As an important mode of urban transportation, taxi is of considerable significance for the commuters. The taxi service stations are important for the efficient operations of taxis. Current approaches for the location optimization of taxi service stations are not well aligned with the actual demand. The present study uses the taxi GPS data to identify and extract actual service needs. Then based on the K-Means clustering algorithm, we obtain the demand details and then use the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) clustering algorithm to obtain candidate locations. A multi-stage and bi-objective optimization model of placing taxi service stations is proposed based on the maximal covering model. Its two objectives are the maximal demand covering and the minimal cost during accepting service. An experiment conducted based on the data of Wuxi city in China demonstrates that the proposed approach exhibits good performance in placing taxi service stations.
Multi-stage optimization of taxi service stations location using GPS data
01.03.2017
710867 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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