Using Precise Time Offset to Improve Freeway Vehicle Delay Estimates

Abstract

Traffic congestion is getting worse and has resulted in increased travel delays and costs. In order to develop effective intelligent transportation systems (ITS) strategies to mitigate traffic congestion on freeways, a good understanding of its causes and impacts is vital but has not been achieved at a satisfactory level. Over the past several decades, deterministic queuing theory (DQT) has been widely used to evaluate freeway travel delays resulted from traffic congestion. However, several studies evaluated the accuracy of its delay estimates and claimed that the DQT method consistently underestimates vehicle delays. The reason for the underestimation, however, had not been clearly identified. This study aims at exploring the main cause of such underestimation problems and proposing a solution to fix it. Based on theoretical analysis and empirical justification, it was found the underestimation resulted primarily from the inappropriate estimates of the time offsets, that is, the travel times between the queue starting point and the immediate upstream and downstream traffic sensor locations. To address this issue, a microscopic approach was developed and implemented in a computer application to enhance the time offset estimation. This proposed approach was tested using the real vehicle delay data manually extracted from traffic surveillance video cameras. The test results indicated that the improved DQT-based vehicle delay estimates with appropriate time offset settings were very close to the ground-truth data. The underestimation problem associated with the traditional DQT method can be effectively addressed and fairly accurate estimates of vehicle delay can be achieved by the proposed method.

Keywords:

• Traffic Congestion
• Traffic Delay
• Traffic Flow
• Traffic Speed

Acknowledgments

This research is partly funded by National Natural Science Foundation of China (project no. 51028802). The authors are grateful for this funding support. Special thanks to Mark Morse at WSDOT for his help on video data collection.