Travel-Time Prediction of Bus Journey With Multiple Bus Trips

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, the authors investigate the problem of travel-time prediction for bus journeys that takes into account a passenger’s riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. They empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. The authors also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as they cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, the authors introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.

Language

  • English

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Filing Info

  • Accession Number: 01723873
  • Record Type: Publication
  • Files: TLIB, TRIS
  • Created Date: Nov 27 2019 4:02PM