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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]

The chronic traffic congestion undermines the level of satisfaction within a society. This study proposes a departure time model for estimating the temporal distribution of morning rush-hour traffic congestion over urban road networks. The departure time model is developed based on the point queue model that is used for estimating travel time. First, we prove the effectiveness of the travel time model (i.e. point queue), showing that it gives the same travel time estimation as the kinematic wave model does for a road with successive bottlenecks. Then, a variant of the bottleneck model is developed accordingly, aiming to capture travelers' departure time choice for commute trips. The proposed departure time model relaxes a traditional assumption that the last commuter experiences the free flow travel time and considers travelers' unwillingness of late arrivals for work. Numerical experiments show that the morning rush-hour generally starts at 7:29 am and ends at 8:46 am with a traffic congestion delay index (TCDI) of 2.164 for Beijing, China. Furthermore, the estimation of rush-hour start and end time is insensitive to most model parameters including the proportion of travelers who tend to arrive at work earlier than their schedules. [ABSTRACT FROM AUTHOR]