位置大数据的分析处理研究进展

现有的关于出租车GPS轨迹大数据的研究均没有考虑出租车司机本身的非运营行为（如出租车加油（气）、就餐、交接班）的特征和需求。根据出租车轨迹大数据，研究了出租车短时非运营行为特征，从轨迹数据中提取出租车短时非运营行为，利用平面线要素核密度分析其时空分布，并采用Ripley's K函数分析了武汉市徐东大街区域的短时非运营行为与加气站的空间相关性。实验结果表明，分析出租车短时非运营行为时空分布能有效地揭示出租车司机群体的短时非运营行为需求，以及需求与现有公共资源不匹配引发的资源低效配置现状，为公共资源优化调整提供科学有效的辅助决策支撑。

Research Progress in Location Big Data Analysis and Processing

Existing studies of big data taxi GPS tracesdo not consider the characteristics and demands of out-of-service taxi driver activities, such as refueling, dining, and shifting activities. This paper studies the these short-term out-of-service behaviors, extracts short-term out-of-service behaviors from taxi trace data, and analyzes the spatio temporal distribution of these events with kernel density estimation (KDE) for linear features. We also analyze the spatial correlation between short-term taxi out-of-service behaviors and locations of gas stations, using Ripley's K function. Our experimental results show that this approach effectively uncovers short-term taxi driver out-of-service demands and exposed the ineffective allocation of urban public resources, by analyzing spatio temporal distribution of short-term out-of-service taxi activities. Our results couldsupport decision-making concerning adjustment and optimization of public resources.