Urban public transit planning is crucial in reducing traffic congestion and enabling green transportation. However, there is no systematic way to integrate passengers' personal preferences in planning public transit routes and schedules so as to achieve high occupancy rates and efficiency gain of ride-sharing. In this paper, we take the first step tp exact passengers' preferences in planning from history public transit data. We propose a data-driven method to construct a Markov decision process model that characterizes the process of passengers making sequential public transit choices, in bus routes, subway lines, and transfer stops/stations. Using the model, we integrate softmax policy iteration into maximum entropy inverse reinforcement learning to infer the passenger's reward function from observed trajectory data. The inferred reward function will enable an urban planner to predict passengers' route planning decisions given some proposed transit plans, for example, opening a new bus route or subway line. Finally, we demonstrate the correctness and accuracy of our modeling and inference methods in a large-scale (three months) passenger-level public transit trajectory data from Shenzhen, China. Our method contributes to smart transportation design and human-centric urban planning.
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Human-Centric Urban Transit Evaluation and Planning
Public transits, such as buses and subway lines, offer affordable ride-sharing services and reduce the road network traffic, thus have significant impacts in mitigating the urban traffic congestion problem. However, it is non-trivial to evaluate a new transit plan, such as a new bus route or a new subway line, of its future ridership prior to actual deployment, since the travel preferences of passengers along the planned routes may vary. In this paper, we make the first attempt to model passengers' preferences of making various transit choices using a Markov Decision Process (MDP). Moreover, we develop a novel inverse preference learning algorithm to infer the passengers' preferences and predict the future human behavior changes, e.g., ridership, of a new urban transit plan before its deployment. We validate our proposed framework using a unique real-world dataset (from Shenzhen, China) with three subway lines opened during the data time span. With the data collected from both before and after the transit plan deployments, Our evaluation results demonstrated that the proposed framework can predict the ridership with only 19.8% relative error, which is 23%-51% lower than other baseline approaches.
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- Award ID(s):
- 1657350
- NSF-PAR ID:
- 10098197
- Date Published:
- Journal Name:
- 2018 IEEE International Conference on Data Mining (ICDM)
- Page Range / eLocation ID:
- 547 to 556
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICDM.2018.00070
