Modelling of Taxi Dispatch Problem Using Heuristic Algorithms
Taxi routing is a complex task that involves the pickup and delivery by a fleet of vehicles in a specified time, taking into account many parameters and criteria. This paper describes issues related to this problem. It proposes a formal description of the problem and a goal function using a wide variety of criteria. Three heuristic algorithms: Ant Colony Optimization, Artificial Bee Colony and Genetic Algorithm are selected for testing. As a result three variants of these algorithms are implemented: Ant Colony System (ACS), Predict and Select - Artificial Bee Colony (PS-ABC) and Genetic Algorithm (GA) to conduct a survey. Operators used in the algorithms are adapted to a taxi dispatch problem. The analysis is performed in order to find the best parameters for the algorithms according to the data input. Finally, the efficiency of the algorithms are compared in order to determine the best algorithm. In several experiments the Ant Colony System outperforms any other algorithm presented here with respect to taxi working time currently in service.
KeywordsEvolutionary computation Real world applications Vehicle routing Collective decision Reinforcement learning Fleet management
The authors acknowledge the support from the statutory funds of the Wrocław University of Science and Technology.
- 2.Gan, J., An, B., Miao, C.: Optimizing efficiency of taxi systems: scaling-up and handling arbitrary constraints. In: Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2015, International Foundation for Autonomous Agents and Multiagent Systems, Richland, SC, pp. 523–531 (2015)Google Scholar
- 3.Ge, Y., Xiong, H., Tuzhilin, A., Xiao, K., Gruteser, M., Pazzani, M.: An energy-efficient mobile recommender system. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2010, pp. 899–908. ACM, New York (2010)Google Scholar
- 5.Kaggle: ECML/PKDD 15: Taxi trajectory prediction (2015). https://www.kaggle.com/c/pkdd-15-predict-taxi-service-trajectory-i
- 6.Kuo, M.: Taxi dispatch algorithms: why route optimization reigns, March 2016. https://blog.routific.com/taxi-dispatch-algorithms-why-route-optimization-reigns-261cc428699f
- 7.Xu, M., Wang, D., Li, J.: DESTPRE: a data-driven approach to destination prediction for taxi rides. In: Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing, UbiComp 2016, pp. 729–739. ACM, New York (2016)Google Scholar
- 8.Yuan, J., Zheng, Y., Zhang, C., Xie, W., Xie, X., Sun, G., Huang, Y.: T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS 2010, pp. 99–108. ACM, New York (2010)Google Scholar
- 9.Zheng, Y., Liu, Y., Yuan, J., Xie, X.: Urban computing with taxicabs. In: Proceedings of the 13th International Conference on Ubiquitous Computing, UbiComp 2011, pp. 89–98. ACM, New York (2011)Google Scholar