A Spatiotemporal Thermo Guidance Based Real-Time Online Ride-Hailing Dispatch Framework

Online ride-hailing platforms can gather travel requests and allocate service vehicles to balance transportation demands and supplies, which may result in an increase in the utilization rate of service resources and improve the transportation efficiency and social welfare. An effective and flexible...

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Bibliographic Details
Published in:IEEE access 2020, Vol.8, p.115063-115077
Main Authors: Guo, Yuhan, Zhang, Yu, Yu, Junyu, Shen, Xueli
Format: Article
Language:English
Subjects:
Algorithms
Car sharing
Dispatching
dynamic timeframe
Heuristic algorithms
Machine learning
Machine learning algorithms
online ride-hailing
Prediction algorithms
Real time
Real-time systems
Spatiotemporal phenomena
spatiotemporal thermo
Transportation
vehicle dispatching
Vehicles
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Summary:Online ride-hailing platforms can gather travel requests and allocate service vehicles to balance transportation demands and supplies, which may result in an increase in the utilization rate of service resources and improve the transportation efficiency and social welfare. An effective and flexible dispatching strategy can significantly reduce the waiting time of passengers and increase the profit of service vehicles. In this paper, we propose a real-time service vehicle dispatching framework in the context of large-scale online ride-hailing, which considers all the main issues involved in the problem in a unified and integrated way. Firstly, we formally define the problem and introduce a novel mathematical model based on the spatiotemporal thermo to guide the dispatching process so as to achieve a better balance of demands and supplies. Secondly, we propose an effective machine learning method to predict the spatiotemporal thermo based on the historical data of multi-dimensional factors. Afterward, decision rules to dynamically segment timeframes and a Kuhn-Munkres based algorithm are developed to solve the dispatching problem. Finally, extensive experiments on large-scale real-world instances are conducted to verify the effectiveness of the proposed framework.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2020.3003942