Abstract
Taxi-scheduling systems are gaining increasing popularity for their benefit in scheduling taxis to serve passengers in need. The taxi-scheduling system is decomposed into two fundamental components: routing with known requests and routing with unknown requests. In the online scenario, one of the critical components of the taxi-scheduling system is the scheduling system with unknown requests, which aims to suggest more efficient routes for drivers. Therefore, this paper focuses on the taxi-scheduling problem based on predictive information in online scenarios. We propose a novel scheduling algorithm based on the predicted spatio-temporal information that attempts to exploit prediction information to recommend profitable driving routes to taxi drivers as well as to reduce the idling travel distance. We experiment on real taxi data sets. The predictive information is input data and generated by a simple probabilistic network model. The results show that our method is able to save from 9.64% to 12.76% total idling travel distance compared with previous works.
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Nguyen, V.S., Pham, Q.D., Nguyen, V.H. (2022). Exploiting Demand Prediction to Reduce Idling Travel Distance for Online Taxi Scheduling Problem. In: Le Thi, H.A., Pham Dinh, T., Le, H.M. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. MCO 2021. Lecture Notes in Networks and Systems, vol 363. Springer, Cham. https://doi.org/10.1007/978-3-030-92666-3_5
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DOI: https://doi.org/10.1007/978-3-030-92666-3_5
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