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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. [ABSTRACT FROM AUTHOR]

Traffic state evaluation is a basic and critical work in the research on road traffic congestion. It can provide basic data support for the improvement measures and information release in traffic management and service. The aim of this research is to obtain a comprehensive value to describe traffic state accurately based on the evaluation index system. In this paper, it is carried out using fuzzy c-means (FCM) algorithm and fuzzy entropy weight method. In the framework, traffic flow was classified into six different states to determine the fuzzy range of indices using the improved FCM clustering analysis. Besides, fuzzy entropy weight method is proposed to compute the evaluation result of traffic state for section, road and road network, respectively. The experiments based on the traffic information in a subset of Beijing's road network prove that the findings of traffic evaluation are in accordance with the actual situation and people's sense of traffic state. [ABSTRACT FROM AUTHOR]