<|begin_of_text|><|start_header_id|>system<|end_header_id|>You are an expert on climate change, human mobility and migration. Answer the question truthfully.<|eot_id|><|start_header_id|>user<|end_header_id|>Here are the title and abstract of a scientific paper probably related to climate change, human mobility and migration..
Title:"Understanding road performance using online traffic condition data."
Abstract:"Traffic conditions predominantly follow a number of recurrent spatiotemporal patterns. An understanding of these recurrent patterns is of great importance to judge whether a road section is likely to be congested, which we called 'road performance'. Traffic condition data, which are freely available online, provide valuable, full coverage, real-time information on traffic conditions. Here, we propose a method for evaluating road performance using the sample points from traffic condition data acquired from Baidu map service for free. By treating human mobility as tidal currents and daily routines as planetary gravities, we utilize a tidal harmonic analysis to establish a simulation model with MATLAB. This model can deal with traffic simulations for a long experimental period in order to obtain a usual road performance. Data from two spatial scales, covering Yunnan Province in China and its capital city, Kunming, were used to evaluate the accuracy of this model. We used 31 days of the dataset to understand the road performance at each scale via three indices: the expectation, variance, and daily patterns of traffic conditions. Our experimental results demonstrate that the tidal current approach to model traffic pattern changes in small and large spatial scales is feasible and promising.."
I have six questions you need to answer.
Q1: Is this article is about 'human migration mobility': 1 = yes; 0 = no. Answer with a number. Do not give a textual explanation.

Q2: What drivers of migration and mobility does the article study? 0 = 'other', 1 = 'climate', 2 = 'environment', 3 = 'economic', 4 = 'conflict', 5 = 'political', 6 = 'social', 7 = 'family'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q3: What is the overall sentiment of the article?:0 = 'neutral', 1 = 'negative', 2 = 'positive'. Answer with a number. Do not give a textual explanation.

Q4: What type of climate or environmental keywords are explicitly mentioned?:  0 = 'none', 1 = 'algal blooms', 2 = 'infestation', 3 = 'infectious diseases', 4 = 'ocean acidification', 5 = 'sea ice', 6 = 'acid rain', 7 = 'drought', 8 = 'cold wave', 9 = 'heat wave', 10 = 'glacial', 11 = 'wildfire', 12 = 'air pollution', 13 = 'biodiversity loss', 14 = 'coastal erosion', 15 = 'land degradation', 16 = 'deforestation', 17 = 'desertification', 18 = 'water pollution', 19 = 'diebacks', 20 = 'permafrost loss', 21 = 'sand mining', 22 = 'sea level rise', 23 = 'earthquake', 24 = 'tsunami', 25 = 'volcanic', 26 = 'flood', 27 = 'avalanche', 28 = 'landslide', 29 = 'downburst', 30 = 'thunderstorm', 31 = 'sandstorm', 32 = 'haze', 33 = 'storm', 34 = 'cyclone', 35 = 'blizzard', 36 = 'hail', 37 = 'snow', 38 = 'tornado', 39 = 'wind', 40 = 'hurricane', 41 = 'typhoon', 42 = 'other'. Answer with a number or a sequence of numbers separated by commas. Do not give a textual explanation. 

Q5: What discipline does the article belong to?: 0 = 'other', 1 = 'social science', 2 = 'natural science', 3 = 'arts and humanities', 4 = 'multidiscipline and interdiscipline'. Answer with a number. Do not give a textual explanation.

Q6: Does the article apply quantitative and/or statistical analysis?: 0 = 'no', 1 = 'yes'. Answer with a number. Do not give a textual explanation.

Use the following template: 

Q1 = [];
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Q6 = [].<|eot_id|><|start_header_id|>assistant<|end_header_id|>Answer:

Understanding road performance using online traffic condition data
Traffic conditions predominantly follow a number of recurrent spatiotemporal patterns. An understanding of these recurrent patterns is of great importance to judge whether a road section is likely to be congested, which we called 'road performance'. Traffic condition data, which are freely available online, provide valuable, full coverage, real-time information on traffic conditions. Here, we propose a method for evaluating road performance using the sample points from traffic condition data acquired from Baidu map service for free. By treating human mobility as tidal currents and daily routines as planetary gravities, we utilize a tidal harmonic analysis to establish a simulation model with MATLAB. This model can deal with traffic simulations for a long experimental period in order to obtain a usual road performance. Data from two spatial scales, covering Yunnan Province in China and its capital city, Kunming, were used to evaluate the accuracy of this model. We used 31 days of the dataset to understand the road performance at each scale via three indices: the expectation, variance, and daily patterns of traffic conditions. Our experimental results demonstrate that the tidal current approach to model traffic pattern changes in small and large spatial scales is feasible and promising.