城市出租车乘客热点搜索算法

Yuhan Dong, Siyuan Qian, Kai Zhang, Yongzhi Zhai
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引用次数: 7

摘要

乘客热点搜索对于提高城市出租车司机的利润至关重要。本文提出了一种两步法的接机热点搜索方法。首先,建立出行相似度模型,量化出行行为的相似度。在第二步中,我们利用亲和传播和模拟退火来识别选定时间段内的每日乘客热点。基于曼哈顿出租车GPS数据的数值结果表明,该方法在不考虑缓冲半径的情况下优于传统的时空聚类方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel passenger hotspots searching algorithm for taxis in urban area
Passenger hotspots searching is essential to increase profits for taxis drivers in urban area. In this paper, we propose a two-step approach for pick-up hotspots searching. In the first step, a traveling similarity model is built to quantify the similarity of traveling behaviors. In the second step, we utilize affinity propagation and simulated annealing to identify the daily passenger hotspots in a selected period. Numerical results based on GPS data of Manhattan taxis suggest that the proposed approach outperforms the traditional spatio-temporal clustering regardless of buffer radius.
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