基于载客数据的出租车热门区域功能发现

Taxi Hot Area Function Discovery Based on Passenger Data

根据出租车行驶载客数据中提取的乘客出行模式和上下客热门区域,提出一种出租车热门区域功能发现方法。采用基于交通数据时空特性的出租车行驶数据聚类算法,实现热门区域划分。建立基于潜在Dirichlet分配的热门区域乘客出行特征发现模型,对具有相似乘客出行模式的出租车热门区域进行聚类。通过总结各热门区域的具体功能,发现在不同客流时间段内的区域功能与乘客出行模式间的关系。实验结果表明,该方法能够有效发现热门区域的功能特点。

According to the passenger movement pattern and the hot pick-up and drop-off areas extracted from taxi driving passenger data, this paper proposes a functions discovery method of taxi hot areas. Firstly,it uses taxi driving data clustering algorithm based on the temporal and spatial characteristics of traffic data to realize hot region division. Then, the passengers travel character discovery model of passengers in hot region based on Latent Dirichlet Allocation（LDA） is built to realize clustering hot taxi region with similar passenger travel mode. Finally ,by summarizing the specific function of each area, it can find the relationship between area function and passenger movement patterns at different period of passenger flow. The experimental results show the method can effectively discovery the function characteristics of hot areas.