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14 February 2024 Customized bus route planning based on taxi order data in a 'many-to-one' scenario
Kuaihui Wang, Wenqiang Chen
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Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130181V (2024) https://doi.org/10.1117/12.3024075
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China
Abstract
Demand identification and route planning are important considerations for the development of customized buses. In the demand identification section, we first identify potential scenarios for customized buses based on the stability and high frequency of passenger flows using taxi order data, then select OD areas in "many-to-one" scenarios and determine passenger demands information between each OD area. In the route planning section, based on spatio-temporal clustering of passenger demands to determine candidate stations for customized buses, customized buses route planning model is constructed with the objective of minimizing passenger travel costs and maximizing operator profits, while considering constraints such as passenger travel time windows and vehicle capacity. In order to balance passenger satisfaction and operator profitability, a dynamic fare function is introduced to calculate the operator's ticket revenue instead of using a fixed fare. The results of the validation using taxi order data from Xi'an demonstrate the feasibility and effectiveness of the proposed customized bus route planning design method in "many-to-one" scenarios. It enables pre-assessment of route profitability, providing decision support for the establishment of customized bus services.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Kuaihui Wang and Wenqiang Chen "Customized bus route planning based on taxi order data in a 'many-to-one' scenario", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130181V (14 February 2024); https://doi.org/10.1117/12.3024075
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KEYWORDS
Data modeling
Genetic algorithms
Elasticity
Particle swarm optimization
Transportation
Design
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