Modelling the impact of weather and context data on transport mode choices: A case study of GPS trajectories from Beijing

Over the years, researchers have been studying the effect of weather and context data on the transport mode choice. The majority of these works are based on survey data, however the accuracy of their findings relies on how respondents give accurate and honest answers. In this paper, the potential of using GPS trajectories as an alternative to travel surveys in studying the impact of weather and context data on transport mode choices is investigated in Beijing city. In the analysis, we apply both descriptive and statistical models such as the MNL and MNP models. Our findings indicate that temperature has the most prominent effect among weather conditions. For instance, for temperatures greater than 25 C, the walking share increases by 27% and the bike share reduces by 21%, which is line with the results from several survey studies. In addition, the evidence of government policy on transport regulation is revealed when the air quality becomes hazardous as people are encouraged to use environmentally friendly travel mode choices such as the bike instead of the bus and car, which are known CO2 emitters. Moreover, due to a series of traffic restrictions introduced by the Beijing government during the 2008 summer Olympics, a decrease of 17.5% in the car share and an increase of 13% and 10% in the walking and bus shares, respectively are observed. These findings provide a scientific basis for effective transport regulation and planning purpose

User Identification across Asynchronous Mobility Trajectories

With the popularity of location-based services and applications, a large amount of mobility data has been generated. Identification through mobile trajectory information, especially asynchronous trajectory data has raised great concerns in social security prevention and control. This paper advocates an identification resolution method based on the most frequently distributed TOP-N (the most frequently distributed N regions regarding user trajectories) regions regarding user trajectories. This method first finds TOP-N regions whose trajectory points are most frequently distributed to reduce the computational complexity. Based on this, we discuss three methods of trajectory similarity metrics for matching tracks belonging to the same user in two datasets. We conducted extensive experiments on two real GPS trajectory datasets GeoLife and Cabspotting and comprehensively discussed the experimental results. Experimentally, our method is substantially effective and efficiency for user identification