Abstract
A successful taxi route recommendation system is helpful to achieve a win-win situation for both increasing drivers’ income and improving passengers’ satisfaction. The critical problem in this system is how to find the optimal routes under the highly time-varying and complex traffic environment. By investigating the main factors and comparing various route recommendation methods, in this paper, we handle the taxi route recommendation issue from a new perspective. The relationships between the cruising taxis and passengers are regarded as attraction or repulsion between electric charges. Then based on urban traffic charge heat map, we propose a simple yet effective taxi route recommendation method named TaxiC. TaxiC considers four key factors: the number of passengers, travel distance, traffic conditions, vacant competition, and then recommends driving direction in real time for drivers to help them find the next passengers more efficiently and reduce the cruising time. The experimental results on a real-world data set extracted from 5398 taxis in Xiamen city demonstrate the effectiveness of the proposed method.
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Acknowledgment
This work is supported by the Natural Science Foundation of Fujian Province (China) under Grant No. 2017J01118, by Shenzhen Science and Technology Planning Program under Grant No. JCYJ20170307141019252, and by the National Natural Science Foundation of China under Grant No. 61503313.
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Cheng, Y., Zhou, Q., Lai, Y. (2019). TaxiC: A Taxi Route Recommendation Method Based on Urban Traffic Charge Heat Map. In: Liu, X., et al. Service-Oriented Computing – ICSOC 2018 Workshops. ICSOC 2018. Lecture Notes in Computer Science(), vol 11434. Springer, Cham. https://doi.org/10.1007/978-3-030-17642-6_27
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DOI: https://doi.org/10.1007/978-3-030-17642-6_27
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