dFDA-VeD: A Dynamic Future Demand Aware Vehicle Dispatching System
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Abstract (arXiv)
With the rising demand of smart mobility, ride-hailing service is getting popular in the urban regions. These services maintain a system for serving the incoming trip requests by dispatching available vehicles to the pickup points. As the process should be socially and economically profitable, the task of vehicle dispatching is highly challenging, specially due to the time-varying travel demands and traffic conditions. Due to the uneven distribution of travel demands, many idle vehicles could be generated during the operation in different subareas. Most of the existing works on vehicle dispatching system, designed static relocation centers to relocate idle vehicles. However, as traffic conditions and demand distribution dynamically change over time, the static solution can not fit the evolving situations. In this paper, we propose a dynamic future demand aware vehicle dispatching system. It can dynamically search the relocation centers considering both travel demand and traffic conditions. We evaluate the system on real-world dataset, and compare with the existing state-of-the-art methods in our experiments in terms of several standard evaluation metrics and operation time. Through our experiments, we demonstrate that the proposed system significantly improves the serving ratio and with a very small increase in operation cost.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 10 Jun 2021.
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References · 7
- 2017Proximal Policy Optimization Algorithmscited by 15,215
- 2018Attention, Learn to Solve Routing Problems!cited by 656
- 2019DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learningcited by 54
- 2021A Deep Value-network Based Approach for Multi-Driver Order Dispatchingcited by 45
- 2018Faster k-Medoids Clustering: Improving the PAM, CLARA, and CLARANS Algorithmscited by 44
- 2019Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problemcited by 29
- 2020Multi-Objective Predictive Taxi Dispatch via Network Flow Optimizationcited by 1
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