Key links identification for urban road traffic network based on temporal-spatial distribution of traffic congestion

Abstract

Traffic congestion is now nearly ubiquitous in many urban areas. The improvement of road infrastructure is an effective way to ease traffic congestion, especially the key road links. So, it is a fundamental and important step to identify the key link for improving transportation efficiency. However, most approaches in the current literature use simulated data and need many assumption conditions. The result shows the low comprehensibility and the bad exactitude. This paper provides a new identification method of key links for urban road traffic network (URTN) based on temporal-spatial distribution of traffic congestion. The method involves identifying congestion state, computing time distribution of congestion state and determining key road link. By the cluster analysis of the history field data of URTN, the threshold to determine the traffic congestion of each link can be obtained. Then the time-interval of the traffic congestion can be computed by median filtering. At last, the time-interval coverage is defined and used to determine the target road link whether it is a key road link or not. The method is validated by a real-world case (Beijing road traffic network, BRTN). The result shows the feasibility and accuracy.

Taxi Global Positioning System Data in Urban Road Network: A Methodology to Identify Key Road Clusters Based on Travel Speed–Traffic Volume Correlation

“…To solve this problem, Su et al proposed a method based on space-time correlation importance of roads in the adjacent area (15). Tian et al introduced a methodology to determines key roads based on the temporal-spatial distribution of traffic congestion (16). However, key road clusters whose congestion has a great impact on the network are not necessarily the combination of directly connected key roads (17,18).…”

“…Most studies focus on network topology and fail to recognize the dynamic influence between roads. Though Su et al and Tian et al provide a new view to identify key road from dynamic traffic condition, few studies consider the correlation relationship between travel speed and traffic volume (15,16). In relation to traffic management and the formation mechanism of congestion, it is necessary to identify network bottlenecks from the view of road clusters.…”