TERP: Time-event-dependent Route Planning in Stochastic Multi-modal Transportation Networks with Bike Sharing System

Advanced traveler information systems (ATISs) provide travelers with public transportation information to improve the quality of individual life and alleviate congestion as well as air pollution. However, existing works have not fully incorporated bike sharing systems within ATIS, providing no interaction with other modalities nor taking bike stocks into account. In addition, the uncertainty of traffic conditions and multimodal routing makes it challenging to accurately estimate the travel time. In this paper, we leverage large-scale historical data collected in London and construct a multimodal transportation network, including bus, tube, public bikes, and walking. We solve the modalities aggregation problem by practically modeling the travel time, arrival time, bike stock, and transfer time between different transport modalities. Furthermore, we propose TERP, a time-event-dependent route planner that optimizes both trip duration and reliability. We conduct experiments on extensive real-world data with over 23 million arrival records and 15 million stock records on more than 10 000 stations from transport for London platform (TfL). The results validates 14.91% reduction of actual total trip duration and 56.28% improvement in terms of route reliability in rush hours comparing with TfL.