Random Coefficient Models for Work Zone Headway Distribution
Publication: Journal of Transportation Engineering, Part A: Systems
Volume 145, Issue 10
Abstract
Vehicle headways in work zones are disaggregated into two types in this study—truck-involved (e.g., car-truck) and nontruck-involved (e.g., car-car) headways. Considering the possible effects of vehicle-following patterns and unobserved heterogeneity, we have developed a multivariate distribution model with random coefficients for each headway type using the work zone headway data from Singapore. Five headway distribution types, including the lognormal, normal, gamma, log-logistic, and Weibull, are considered. The lognormal distribution is found to be the best pattern for both headway types. Results show that, for any given distribution pattern, the distribution model with random coefficients could provide better goodness-of-fit than the model with fixed coefficients. It is further found that there is a bigger effect of work intensity on the truck-involved headways than the nontruck-involved headways. In addition, both types of headways decrease as the traffic flow or truck percentage increases. The likelihood ratio test results confirm the necessity of building a separate distribution model for each headway type in work zones.
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Acknowledgments
The author sincerely thanks the editor and anonymous referees for their helpful comments and valuable suggestions, which considerably improved the exposition of this work. This study is supported by the National Natural Science Foundation of China (Grant No. 71871137). It is also sponsored by Shuguang Program supported by Shanghai Education Development Foundation and Shanghai Municipal Education Commission (Grant No. 16SG41).
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Journal of Transportation Engineering, Part A: Systems
Volume 145 • Issue 10 • October 2019
Copyright
©2019 American Society of Civil Engineers.
History
Received: Jul 9, 2018
Accepted: Feb 25, 2019
Published online: Jul 24, 2019
Published in print: Oct 1, 2019
Discussion open until: Dec 24, 2019
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