Abstract
Given the extreme difficulty of estimating crash likelihoods, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. Such factors must be identified to develop crash management strategies and congestion management strategies. The objectives of this study are to identify causal factors that affect non-recurrent congestion and to propose some operational strategies for mitigating crash-induced non-recurrent traffic congestion. To achieve these objectives, a case study was conducted to identify spatiotemporal non-recurrent congestion regions using a previously developed method based on historical inductance loop detector data collected from six major freeways in Orange County, California. Based on the case study results, potentially significant factors in non-recurrent congestion were identified using the Cox proportional hazard model. Additionally, with the factors identified as significant, operational strategies were proposed for mitigating non-recurrent congestion due to freeway crashes.
Highlights
It has been speculated that non-recurrent congestion caused by incidents such as crashes, disabled vehicles, spills, weather events, and visual distractions accounts for one half to three fourths of the total congestion on metropolitan freeways [1,2], there are insufficient data to either confirm or deny this conjecture
The method by Chung and Recker [3] was used to identify regions of non-recurrent traffic congestion caused by crashes in Orange County, California, and Cox’s semi-parametric model was used to analyze the results and assess the effect of multiple variables on non-recurrent congestion caused by urban freeway crashes
The first consisted of traffic flow data collected from six major freeways in Orange County, California—Interstate 405 (I-405), Interstate 5 (I-5), State Route 22 (SR-22), State Route 55 (SR-55), State Route 57 (SR-57), and State Route 91 (SR-91)—over the course of one year
Summary
It has been speculated that non-recurrent congestion caused by incidents such as crashes, disabled vehicles, spills, weather events, and visual distractions accounts for one half to three fourths of the total congestion on metropolitan freeways [1,2], there are insufficient data to either confirm or deny this conjecture. Given the extreme difficulty of estimating crash likelihoods, because of the nature of non-recurring congestion, the most important aspect of the development of congestion management strategies is the identification of the factors that affect non-recurrent congestion caused by crashes. This requires basic information on how, where, and to what extent congestion occurs. Chung and Recker [3] presented an approach to quantifying non-recurrent congestion in terms of total delays through spatiotemporal crash impact regions, as well as a relationship between the total delay and the factors that affect it, based on univariate analysis Since this type of analysis describes the total delay with respect to each factor individually, it does not adequately describe the combined effect of the significant factors. The method by Chung and Recker [3] was used to identify regions of non-recurrent traffic congestion caused by crashes in Orange County, California, and Cox’s semi-parametric model was used to analyze the results and assess the effect of multiple variables on non-recurrent congestion caused by urban freeway crashes
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