基于城市交通大数据的车辆类别挖掘及应用分析

实时城市交通监控已成为现代城市管理的一个重要组成部分,视频监控采集的交通大数据在城市管理和交通控制方面得到了越来越多的应用;然而,全城范围内庞大的监控交通大数据还鲜少用于城市交通及城市计算研究。在一个省会城市全城范围内的监控交通大数据上展开了车辆类别挖掘及应用分析研究。首先,定义了周期性私家车、类出租车和公共通勤车三种对城市交通具有重要影响的车辆类别,将车辆类别定义与频繁序列模式挖掘算法相结合提出了相应的挖掘方法。在济南市一周1 704个视频监测点,1.2亿次车辆记录数据上,验证了所提定义及挖掘方法的有效性;其次,以4个居民小区为例挖掘分析了居民出行的交通方式及与周围兴趣点(POI)分布关系,此外,还探索了城市交通大数据与POI相结合在城市规划、需求预测和偏好推荐方面的应用潜能。

Vehicle type mining and application analysis based on urban traffic big data

Real-time urban traffic monitoring has become an important part of modern urban management, and traffic big data collected by video monitoring is wildly applied to urban management and traffic control. However, such huge citywide monitoring traffic big data is rarely used for urban traffic and urban computing research. The vehicle type mining and application analysis were implemented on the citywide monitoring traffic big data of a provincial capital city. Firstly, three types of vehicles with important influence on urban traffic:periodic private car, taxi and public commuter bus were defined. And the corresponding mining method for each type of vehicles was proposed. Experiments on 120 million vehicle records collected from 1704 video monitoring points in Jinan demonstrated the effectiveness of the proposed definitions and mining methods. Secondly, with four communities as examples, the residents' traffic modes and the relationships between the modes and the distribution of surrounding Points of Interest (POI) were mined and analyzed. Moreover, the potential applications of the urban traffic big data incorporated with POI in urban planning, demand forecasting and preference recommendation were explored.