Trajectory Data Driven Transit-Transportation Planning

Abstract

Taxi, bus, and subway are the most commonly used public transportation tools for urban residents. All these three transportation tools have their drawbacks. On one hand, the increasing road traffic flows reduces the traveling speed of taxi and bus, especially in rush hours; on the other hand, subway cannot cover all urban locations. Moreover, taxi is much more expensive than bus and subway for long distance travel. To identify the potential solutions to the above issue, a thorough analysis on Shanghai traffic data, including taxi and subway trajectory data, has been conducted. Based on this urban trajectory data analysis, it has been observed that it is benefit to provide public transit-transportation planning service to passengers with various interests. Although the existing public transportation planning services, such as Google and Baidu maps, can provide subway-bus transit planning service, they cannot provide the transit service between subway and taxi. Moreover, the recommended transit services provided by the existing commercial products are not time varying, which does not reflect the reality scenarios. Therefore, we propose a transit-transportation planning scheme between subway and taxi, which not only trades off travel cost and travel time, but also provides relatively bounded travel plans. Moreover, the proposed subway-taxi transit-transportation scheme can encourage urban residents to take public transportation service, as it can provide a relatively timely and bounded travel time based on real urban traffic. Thus, it can mitigate the pressure of urban road networks, reduce the overall energy consumption of the society, and increase the coverage of public transport systems.