Research On Resident Travel Time And Space Law And Travel Hotspot Area Based On Taxi GPS Data

The research of traditional residents travel activities usually adopt questionnaires way to study, but there are some factors such as complexity of data collection, long duration and subjective, which cannot fully reflect the spatial and temporal characteristics of the residents’travel. These have brought some difficulties to study. With the popularity and widely used of GPS technology and GIS visualization, which provide effective support for this research. Taxi is a special kind of public transport, with all-weather operations, real-time data, running routes and times determined entirely by the passengers and other characteristics. Therefore, taxi-based trajectory data may better reflect the time and space laws of residents travel and commuting behaviors. Based on this, my paper will use taxi GPS track data to carry out the time and space laws of residents travel and travel hotspots-research. The main content of my paper is reflected in the following aspects:(1) Based on the number of 6599 taxi in Kunming from August 13 to 19,2012 for a week which produced huge amounts of GPS track data as the object of study. Then completing the data preprocessing and map matching algorithm based on nine-rectangle grid to come true map matching, and completing the taxi the extraction of effective path.(2) Using ArcGIS Spatial statistical tools to analyze the distribution of OD Matrix passengers travel, which study urban residents’travel behavior from the macro level temporal to mechanism. Meanwhile, two indicators of the length of residents travel and travel time as travel behaviors characteristic amount, to further found rules of residents travel activities.(3) Based on track point data of the GPS of the taxi to get on and off, computing the situation of the distribution of the total daily travellings and using hourly trips on the residents travel time to study, get working days and rest days residents travel time spatial distribution.(4) The parameters of DBSCAN optimization, and then use this algorithm for GPS track points off point with cluster analysis, then use the ArcGIS platform to make clustering results visual, identify Kunming residents travel hot spots and build aggregate index hot spots. Finally, analyzing both the hot zone and point of interest data to construct vectors based on a point of interest feature. To identify the city functional areas role that hot pots need to undertake.The results of this paper can not only evaluate the transportation of kunming city, but it also can reasonably predict the future of the residents’ travelling demands. At the same time, the city’s hot spots of time and space distribution can provide scientific basis and reference in some field of the public infrastructure planning of Kunming city, land value evaluation, the location of the shops and consumption recommend.