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相比于大城市,中小城市在新型城镇化中至关重要,具有独特的居民出行行为特征,但以往的研究并没有得到足够的关注。目前研究主要使用浮动车数据分析特大城市居民的出行行为,但考虑到小城市土地开发强度低、公共交通不发达、研究空间尺度精细等特点,这些研究方法不能完全适用于针对小城市的研究。因此,本文使用小城市出租车GPS轨迹数据识别上下客事件,沿道路生成随机样点采样得到了分时段的上下客密度,并对其时空动态进行描述和表达;筛选出显著影响上下客密度时空分布的9类设施,建立出租车上下客事件的地理加权回归模型;分析了小城市出租车上下客时空动态与各类城市设施的时空关系,发现在工作日与双休日和一天中不同时段中,不同城市设施对上下客事件的影响具有不同的分布规律及其驱动机制。研究结果可为小城市的城市规划和交通需求精细化管理提供参考。
Compared to big cities, small and medium-sized cities are crucial in new urbanization and have unique characteristics of residents’ travel behavior. However, previous studies have not received enough attention. At present, the research mainly uses floating car data to analyze the travel behavior of mega-city residents. However, considering the characteristics of low land development intensity, underdeveloped public transport and fine research spatial scale in small cities, these research methods can not be completely applied to the research on small cities. Therefore, this paper uses the GPS data of taxis in small cities to identify up and down passengers events, generates random samples of points along the road to get the sub-passenger density in sub-periods, and describes and expresses the spatiotemporal dynamics of the passengers. Distributed 9 types of facilities to establish a geo-weighted regression model of taxi off-board events; analyzed the spatio-temporal relationship between the time-and-flight status of all cabs and the various types of urban facilities in small cities, and found that in different working days, weekends and daytime, Different urban facilities have different distribution rules and their driving mechanisms on the passenger incident. The results can provide reference for the urban planning of small cities and the fine management of traffic demand.