An Artificial-Neural-Network-Based Model for Real-Time Dispatching of Electric Autonomous Taxis

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2020-10-22
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Hu, Liang
Dong, Jing
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Dong-O'Brien, Jing
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Civil, Construction and Environmental Engineering

The Department of Civil, Construction, and Environmental Engineering seeks to apply knowledge of the laws, forces, and materials of nature to the construction, planning, design, and maintenance of public and private facilities. The Civil Engineering option focuses on transportation systems, bridges, roads, water systems and dams, pollution control, etc. The Construction Engineering option focuses on construction project engineering, design, management, etc.

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The Department of Civil Engineering was founded in 1889. In 1987 it changed its name to the Department of Civil and Construction Engineering. In 2003 it changed its name to the Department of Civil, Construction and Environmental Engineering.

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1889-present

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  • Department of Civil Engineering (1889-1987)
  • Department of Civil and Construction Engineering (1987-2003)
  • Department of Civil, Construction and Environmental Engineering (2003–present)

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This paper presents a real-time dispatching model for electric autonomous vehicle (EAV) taxis that combines mathematical programming and machine learning. The EAV taxi dispatching problem is formulated and solved as an integer linear program that maximizes the total reward for serving customers. The optimal dispatch solutions are generated by simulating electric autonomous taxis that are dispatched by the optimization model. The artificial-neural-network-(ANN)-based model was trained using the optimization model's dispatch solutions to learn the optimal dispatch strategies. Although the dispatch decisions made by the ANN-based model are not optimal, the system's performance is very close to the optimization dispatch model in terms of customer service and taxis' operational efficiency. In addition, the ANN-based dispatch model runs much faster. By comparing with current taxis, it was found that the EAV taxis dispatched by our ANN-based model can improve operational efficiency by reducing empty travel distance. EAV taxis can also reduce fleet size by 15% while maintaining a comparable level of service with the current taxi fleet.

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This is a manuscript of an article published as Hu, Liang, and Jing Dong. "An Artificial-Neural-Network-Based Model for Real-Time Dispatching of Electric Autonomous Taxis." IEEE Transactions on Intelligent Transportation Systems (2020). DOI: 10.1109/TITS.2020.3029141. Posted with permission.

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Wed Jan 01 00:00:00 UTC 2020
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