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Research Articles

Analyzing the time-varying patterns of contributing factors in work zone-related crashes

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References

  • Behnood, A., & Mannering, F. (2019). Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes. Analytic Methods in Accident Research, 23, 100102. doi:10.1016/j.amar.2019.100102
  • Clark, J. B., & Fontaine, M. D. (2015). Exploration of work zone crash causes and implications for safety performance measurement programs. Transportation Research Record: Journal of the Transportation Research Board, 2485(1), 61–69. doi:10.3141/2485-08
  • Cheng, Y., Parker, S., Ran, B., & Noyce, D. A. (2016). Work zone crash cost prediction with a least median squares linear regression model. Transportation Research Record: Journal of the Transportation Research Board, 2555(1), 38–45. doi:10.3141/2555-05
  • Cheng, Y., Wu, K., Li, H., Parker, S., Ran, B., & Noyce, D. (2022). Work zone crash occurrence prediction based on planning stage work zone configurations using an artificial neural network. Transportation Research Record: Journal of the Transportation Research Board, 2676(11), 377–384. doi:10.1177/03611981221092716
  • Das, A., Ahmed, M. M., & Ghasemzadeh, A. (2019). Using trajectory-level SHRP2 naturalistic driving data for investigating driver lane-keeping ability in fog: An association rules mining approach. Accident; Analysis and Prevention, 129, 250–262. doi:10.1016/j.aap.2019.05.024
  • Das, S., Dutta, A., Avelar, R., Dixon, K., Sun, X., & Jalayer, M. (2019). Supervised association rules mining on pedestrian crashes in urban areas: Identifying patterns for appropriate countermeasures. International Journal of Urban Sciences, 23(1), 30–48. doi:10.1080/12265934.2018.1431146
  • Federal Highway Administration (FHWA). (2022). Work zone facts and statistics. https://ops.fhwa.dot.gov/wz/resources/facts_stats.htm
  • Hahsler, M., Grun, B., & Hornik, K. (2005). Arules – A computational environment for mining association rules and frequent item sets. Journal of Statistical Software, 14(15), 1–25. doi:10.18637/jss.v014.i15
  • Hong, J., Tamakloe, R., & Park, D. (2020). Application of association rules mining algorithm for hazardous materials transportation crashes on expressway. Accident; Analysis and Prevention, 142, 105497. doi:10.1016/j.aap.2020.105497
  • Mannering, F. (2018). Temporal instability and the analysis of highway accident data. Analytic Methods in Accident Research, 17, 1–13. doi:10.1016/j.amar.2017.10.002
  • Islam, M., Alnawmasi, N., & Mannering, F. (2020). Unobserved heterogeneity and temporal instability in the analysis of work-zone crash-injury severities. Analytic Methods of Accident Research, 28, 100130. doi:10.1016/j.amar.2020.100130
  • Koilada, K., Mane, A. S., & Pulugurtha, S. S. (2020). Odds of work zone crash occurrence and getting involved in advance warning, transition, and activity areas by injury severity. IATSS Research, 44(1), 75–83. doi:10.1016/j.iatssr.2019.07.003
  • Li, Y., Song, L., & Fan, W. (2021). Day-of-the-week variations and temporal instability of factors influencing pedestrian injury severity in pedestrian-vehicle crashes: A random parameters logit approach with heterogeneity in means and variances. Analytic Methods in Accident Research, 29, 100152. doi:10.1016/j.amar.2020.100152
  • Li, Y., & Bai, Y. (2008). Development of crash-severity-index models for the measurement of work zone risk levels. Accident; Analysis and Prevention, 40(5), 1724–1731. doi:10.1016/j.aap.2008.06.012
  • Lyu, P., Lin, Y., Wang, L., & Yang, X. (2017). Variable speed limit control for delay and crash reductions at freeway work zone area. Journal of Transportation Engineering, Part A: Systems, 143(12), 1–10. doi:10.1061/JTEPBS.0000099
  • Liu, J., Khattak, A., & Zhang, M. (2016). What role do precrash driver actions play in work zone crashes? Application of hierarchical models to crash data. Transportation Research Record: Journal of the Transportation Research Board, 2555(1), 1–11. doi:10.3141/2555-01
  • Lord, D., & Mannering, F. (2010). The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transportation Research Part A: Policy and Practice, 44(5), 291–305. doi:10.1016/j.tra.2010.02.001
  • Meng, Q., & Weng, J. (2011). Evaluation of rear-end crash risk at work zone using work zone traffic data. Accident Analysis & Prevention, 43(4), 1291–1300. doi:10.1016/j.aap.2011.01.011
  • Mokhtarimousavi, S., Anderson, J. C., Hadi, M., & Azizinamini, A. (2021). A temporal investigation of crash severity factors in worker-involved work zone crashes: Random parameters and machine learning approaches. Transportation Research Interdisciplinary Perspectives, 10, 100378. doi:10.1016/j.trip.2021.100378
  • Mokhtarimousavi, S., Anderson, J. C., Azizinamini, A., & Hadi, M. (2019). Improved support vector machine models for work zone crash injury severity prediction and analysis. Transportation Research Record: Journal of the Transportation Research Board, 2673(11), 680–692. doi:10.1177/0361198119845899
  • Mondal, A. R., Bhuiyan, M. A. E., & Yang, F. (2020). Advancement of weather-related crash prediction model using nonparametric machine learning algorithms. SN Applied Sciences, 2(8), 1–11. doi:10.1007/s42452-020-03196-x
  • Montella, A., Aria, M., Ambrosio, A. D., & Mauriello, F. (2012). Analysis of powered two-wheeler crashes in Italy by classification trees and rules discovery. Accident; Analysis and Prevention, 49, 58–72. doi:10.1016/j.aap.2011.04.025
  • Osman, M., Paleti, R., & Mishra, S. (2018). Analysis of passenger-car crash injury severity in different work zone configurations. Accident; Analysis and Prevention, 111, 161–172. doi:10.1016/j.aap.2017.11.026
  • Pande, A., & Abdel-Aty, M. (2009). Market basket analysis of crash data from large jurisdictions and its potential as a decision support tool. Safety Science. 47(1), 145–154. doi:10.1016/j.ssci.2007.12.001
  • Savolainen, P. T., Mannering, F. L., Lord, D., & Quddus, M. A. (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives. Accident; Analysis and Prevention, 43(5), 1666–1676. doi:10.1016/J.AAP.2011.03.025
  • Song, L., Fan, W. (., & Li, Y. (2021). Time-of-day variations and the temporal instability of multi-vehicle crash injury severities under the influence of alcohol or drugs after the Great Recession. Analytic Methods in Accident Research, 32, 100183. doi:10.1016/j.amar.2021.100183
  • Tamakloe, R., Das, S., Nimako Aidoo, E., & Park, D. (2022). Factors affecting motorcycle crash casualty severity at signalized and non-signalized intersections in Ghana: Insights from a data mining and binary logit regression approach. Accident; Analysis and Prevention, 165, 106517. doi:10.1016/j.aap.2021.106517
  • Tamakloe, R., Lim, S., Sam, E. F., Park, S. H., & Park, D. (2021). Investigating factors affecting bus/minibus accident severity in a developing country for different subgroup datasets characterised by time, pavement, and light conditions. Accident; Analysis and Prevention, 159, 106268. doi:10.1016/j.aap.2021.106268
  • Tamakloe, R., & Park, D. (2023). Factors influencing fatal vehicle-involved crash consequence metrics at spatio-temporal hotspots in South Korea: Application of GIS and machine learning techniques. International Journal of Urban Sciences, 27(3), 483–517. doi:10.1080/12265934.2022.2134182
  • Tribby, C. P., Miller, H. J., Song, Y., & Smith, K. R. (2013). Do air quality alerts reduce traffic? An analysis of traffic data from the Salt Lake City metropolitan area, Utah, USA. Transport Policy, 30, 173–185. doi:10.1016/j.tranpol.2013.09.012
  • Ullman, G. L., Pratt, M., Geedipally, S., Dadashova, B., Porter, R. J., Medina, J., & Fontaine, M. D. (2018). Analysis of work zone crash characteristics and countermeasures. Washington DC: Transportation Research Board.
  • Weng, J., Meng, Q., & Yan, X. (2014). Analysis of work zone rear-end crash risk for different vehicle-following patterns. Accident; Analysis and Prevention, 72, 449–457. doi:10.1016/j.aap.2014.08.003
  • Weng, J., Xue, S., Yang, Y., Yan, X., & Qu, X. (2015). In-depth analysis of drivers’ merging behavior and rear-end crash risks in work zone merging areas. Accident; Analysis and Prevention, 77, 51–61. doi:10.1016/j.aap.2015.02.002
  • Weng, J., Zhu, J.-Z., Yan, X., & Liu, Z. (2016). Investigation of work zone crash casualty patterns using association rules. Accident; Analysis and Prevention, 92, 43–52. doi:10.1016/j.aap.2016.03.017
  • Yang, H., Kaan, O., Ozgur, O., & Kun, X. (2015). Work zone safety analysis and modeling: A state-of-the-art review. Traffic Injury Prevention, 16(4), 387–396. doi:10.1080/15389588.2014.948615
  • Yang, D., Zhao, X., Chen, Y., Zhang, X., & Chen, C. (2018). Study on the day-based work zone scheduling problem in urban road networks based on the day-to-day traffic assignment model. Transportation Research Record: Journal of the Transportation Research Board, 2672(16), 14–22. doi:10.1177/0361198118757982
  • Zhang, K., & Hassan, M. (2019). Crash severity analysis of nighttime and daytime highway work zone crashes. PloS One, 14(8), e0221128. doi:10.1371/journal.pone.0221128

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