TaxiSim: A Multiagent Simulation Platform for Evaluating Taxi Fleet Operations

Taxi service is an important mode of public transportation in most metropolitan areas since it provides door-to-door convenience in the public domain. Unfortunately, despite all the convenience taxis bring, taxi fleets are also extremely inefficient to the point that over 50% of its operation time c...

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Bibliographic Details
Main Authors: Cheng, Shih-Fen, Nguyen, Thi Duong
Format: Conference Proceeding
Language:English
Subjects:
Aggregates
Computational modeling
Computing methodologies > Artificial intelligence > Distributed artificial intelligence > Multi-agent systems
Computing methodologies > Modeling and simulation
Correlation
Data models
driver behavior
Educational institutions
Information systems > Information retrieval
Information systems > Information storage systems
mobility pattern
multiagent simulation
taxi fleet
urban transportation
Vehicles
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Summary:Taxi service is an important mode of public transportation in most metropolitan areas since it provides door-to-door convenience in the public domain. Unfortunately, despite all the convenience taxis bring, taxi fleets are also extremely inefficient to the point that over 50% of its operation time could be spent in idling state. Improving taxi fleet operation is an extremely challenging problem, not just because of its scale, but also due to fact that taxi drivers are self-interested agents that cannot be controlled centrally. To facilitate the study of such complex and decentralized system, we propose to construct a multiagent simulation platform that would allow researchers to investigate interactions among taxis and to evaluate the impact of implementing certain management policies. The major contribution of our work is the incorporation of our analysis on the real-world driver's behaviors. Despite the fact that taxi drivers are selfish and unpredictable, by analyzing a huge GPS dataset collected from a major taxi fleet operator, we are able to clearly demonstrate that driver's movements are closely related to the relative attractiveness of neighboring regions. By applying this insight, we are able to design a background agent movement strategy that generates aggregate performance patterns that are very similar to the real-world ones. Finally, we demonstrate the value of such system with a real-world case study.
DOI:10.1109/WI-IAT.2011.138