城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.

Visual analytics of urban road transportation data: a survey

Objective Urban transportation has become a global challenge that plagues all global metropolises. Visual analytics techniques have recently become an important intelligent transportation technology that is crucial to the analysis and utilization of big transportation data. This study attempts to review the state-of-the-art of visual analytics techniques in urban transportation data comprehensively. These techniques have been developed since the proliferation of information visualization and visual analytics. Method The discussion mainly focuses on two broad aspects: road traffic analysis problems and other intelligent transportation-related problems. A detailed presentation of the visualization techniques and visual analytics systems is organized according to the transportation data type and transportation problem category. Recent research trends are succinctly summarized. Result Many early studies have focused on designing techniques,such as arrow graph, mosaic map, and traffic wall,to visualize road traffic. With these techniques, current research on road traffic analysis has placed emphasis on traffic events. However, the definition of traffic events remains limited to traffic congestion. Other application areas of visual analytics in the intelligent transportation domain include public transportation, traffic accidents, and human mobility. In recent years, a new research trend of mining and utilizing the social dimensions or social contextual information of vehicle trajectories or traffic events has emerged. Conclusion From traffic flow visualization to visual analysis traffic incidents, from analyzing road traffic status to other urban transportation-related social problems, from analyzing single-source transportation data to multi-source data that are rich in social semantics, and from the traditional interactive visualization paradigm at the PC end to novel media and devices for visual presentation, the depth and breadth of research on transportation data visualization have significantly broadened. The research trend of this domain is also evident.