-Abdi, A., & O’Hern, S. (2024). Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models. https://www.sciencedirect.com/science/article/pii/S0022437523002049
-Al-bdairi, N. S. S., & Hernandez, S. (2017). An empirical analysis of run-off-road injury severity crashes involving large trucks. Accident Analysis and Prevention, 102, 93–100. doi.org/10.1016/j.aap.2017.02.024
-Alrejjal, A., Farid, A., & Ksaibati, K. (2021). A correlated random parameters approach to investigate large truck rollover crashes on mountainous interstates. Accident Analysis & Prevention, 159, 106233. doi.org/https://doi.org/10.1016/j.aap.2021.106233
-Alrejjal, A., Moomen, M., & Ksaibati, K. (2022). Evaluating the effectiveness of law enforcement in reducing truck crashes for a rural mountainous freeway in Wyoming. Transportation Letters. doi.org/10.1080/19427867.2021.1949089
-Anderson, J. C., & Dong, S. (2017). Heavy-vehicle driver injury severity analysis by time of week: A mixed logit approach using HSIS crash data. ITE Journal (Institute of Transportation Engineers), 87(9), 41–49.
-Azhar, A., Mohd ariff, N., Abu Bakar, M. A., & Roslan, A. (2022). Classification of Driver Injury Severity for Accidents Involving Heavy Vehicles with Decision Tree and Random Forest. Sustainability, 14. doi.org/10.3390/su14074101
-Azimi, G., Rahimi, A., Asgari, H., & Jin, X. (2020). Severity analysis for large truck rollover crashes using a random parameter ordered logit model. Accident Analysis and Prevention, 135(April 2019), 105355.
doi.org/10.1016/j.aap.2019.105355
-Azimi, G., Rahimi, A., Asgari, H., & Jin, X. (2022). Injury Severity Analysis for Large Truck-Involved Crashes: Accounting for Heterogeneity. In Transportation Research Record (Vol. 2676, Issue 11, 15–29). SAGE Publications Ltd.
doi.org/10.1177/03611981221091562
-Bai, Y., Yang, Y., & Li, Y. (2015). Determining the effective location of a portable changeable message sign on reducing the risk of truck-related crashes in work zones. Accident Analysis & Prevention, 83, 197–202.
doi.org/https://doi.org/10.1016/j.aap.2015.07.024
-Banerjee, S., & Khadem, N. K. (2019). Factors influencing injury severity in alcohol-related crashes: A neural network approach using hsis crash data. ITE Journal (Institute of Transportation Engineers), 89(3), 42–49.
-Behnood, A., Mannering, F. L., & Mannering, F. L. (2019). Time-of-day variations and temporal instability of factors affecting injury severities in large-truck crashes. Analytic Methods in Accident Research, 100102.
doi.org/10.1016/j.amar.2019.100102
-Chang, L., & Mannering, F. (1999). Analysis of injury severity and vehicle occupancy in truck- and non-truck-involved accidents. 31, 579–592.
-Dadvar, S., Lee, Y. J., Shin, H. S., & Khodaparasti, H. (2020). Integrated Highway Safety Information System (HSIS) datasets by means of a Roadway Safety Data Integrator (RSDI) tool. Data in Brief, 32(August).
doi.org/10.1016/j.dib.2020.106154
-Das, S., Park, E. S., & Sarkar, S. (2023). Impact of operating speed measures on traffic crashes: Annual and daily level models for rural two-lane and rural multilane roadways.
-Dissanayake, S., & Lu, J. (2002). Analysis of Severity of Young Driver Crashes. 02, 108–114.
-Doecke, S. D., Elsegood, M. E., Thompson, J. P., Ponte, G., Edwards, S. A., & Donovan, S. O. (2024). Preventing and mitigating truck crashes : results from 100 in-depth no-blame truck crash investigations. chrome-extension://hmigninkgibhdckiaphhmbgcghochdjc/pdfjs/web/viewer.html?file=file%3A%2F%2F%2FC%3A%2FUsers%2FMARKAZI%2FDownloads%2FCASR216.pdf
-Feknssa, N., Venkataraman, N., Shankar, V., & Ghebrab, T. (2023). Unobserved heterogeneity in ramp crashes due to alignment, interchange geometry and truck volume: Insights from a random parameter model. Analytic Methods in Accident Research, 37, 100254. doi.org/https://doi.org/10.1016/j.amar.2022.100254
-Gaweesh, S., U. Ahmed, I., & M. Ahmed, M. (2024). Assessment of Large Trucks Crash Severity on a Rural Interstate Road in Wyoming Using Decision Trees and Structural Equation Model.
-Greene, W. H. (2000). Adoption and Intensity of Row-Seeding (Case of Wolaita Zone).
-Haq, M. T., Ampadu, V.-M. K., & Ksaibati, K. (2023). An investigation of brake failure related crashes and injury severity on mountainous roadways in Wyoming. Journal of Safety Research, 84, 7–17.
doi.org/10.1016/j.jsr.2022.10.003
-Haq, M. T., Zlatkovic, M., & Ksaibati, K. (2020a). Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach. Accident Analysis & Prevention, 144, 105654. doi.org/10.1016/j.aap.2020.105654
-Haq, M. T., Zlatkovic, M., & Ksaibati, K. (2020b). Investigating occupant injury severity of truck-involved crashes based on vehicle types on a mountainous freeway: A hierarchical Bayesian random intercept approach. Accident Analysis & doi.org/10.1016/j.aap.2020.105654
-Harris, J. K. (2021). Primer on binary logistic regression. Family Medicine and Community Health, 9(Suppl 1). doi.org/10.1136/fmch-2021-001290
-Iranitalab, A., & Khattak, A. (2020). Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes. Reliability Engineering & System Safety, 199, 106914. doi.org/10.1016/j.ress.2020.106914
-Iranitalab, A., Khattak, A., & Bahouth, G. (2020). Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials. Journal of Safety Research, 74, 71–79.
doi.org/https://doi.org/10.1016/j.jsr.2020.04.010
-Kang, S. (2023). Reexamination of the association between development patterns and truck crashes: A case study in Dallas-Fort Worth, TX. Accident Analysis & Prevention, 186, 107052.
doi.org/https://doi.org/10.1016/j.aap.2023.107052
-Khattak, A. J., Ph, D., & Schneider, R. J. (2003). Trb Paper : 03-2331 Risk Factors In Large Truck Rollovers And Injury Severity : Analysis Of Single-Vehicle Collisions. TRB Annual Meeting, January.
-Liu, J. (2021). Severity Analysis of Large Truck Crashes- Comparision Between the Regression Modeling Methods with Machine Learning Methods.
-Lyman, S., & Braver, E. R. (2003). Occupant deaths in large truck crashes in the United States: 25 years of experience. Accident Analysis & Prevention, 35(5), 731–739.
doi.org/https://doi.org/10.1016/S0001-4575(02)00053-2
-Mehdizadeh, M., Shariat-Mohaymany, A., & Nordfjaern, T. (2019). Driver behaviour and crash involvement among professional taxi and truck drivers: Light passenger cars versus heavy goods vehicles. Transportation Research Part F: Traffic Psychology and Behaviour, 62, 86–98. doi.org/10.1016/j.trf.2018.12.010
-Naik, B., Tung, L., Zhao, S., & Khattak, J. (2016). Weather impacts on single-vehicle truck crash injury severity. 58, 57–59.
-Nasim Khan, M., Das, A., & M. Ahmed, M. (2024). Prediction of Truck-Involved Crash Severity on a Rural Mountainous Freeway Using Transfer Learning with ResNet-50 Deep Neural Network. https://ascelibrary.org/doi/abs/10.1061/JTEPBS.TEENG-7304
-Okafor, S., Adanu, E. K., Lidbe, A., & Jones, S. (2023). Severity analysis of single-vehicle left and right run-off-road crashes using a random parameter ordered logit model. Traffic Injury Prevention, 24(3), 251–255.
doi.org/10.1080/15389588.2023.2174376
-Pasupuleti, N., & Pulugurtha, S. S. (2013). Spatial Extent and Modeling Intracity Truck Crashes. Procedia - Social and Behavioral Sciences, 104, 1188–1197. doi.org/https://doi.org/10.1016/j.sbspro.2013.11.215
-Pour-Rouholamin, M., & Jalayer, M. (2016). Analyzing the Severity of. 45–49.
-Rahimi, E., Shamshiripour, A., Samimi, A., & Kouros, A. (2020). Investigating the injury severity of single-vehicle truck crashes in a developing country. Accident Analysis and Prevention, 137(October 2019), 105444.
doi.org/10.1016/j.aap.2020.105444
-Rosenbloom, T., Eldror, E., & Shahar, A. (2009). Approaches of truck drivers and non-truck drivers toward reckless on-road behavior. Accident Analysis & Prevention, 41(4), 723–728.
doi.org/10.1016/j.aap.2009.03.011
-Safar, M., Benis, K. Z., Alizadeh, S. S., & Bazargani, H. S. (2024). A Review on Factors Affecting Large Truck – Involved Crashes. 10(1), 1559–1570.
-Song, L., & Fan, W. D. (2020). Exploring truck driver-injury severity at intersections considering heterogeneity in latent classes : A case study of North Carolina International Journal of Transportation Exploring truck driver-injury severity at intersections considering heterogeneity. December. doi.org/10.1016/j.ijtst.2020.12.006
-Song, X., Wu, J., Zhang, H., & Pi, R. (2020). Analysis of Crash Severity for Hazard Material Transportation Using Highway Safety Information System Data. SAGE Open, 10(3). doi.org/10.1177/2158244020939924
-Statistics, G. (2022). Binary Logistic Regression in SPSS software. Statistical tutorials and software guides. https://graphpad.ir/binary-logistic-regression-spss/.php
-Sun, M., Zhou, R., Jiao, C., & Sun, X. (2022). Severity Analysis of Hazardous Material Road Transportation Crashes with a Bayesian Network Using Highway Safety Information System Data. International Journal of Environmental Research and Public Health, 19(7). doi.org/10.3390/ijerph19074002
-Tahfim, S., & Chan, Y. (2024). Comparison of Cluster-Based Sampling Approaches for Imbalanced Data of Crashes Involving Large Trucks.
-Teoh, E. R., Carter, D. L., Smith, S., & McCartt, A. T. (2017). Crash risk factors for interstate large trucks in North Carolina. Journal of Safety Research, 62, 13–21. doi.org/10.1016/j.jsr.2017.05.002
-Uddin, M., & Huynh, N. (2017). International Journal of Transportation Factors influencing injury severity of crashes involving HAZMAT trucks. International Journal of Transportation Science and Technology, 1(June), 1–9.
doi.org/10.1016/j.ijtst.2017.06.004
-Uddin, M., & Huynh, N. (2018). Factors influencing injury severity of crashes involving HAZMAT trucks. International Journal of Transportation Science and Technology, 7(1), 1–9.
doi.org/https://doi.org/10.1016/j.ijtst.2017.06.004
-USDOT. (2019). arge Truck and Bus Crash Facts 2017, Federal Motor Carrier Safety Administration.
-Wen, H., Ma, Z., Chen, Z., & Luo, C. (2023). Analyzing the impact of curve and slope on multi-vehicle truck crash severity on mountainous freeways. Accident Analysis & Prevention, 181, 106951.
doi.org/https://doi.org/10.1016/j.aap.2022.106951
-Wu, J., Rasouli, S., Zhao, J., Qian, Y., & Cheng, L. (2023). Large truck fatal crash severity segmentation and analysis incorporating all parties involved: A Bayesian network approach. Travel Behaviour and Society, 30, 135–147.
doi.org/https://doi.org/10.1016/j.tbs.2022.09.003
-Yang, C., Chen, M., & Yuan, Q. (2021). The application of XGBoost and SHAP to examining the factors in freight truck-related crashes: An exploratory analysis. Accident Analysis & Prevention, 158, 106153.
doi.org/https://doi.org/10.1016/j.aap.2021.106153
-Young, R. K., & Liesman, J. (2007a). Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model. Accident Analysis & Prevention, 39(3), 574–580.
doi.org/https://doi.org/10.1016/j.aap.2006.10.002
-Young, R. K., & Liesman, J. (2007b). Estimating the relationship between measured wind speed and overturning truck crashes using a binary logit model. Accident Analysis & Prevention, 39(3), 574–580.
doi.org/https://doi.org/10.1016/j.aap.2006.10.002
-Zaloshnja, E., & Miller, T. R. (2004). Costs of large truck-involved crashes in the United States. Accident Analysis & Prevention, 36(5), 801–808. doi.org/10.1016/j.aap.2003.07.006
-Zhu, X., & Srinivasan, S. (2011). Modeling occupant-level injury severity: An application to large-truck crashes. Accident Analysis & Prevention, 43(4), 1427–1437. doi.org/10.1016/j.aap.2011.02.021
