Abstract
Roadway improvements to reduce the frequency of crashes are of the utmost priority to transportation agencies. To a great extent, implementation of improvement programs depends on the reliable identification of roadway segments with high crash risk. Among all crash types, wrong-way driving (WWD) crashes are considered random in nature and are a major safety concern. The Federal Highway Administration defines WWD specifically for high-speed divided highways and access ramps. This definition excludes all other roadway classifications when a crash occurs in the opposing direction to the legal flow of traffic. Screening 5 years of crash data in Minnesota revealed that WWD resulted in crashes on other types of roadway functional classes. This work aimed to (1) introduce a new term/acronym to the literature for driving in the wrong direction (DWD) on all roadway functional classes, (2) apply a set of count data models to estimate the occurrence of DWD crashes, (3) identify roadway geometric features of high-risk segments for DWD crashes, (4) investigate random effects of covariates due to unobserved factors, and (5) calculate elasticity effects of variables. Final models’ specifications indicate that the negative binomial (NB) mixed effect model was found to be the best-fit model. Focusing on DWD crashes, we uncovered the factors contributing to higher DWD crash-risk segments: log of average annual daily traffic (AADT), number of lanes, sidewalk, and shoulder type. The change in frequency of crashes is also investigated using marginal effects, and safety interventions for preventing DWD crashes are also discussed. Transportation agencies can use the findings of this research, in terms of contributing factors and their relative effects on DWD crashes, to deploy appropriate countermeasures at high-risk locations.
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Data Availability Statement
Some or all data, models, or code used during the study were provided by a third party. Direct requests for these materials may be made to the provider as indicated in the Acknowledgments. Crash, traffic, and roadway data of Minnesota were collected from the Highway Safety Performance System (HSIS).
Acknowledgments
This study is supported by the Tennessee Department of Transportation. Crash, traffic, and roadway data of Minnesota were collected from the Highway Safety Performance System (HSIS), https://www.hsisinfo.org/. The authors confirm contributions to the paper as follows: study conception and design, M. Osman, K. Dey, and S. Mishra; data collection, M. Osman, S. Mishra, and S. El Said; analysis and interpretation of results, M. Osman, K. Dey, S. Mishra, S. El Said, and D. Thapa; draft manuscript preparation, M. Osman, K. Dey, S. Mishra, and D. Thapa. All authors reviewed the results and approved the final version of the manuscript.
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Journal of Transportation Engineering, Part A: Systems
Volume 148 • Issue 8 • August 2022
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© 2022 American Society of Civil Engineers.
History
Received: Sep 23, 2021
Accepted: Mar 10, 2022
Published online: May 19, 2022
Published in print: Aug 1, 2022
Discussion open until: Oct 19, 2022
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Cited by
- Md Tanvir Ashraf, Kakan Dey, Sabyasachee Mishra, Identification of high-risk roadway segments for wrong-way driving crash using rare event modeling and data augmentation techniques, Accident Analysis & Prevention, 10.1016/j.aap.2022.106933, 181, (106933), (2023).