Abstract
The vehicle trajectory data is a feasible way for us to understand and reveal urban traffic conditions and human mobility. Therefore, it is extremely valuable to have a fine-grained picture of large-scale vehicle trajectory data, particularly in two different modes, taxis and buses, over the same period at an urban scale. This paper integrates the trajectory data of approximately 7,000 taxis and 1,500 buses in Changchun City, China and accesses the temporal geographically-explicit network of public transport via sequential snapshots of vehicle trajectory data every 30 seconds of the first week in March 2018. In order to reveal urban traffic conditions and human mobility, we construct two-layer urban traffic network (UTN) between these two different transport modes, take crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. We released this temporal geographically-explicit network of public transport and the dynamics, weighted and directed UTN in simple formats for easy access.
Highlights
Background & SummaryUrban public transport plays an important role in citizens’ daily life as the infrastructure of social economics
Trajectory data can be processed as the materials for effective methods to issue the urban problems, such as travel demand analysis[12,13], discovery of community detection[14,15], analysis of human movements[16,17], especially, it plays an important role to solve the problem of urban traffic congestion[7,18,19,20]
The trajectory data of taxis and buses can reflect the operation of urban. These different urban traffic modes are often managed by a number of companies alone, there is a lack of unified analysis and research on various modes of transportation, only a few studies focus on multilayer aspects[21,22,23,24] of the public transport
Summary
Urban public transport plays an important role in citizens’ daily life as the infrastructure of social economics. Informed by the corrected the data (the details are in the Methods section), we use the map-matching algorithm[25,26] to match the trajectory points available in the dataset to the road network and estimate the hourly traffic volume of each road as well as the average speed. We construct a two-layer urban traffic network (UTN) by taking crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. For each traffic mode has 2 files, each one records UTN of buses and taxis with rows of road ID, and the weight of 24 hours, one is the number of vehicles passing through the road, and the other one is the average passing speed of the road. We provide the ArcGIS shapefiles for the road segments, bus stations and the districts of Changchun city in our dataset
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