Data Driven Analytics for School Location Impacting Urban Traffic Congestion

摘要

Based on the real-time traffic information available recently from the popular travelling service systems like AutoNavi in China, data driven analytics is introduced in this paper to explore implicit factors stressing the traffic congestion in urban areas. Some primary and high schools in the selected areas in Beijing are taken into account to discover the impacts of their locations on the traffic congestion around. The traffic data is extracted from AutoNavi and grouped on links to represent the traffic status in the period of rush hours. The indexes for traffic congestion and its tendency are then defined to describe the convergence and divergence of traffic congestion as well as the degrees of impact and changing gradient. Finally, the visualized analysis is introduced to demonstrate the contributions of all schools to traffic congestion in the selected area. Some suggestions to the governmental administration about how to improve the traffic situation around schools are also discussed.