Diagnosis of Encroachment-Related Work-Zone Crashes by Applying Pattern Recognition

Abstract

Work-zone safety is one of the critical goals of transportation agencies. Vehicles are required to change travel paths and lanes over a short length of a road section at work zones. Distracted drivers, unable to see advanced warning signals and pavement markings delineating the work-zone travel paths, could increase the likelihood of a crash. Recent statistics showed that fatal collisions in work zones had increased by 46% in 2019 compared with 2011. The frequency of roadway departures at work zones, the higher risk of fatalities, and little insight into encroachment types at work zones underscored the need for a thorough study. This study aimed to examine the vehicle encroachment conditions associated with work-zone locations and focused on 4 years (2016 to 2019) of crash data from the Texas Department of Transportation by applying a unique data-mining method known as cluster correspondence analysis. This method identified four clusters in both “no-injury” and “fatal and injury” crash data. Major factors contributing to vehicle encroachment were identified. Three dominating clusters were median-related crashes on two-lane divided high-volume roadways; single-vehicle overturning collisions on two-way divided roadways with unprotected median; and overturning crashes on two-lane undivided roadways in controlled traffic. The findings of this study will be useful for safety engineers to contribute to reducing encroachment-related work-zone crashes.

Keywords

work zone encroachment road departure pattern recognition safety cluster correspondence analysis

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References

NHTSA. Fatality Analysis Reporting System (FARS). National Highway Traffic Safety Administration, U.S. Department of Transportation, Washington, D.C, 2021.

Ullman

G. L.

Pratt

Geedipally

Dadashova

Porter

R. J.

Medina

Fontaine

M. D.

Analysis of Work Zone Crash Characteristics and Countermeasures. Publication 0309471281. Transportation Research Board, Washington, D.C., 2018.

The National Work Zone Safety Information Clearinghouse. Library of Resources to Improve Roadway Work Zone Safety for All Roadway Users. The National Work Zone Safety Information Clearinghouse. https://www.workzonesafety.org/. Accessed July 26, 2021.

Meng

Weng

Evaluation of Rear-End Crash Risk at Work Zone Using Work Zone Traffic Data. Accident Analysis & Prevention, Vol. 43, No. 4, 2011, pp. 1291–1300. https://doi.org/10.1016/j.aap.2011.01.011.

Weng

Meng

Yan

Analysis of Work Zone Rear-End Crash Risk for Different Vehicle-Following Patterns. Accident Analysis & Prevention, Vol. 72, 2014, pp. 449–457.

Zhang

Hassan

Identifying the Factors Contributing to Injury Severity in Work Zone Rear-End Crashes. Journal of Advanced Transportation, Vol. 2019, 2019, p. 4126102. https://doi.org/10.1155/2019/4126102.

Bryden

J. E.

Andrew

L. B.

Fortuniewicz

J. S.

Intrusion Accidents on Highway Construction Projects. Transportation Research Record: Journal of the Transportation Research Board, 2000. 1715: 30–35.

Ullman

G. L.

Finley

M. D.

Bryden

J. E.

Srinivasan

Council

F. M.

Traffic Safety Evaluation of Nighttime and Daytime Work Zones. NCHRP Report No. 627. Transportation Research Board of the National Academies, Washington, D.C., 2008.

Ullman

G. L.

Finley

M. D.

Theiss

Categorization of Work Zone Intrusion Crashes. Transportation Research Record: Journal of the Transportation Research Board, 2011. 2258: 57–63.

10.

Sun

Shan

Pedestrian Crash Analysis With Latent Class Clustering Method. Accident Analysis & Prevention, Vol. 124, 2019, pp. 50–57. https://doi.org/10.1016/j.aap.2018.12.016.

11.

Chang

Zhou

Chan

A. H. S.

Huang

Investigating Injury Severities of Motorcycle Riders: A Two-Step Method Integrating Latent Class Cluster Analysis and Random Parameters Logit Model. Accident Analysis & Prevention, Vol. 131, 2019, pp. 316–326. https://doi.org/10.1016/j.aap.2019.07.012.

12.

Nilsson

Lindman

Victor

Dozza

Definition of Run-off-Road Crash Clusters—For Safety Benefit Estimation and Driver Assistance Development. Accident Analysis & Prevention, Vol. 113, 2018, pp. 97–105. https://doi.org/10.1016/j.aap.2018.01.011.

13.

Chen

Zhang

Liu

(Sean) Qian

Examining Driver Injury Severity in Intersection-Related Crashes Using Cluster Analysis and Hierarchical Bayesian Models. Accident Analysis & Prevention, Vol. 120, 2018, pp. 139–151. https://doi.org/10.1016/j.aap.2018.08.009.

14.

van de Velden

D’Enza

A. I.

Palumbo

Cluster Correspondence Analysis. Psychometrika, Vol. 82, No. 1, 2017, pp. 158–185. https://doi.org/10.1007/s11336-016-9514-0.

15.

Feigenbaum

Fields

M. G.

Purnell

24th Annual Highway Report. Reason Foundation, Los Angeles, CA, August, 2019.

16.

Schrock

S. D.

Ullman

G. L.

Cothron

A. S.

Kraus

Voigt

A. P.

An Analysis of Fatal Work Zone Crashes in Texas. Report FHW A/TX-05/0-4028-1. Texas Department of Transportation, Research and Technology Implementation Office, Austin, 2004.

17.

Whitmire

I. J.

Morgan

J. F.

Oron-Gilad

Hancock

P. A.

The Effect of in-Vehicle Warning Systems on Speed Compliance in Work Zones. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Vol. 54, No. 24, 2010, pp. 2023–2027.

18.

Debnath

Blackman

Haworth

A Review of the Effectiveness of Speed Control Measures in Roadwork Zones. Proc., Occupational Safety in Transport (OSIT) Conference, September 20–21, 2012, Gold Coast, Australia.

19.

Garber

N. J.

Woo

T.-S. H.

Accident Characteristics at Construction and Maintenance Zones in Urban Areas. Virginia Transportation Research Council, Charlottesville, 1990.

20.

Osman

Paleti

Mishra

Analysis of Passenger-Car Crash Injury Severity in Different Work Zone Configurations. Accident Analysis & Prevention, Vol. 111, 2018, pp. 161–172. https://doi.org/10.1016/j.aap.2017.11.026.

21.

Liu

Yang

Huang

G.-B.

Yeo

Y. K.

Lin

Driver Distraction Detection Using Semi-Supervised Machine Learning. IEEE Transactions on Intelligent Transportation Systems, Vol. 17, No. 4, 2016, pp. 1108–1120. https://doi.org/10.1109/TITS.2015.2496157.

22.

NHTSA. Fatality Analysis Reporting System (FARS). https://www.nhtsa.gov/research-data/fatality-analysis-reporting-system-fars. Accessed July 19, 2021.

23.

Garber

N. J.

Zhao

Distribution and Characteristics of Crashes at Different Work Zone Locations in Virginia. Transportation Research Record: Journal of the Transportation Research Board, 2002. 1794: 19–25.

24.

Raub

R. A.

Sawaya

O. B.

Schofer

J. L.

Ziliaskopoulos

Enhanced Crash Reporting to Explore Workzone Crash Patterns. Paper No. 01-0166. Northwestern University Center for Public Safety, Evanston, IL, 2001.

25.

Hargroves

B. T.

Vehicle Accidents in Highway Work Zones. Transportation Engineering Journal of ASCE, Vol. 107, No. 5, 1981, pp. 525–539. https://doi.org/10.1061/TPEJAN.0000946.

26.

Nemeth

Rathi

Freeway Work Zone Accident Characteristics. Transportation Quarterly, Vol. 37, No. 1, 1983, pp. 145–159.

27.

Abdel-Aty

Analysis of Driver Injury Severity Levels at Multiple Locations Using Ordered Probit Models. Journal of Safety Research, Vol. 34, No. 5, 2003, pp. 597–603. https://doi.org/10.1016/j.jsr.2003.05.009.

28.

Anastasopoulos

P. C.

Mannering

F. L.

An Empirical Assessment of Fixed and Random Parameter Logit Models Using Crash- and Non-Crash-Specific Injury Data. Accident Analysis & Prevention, Vol. 43, No. 3, 2011, pp. 1140–1147.

29.

Akepati

S. R.

Dissanayake

Characteristics of the Work Zone Crashes. Proc., 1st Congress of Transportation and Development Institute (TDI), Chicago, IL, 2011.

30.

McGee

H. W.

Practices for Preventing Roadway Departures. Transportation Research Board, Washington, D.C., 2018.

31.

Alshatti

D. A.

Examining Driver Risk Factors in Road Departure Conflicts Using SHRP2 Data. Master’s thesis. University of Dayton, OH, 2018.

32.

McLaughlin

S. B.

Hankey

J. M.

Klauer

S. G.

Dingus

T. A.

Contributing Factors to Run-Off-Road Crashes and Near-Crashes. National Highway Traffic Safety Administration, Washington, D.C., 2009.

33.

Rahman

M. A.

Das

Sun

Understanding the Drowsy Driving Crash Patterns From Correspondence Regression Analysis. Journal of Safety Research, 2022. https://doi.org/10.1016/j.jsr.2022.10.017.

34.

Rahman

M. A.

Das

Sun

Hossain

M. M.

Using Unsupervised Learning to Investigate Injury-Associated Factors of Animal-Vehicle Crashes. International Journal of Injury Control and Safety Promotion, 2022, pp. 1–10. https://doi.org/10.1080/17457300.2022.2125532.

35.

Das

Tamakloe

Kutela

Hossain

Pattern Recognition From Injury Severity Types of Frontage Roadway Crashes. Journal of Transportation Safety and Security, 2022, pp. 1–22.

36.

Das

Artificial Intelligence in Highway Safety. CRC Press, Boca Raton, FL, 2022.

37.

Rahman

M. A.

Das

Sun

Single-Vehicle Run-Off Road Crashes Because of Cellphone Distraction: Finding Patterns With Rule Mining. Transportation Research Record: Journal of the Transportation Research Board, 2022.

38.

Rahman

M. A.

Das

Sun

Using Cluster Correspondence Analysis to Explore Rainy Weather Crashes in Louisiana. Transportation Research Record: Journal of the Transportation Research Board, 2022. 2676: 159–173.

39.

Das

Hossain

M. M.

Rahman

M. A.

Kong

Sun

Mamun

Understanding Patterns of Moped and Seated Motor Scooter (50 cc or Less) Involved Fatal Crashes Using Cluster Correspondence Analysis. Transportmetrica A: Transport Science, 2021, pp. 1–21. https://doi.org/10.1080/23249935.2022.2029613.