中文摘要：

出租车作为城市公共交通的重要补充,是城市面向公众的一个窗口,在人们日常出行中起到越来越重要的作用。出租车资源时空分布的不均衡,直接影响到城市公共交通的运行效率和城市形象。通过研究出租车上下客的时空分布特征,不仅可以反映城市居民的工作、生活、出行的规律和模式,也可反映城市空间在不同时段内的动态性和＂热度＂。本文基于出租车GPS轨迹大数据,针对出租车上下客事件轨迹呈现的线状特征,以及城市道路网络空间不同时段＂热度＂的动态分段特征,提出了出租车上下客时空分布的线密度探测模型。该模型通过对时间多粒度描述与表达,对不同城市道路网络空间,进行出租车上下客事件的探测和分析,获取城市出租车上下客的时空分布规律,更深刻地理解和认知了城市空间的动态性。

英文摘要：

As a complement for urban public transportation, taxi plays an increasingly important role in people＇ s life, which is also taken as one of the most important windows opening to the public. With the rapid development of economy, traffic condition is becoming more and more terrible, which causes the heavy traffic situation in many cities in China. Taxi is a type of transportation resourece that is dynamic and unbalanced in different road networks and at different time, which faces a lot of problems, such as the difficulty in finding a taxi for a passenger or finding a passenger for a taxi driver. This makes taxi transportation to be poorly efficient, and negatively affects the performance of government. It is helpful to learn about the dynamic space in the city, and the patterns of citizens＇ working, living and travelling after studying the features and rules of taxis＇ pick-up and drop-off distribution. Moreover, it is helpful to learn about the ＂hotspots＂ in the city, which represents the areas with huge volumes of taxis＇ pick-up and drop-off activities. Based on taxis＇ s GPS trajecotries big data, this paper puts forward a new model called Line Density Model （LDM） to detect the space time distribution pattern of taxis＇pick-up and drop-off activities, in which there are linear trends existing within the taxis＇ pick-up and drop-off, and the ＂hotspots＂ exhibiting linear trends near the road network in the city. Finally, Wuhan city is taken as the testing area, and the experimental result shows that taxis＇ pick-up and drop-off distribution is unbalanced in different areas and at different time, which helps to understand the dynamic and the pattern of the public＇ s working, living and travelling, and gives a reference to find the ＂hotspots＂ at different time in Wuhan city.