were covered by the sample. The MNL model shows strong potential for automated GPS processing and, as a commonly used transportation modeling technique, should be relatively easy to implement elsewhere.
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paper_title: Processing GPS data from travel surveys

paper_content:

In this paper, the authors have described a series of procedures that have been developed to manipulate data collected from GPS devices carried by people or placed in personal vehicles, and used to produce records of the trips made over a period of days or weeks. By setting up various algorithms and rules, the authors have found it to be possible to break the data up into trips with approximately a 95 percent correct rating in identifying real trips. The authors were able to obtain a more complete picture of where a person/vehicle travelled while they had the GPS device. Finally, procedures were developed to allow the impute the mode and purpose of the majority of the recorded trips. (a) For the covering entry of this conference, please see ITRD abstract no. E213716.
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paper_title: Identifying Travel Mode with GPS Data Using Support Vector Machines and Genetic Algorithm

paper_content:

Travel mode identification is one of the essential steps in travel information detection with Global Positioning System (GPS) survey data. This paper presents a Support Vector Classification (SVC) model for travel mode identification with GPS data. Genetic algorithm (GA) is employed for optimizing the parameters in the model. The travel modes of walking, bicycle, subway, bus, and car are recognized in this model. The results indicate that the developed model shows a high level of accuracy for mode identification. The estimation results also present GA’s contribution to the optimization of the model. The findings can be used to identify travel mode based on GPS survey data, which will significantly enhance the efficiency and accuracy of travel survey and data processing. By providing crucial trip information, the results also contribute to the modeling and analyzing of travel behavior and are readily applicable to a wide range of transportation practices.
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paper_title: AN INTRODUCTION TO HIDDEN MARKOV MODELS AND BAYESIAN NETWORKS

paper_content:

We provide a tutorial on learning and inference in hidden Markov models in the context of the recent literature on Bayesian networks. This perspective make sit possible to consider novel generalizations to hidden Markov models with multiple hidden state variables, multiscale representations, and mixed discrete and continuous variables. Although exact inference in these generalizations is usually intractable, one can use approximate inference in these generalizations is usually intractable, one can use approximate inference algorithms such as Markov chain sampling and variational methods. We describe how such methods are applied to these generalized hidden Markov models. We conclude this review with a discussion of Bayesian methods for model selection in generalized HMMs.
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paper_title: Supporting large-scale travel surveys with smartphones – A practical approach

paper_content:

Abstract Collection of travel data is a key task of transportation modeling. Data collection is currently based on costly and time-intensive questionnaires, and can thus only provide limited cross-sectional coverage and inadequate updates. There is an urgent need for technologically supported travel data acquisition tools. We present a novel approach for supporting travel surveys using data collected with smartphones. Individual trips of the person carrying the phone are automatically reconstructed and trip legs are classified into one of eight different modes of transport. This task is performed by an ensemble of probabilistic classifiers combined with a Discrete Hidden Markov Model (DHMM). Classification is based on features extracted from the motion trajectory recorded by the smartphone’s positioning system and signals of the embedded accelerometer. Our approach can cope with GPS signal losses by including positioning data obtained from the mobile phone cell network, and relies solely on accelerometer features when the trajectory cannot be reconstructed with sufficient accuracy. To train and evaluate the models, 355 h of probe travel data were collected in the metropolitan area of Vienna, Austria by 15 volunteers over a period of 2 months. Distinguishing eight different transportation modes, the classification results range from 65% (train, subway) to 95% (bicycle). The increasing popularity of smartphones gives the proposed method the potential to be used on a wide-spread basis and can complement existing travel survey methods.
---
paper_title: Transportation mode recognition using GPS and accelerometer data

paper_content:

Potential advantages of global positioning systems (GPS) in collecting travel behavior data have been discussed in several publications and evidenced in many recent studies. Most applications depend on GPS information only. However, transportation mode detection that relies only on GPS information may be erroneous due to variance in device performance and settings, and the environment in which measurements are made. Accelerometers, being used mainly for identifying peoples’ physical activities, may offer new opportunities as these devices record data independent of exterior contexts. The purpose of this paper is therefore to examine the merits of employing accelerometer data in combination with GPS data in transportation mode identification. Three approaches (GPS data only, accelerometer data only and a combination of both accelerometer and GPS data) are examined. A Bayesian Belief Network model is used to infer transportation modes and activity episodes simultaneously. Results show that the use of accelerometer data can make a substantial contribution to successful imputation of transportation mode. The accelerometer only approach outperforms the GPS only approach in terms of the predictive accuracy. The approach which combines GPS and accelerometer data yields the best performance.
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paper_title: Understanding transportation modes based on GPS data for web applications

paper_content:

User mobility has given rise to a variety of Web applications, in which the global positioning system (GPS) plays many important roles in bridging between these applications and end users. As a kind of human behavior, transportation modes, such as walking and driving, can provide pervasive computing systems with more contextual information and enrich a user's mobility with informative knowledge. In this article, we report on an approach based on supervised learning to automatically infer users' transportation modes, including driving, walking, taking a bus and riding a bike, from raw GPS logs. Our approach consists of three parts: a change point-based segmentation method, an inference model and a graph-based post-processing algorithm. First, we propose a change point-based segmentation method to partition each GPS trajectory into separate segments of different transportation modes. Second, from each segment, we identify a set of sophisticated features, which are not affected by differing traffic conditions (e.g., a person's direction when in a car is constrained more by the road than any change in traffic conditions). Later, these features are fed to a generative inference model to classify the segments of different modes. Third, we conduct graph-based postprocessing to further improve the inference performance. This postprocessing algorithm considers both the commonsense constraints of the real world and typical user behaviors based on locations in a probabilistic manner. The advantages of our method over the related works include three aspects. (1) Our approach can effectively segment trajectories containing multiple transportation modes. (2) Our work mined the location constraints from user-generated GPS logs, while being independent of additional sensor data and map information like road networks and bus stops. (3) The model learned from the dataset of some users can be applied to infer GPS data from others. Using the GPS logs collected by 65 people over a period of 10 months, we evaluated our approach via a set of experiments. As a result, based on the change-point-based segmentation method and Decision Tree-based inference model, we achieved prediction accuracy greater than 71 percent. Further, using the graph-based post-processing algorithm, the performance attained a 4-percent enhancement.
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Title: Detecting Travel Modes from Smartphone-Based Travel Surveys with Continuous Hidden Markov Models

Section 1: Introduction
Description 1: Introduce the issue of traffic congestion, traditional travel surveys, the advent of GPS-based surveys, and the purpose of the study.

Section 2: Data collection and description
Description 2: Describe the data collection process, including respondent instruction, data validation, types of data collected, and survey duration.

Section 3: Feature extraction and selection
Description 3: Explain the process of extracting relevant features from GPS data and the steps and techniques used for feature selection.

Section 4: Travel mode detection with CHMMs
Description 4: Detail the use of continuous hidden Markov models for travel mode detection, including model training and classification methods.

Section 5: Classification results on dataset 1
Description 5: Present the results of travel mode classification using CHMMs on Dataset 1, including performance metrics and feature importance.

Section 6: Comparison with other classifiers
Description 6: Compare the performance of CHMMs with other common classification methods like SVM, MNL, BN, ANN, DHMM, DBN, and MRF.

Section 7: Classification results on dataset 2
Description 7: Show the classification results on Dataset 2, highlighting the improvement with additional data sources like GSM/Wi-Fi and accelerometer.

Section 8: Discussion and conclusion
Description 8: Summarize the contributions of the study, compare with other studies, and suggest future directions for research.

Section 9: Declaration of conflicting interests
Description 9: Declare any potential conflicts of interest related to the research, authorship, and publication of the article.