Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

Muamer Abuzwidah; Ahmed Elawady; Ling Wang; Waleed Zeiada

doi:10.28991/CEJ-2024-010-09-019

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Muamer Abuzwidah
mabuzwidah@sharjah.ac.ae
Civil and Environmental Engineering Department, College of Engineering, University of Sharjah, Sharjah,, United Arab Emirates https://orcid.org/0000-0002-5605-3609
Ahmed Elawady Civil and Environmental Engineering Department, College of Engineering, University of Sharjah, Sharjah,, United Arab Emirates
Ling Wang College of Transportation Engineering, Tongji University,, China
Waleed Zeiada 1) Civil and Environmental Engineering Department, College of Engineering, University of Sharjah, Sharjah, United Arab Emirates. 3) Department of Public Works Engineering, Mansoura University, Mansoura 35516, Egypt.

Vol. 10 No. 9 (2024): September

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Connected and Autonomous Vehicles (CAVs) might significantly enhance the transportation system by improving safety, accessibility, efficiency, and sustainability. However, a major challenge lies in ensuring CAVs can operate properly under diverse weather conditions, which have already proven to impair human driving capabilities. This pioneering study aims to bridge a crucial research gap by comprehensively assessing the performance of CAVs on traffic operations and safety across varying weather scenarios. Using microscopic traffic simulation in VISSIM and the Surrogate Safety Assessment Model (SSAM), this study evaluates key metrics, including average speed, delay, number of stops, travel time, and number of conflicts for different CAV market penetration rates. The analysis spans 21 scenarios under clear, light rain, heavy rain, and foggy conditions within a selected urban corridor in the United Arab Emirates. The results showed that the average speed rose by 55% in clear weather, while the average delay, the number of stops, travel time, and the number of accidents decreased by 50%, 50%, 95%, and 68%, respectively. In light rain, the average speed improved by 43%, while the average delay, number of stops, travel time, and the number of accidents reduced by 43%, 56%, 96%, and 74%, respectively. The average speed increased by 82% under heavy rain, while the average delay, the number of stops, the travel time, and the number of accidents all fell by 62%, 68%, 96%, and 74%, respectively. In fog, the average speed rose by 32%, while the average delay, average stop number, travel time, and the number of accidents decreased by 33%, 47%, 90%, and 83%, respectively. Overall, this paper highlights the need for resilient CAV systems adaptable to diverse environmental conditions. It helps advance the understanding of how CAVs can be optimized for safety and efficiency in urban settings, contributing to sustainable transportation solutions. It provides insights into the challenges and innovative approaches for CAV deployment in adverse weather, laying a foundation for future research and the broader implementation of these technologies in urban mobility.

Doi: 10.28991/CEJ-2024-010-09-019

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Abuzwidah, M., Elawady, A., Wang, L., & Zeiada, W. (2024). Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles. Civil Engineering Journal, 10(9), 3070–3089. https://doi.org/10.28991/CEJ-2024-010-09-019

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[1] Fagnant, D. J., & Kockelman, K. (2015). Preparing a nation for autonomous vehicles: Opportunities, barriers and policy recommendations. Transportation Research Part A: Policy and Practice, 77, 167–181. doi:10.1016/j.tra.2015.04.003.
[2] Bajpai, J. N. (2016). Emerging vehicle technologies & the search for urban mobility solutions. Urban, Planning and Transport Research, 4(1), 83–100. doi:10.1080/21650020.2016.1185964.
[3] Abuzwidah, M., Elawady, A., Ashour, A. G., Yilmaz, A. G., Shanableh, A., & Zeiada, W. (2024). Flood Risk Assessment for Sustainable Transportation Planning and Development under Climate Change: A GIS-Based Comparative Analysis of CMIP6 Scenarios. Sustainability (Switzerland), 16(14), 5939. doi:10.3390/su16145939.
[4] Bohm, F., & Häger, K. (2015). Introduction of Autonomous Vehicles in the Swedish Traffic System Effects and Changes Due to the New Self-Driving Car Technology, Uppsala University, Uppsala, Sweden.
[5] Mahmassani, H. S. (2016). 50th Anniversary invited article autonomous vehicles and connected vehicle systems: Flow and operations considerations. Transportation Science, 50(4), 1140–1162. doi:10.1287/trsc.2016.0712.
[6] Singh, S. (2015). Critical reasons for crashes investigated in the National Motor Vehicle Crash Causation Survey. National Highway Traffic Safety Administration. Report No. DOT HS 812 115, 01557283.
[7] Elbaz, Y., Naeem, M., Abuzwidah, M., & Barakat, S. (2020). Effect of drowsiness on driver performance and traffic safety. Advances in Science and Engineering Technology International Conferences, ASET 2020, Dubai, United Arab Emirates. doi:10.1109/ASET48392.2020.9118242.
[8] NHTSA. (2022). Automated Vehicles for Safety. National Highway Traffic Safety Administration, Volume 21, Washington, D.C., United States.
[9] Tanner, J. C. (1952). Effect of weather on traffic flow. Nature, 169(4290), 107. doi:10.1038/169107a0.
[10] Hranac, R., Sterzin, E., Krechmer, D., Rakha, H. A., & Farzaneh, M. (2006). Empirical studies on traffic flow in inclement weather. Appendix B"¯: Model Formulation. United States. Federal Highway Administration. Road Weather Management Program, 3–5.
[11] Kilpeläinen, M., & Summala, H. (2007). Effects of weather and weather forecasts on driver behaviour. Transportation Research Part F: Traffic Psychology and Behaviour, 10(4), 288-299. doi:10.1016/j.trf.2006.11.002.
[12] Brilon, W., & Ponzlet, M. (1997). Variability of speed-flow relationships on German autobahns. Transportation Research Record, 1555, 91–98. doi:10.1177/0361198196155500112.
[13] Alfelor, R., Mahmassani, H. S., & Dong, J. (2009). Incorporating weather impacts in traffic estimation and prediction systems. Institute of Transportation Engineers Annual Meeting and Exhibit 2009, 1, 443–457.
[14] Mahmoud, N., Abdel-Aty, M., Cai, Q., & Abuzwidah, M. (2022). Analyzing the Difference Between Operating Speed and Target Speed Using Mixed-Effect Ordered Logit Model. Transportation Research Record, 2676(9), 596–607. doi:10.1177/03611981221088197.
[15] Smith, B. L., Byrne, K. G., Copperman, R. B., Hennessy, S. M., & Goodall, N. J. (2004, January). An investigation into the impact of rainfall on freeway traffic flow. 83rd annual meeting of the Transportation Research Board, Washington, D.C., United States.
[16] Agarwal, M., Maze, T. H., & Souleyrette, R. R. (2005). Impacts of Weather on Urban Freeway Traffic Flow Characteristics and Facility Capacity. Proceedings of the 2005 Mid-Continent Transportation Research Symposium, August 2005, 1-14.
[17] Abuzwidah, M., & Abdel-Aty, M. (2024). Assessing the impact of express lanes on traffic safety of freeways. Accident Analysis and Prevention, 207. doi:10.1016/j.aap.2024.107718.
[18] Kaisari, N. K., Abuzwidah, M., Elawady, A., & Zeiada, W. (2022). Adverse weather impact on driver performance in the UAE. E3S Web of Conferences, 347, 1020. doi:10.1051/e3sconf/202234701020.
[19] Saha, A. K., & Agrawal, S. (2020). Mapping and assessment of flood risk in Prayagraj district, India: a GIS and remote sensing study. Nanotechnology for Environmental Engineering, 5(2), 1-18. doi:10.1007/s41204-020-00073-1.
[20] Abdel-Aty, M. A., Oloufa, A., Eluru, N., Yu, Y., & Park, J. (2016). Phase II: Real Time Monitoring and Prediction of Reduced Visibility Events on Florida's Highways. Report, BDV24, 901–962,
[21] Peng, Y., Abdel-Aty, M., Shi, Q., & Yu, R. (2017). Assessing the impact of reduced visibility on traffic crash risk using microscopic data and surrogate safety measures. Transportation Research Part C: Emerging Technologies, 74, 295–305. doi:10.1016/j.trc.2016.11.022.
[22] Ahmed, M., Abdel-Aty, M., Qi, S., & Abuzwidah, M. (2014). Synthesis of State-of-the-Art in Visibility Detection Systems' Applications and Research. Journal of Transportation Safety and Security, 6(3), 183–206. doi:10.1080/19439962.2013.824055.
[23] Rezaei, A., & Caulfield, B. (2021). Safety of autonomous vehicles: what are the insights from experienced industry professionals?. Transportation research part F: traffic psychology and behaviour, 81, 472-489. doi:10.1016/j.trf.2021.07.005.
[24] Elawady, A., Abuzwidah, M., & Zeiada, W. (2022). The Benefits of Using Connected Vehicles System on Traffic Delay and Safety at Urban Signalized Intersections. 2022 Advances in Science and Engineering Technology International Conferences, ASET 2022, 1–6. doi:10.1109/ASET53988.2022.9734911.
[25] Kim, B., Heaslip, K. P., Aad, M. A., Fuentes, A., & Goodall, N. (2021). Assessing the impact of automated and connected automated vehicles on Virginia freeways. Transportation Research Record, 2675(9), 870–884. doi:10.1177/03611981211004979.
[26] Fan, R., Yu, H., Liu, P., & Wang, W. (2013). Using VISSIM simulation model and Surrogate Safety Assessment Model for estimating field measured traffic conflicts at freeway merge areas. IET Intelligent Transport Systems, 7(1), 68–77. doi:10.1049/iet-its.2011.0232.
[27] Huang, F., Liu, P., Yu, H., & Wang, W. (2013). Identifying if VISSIM simulation model and SSAM provide reasonable estimates for field measured traffic conflicts at signalized intersections. Accident Analysis and Prevention, 50, 1014–1024. doi:10.1016/j.aap.2012.08.018.
[28] Shahdah, U., Saccomanno, F., & Persaud, B. (2015). Application of traffic microsimulation for evaluating safety performance of urban signalized intersections. Transportation Research Part C: Emerging Technologies, 60, 96–104. doi:10.1016/j.trc.2015.06.010.
[29] Ye, L., & Yamamoto, T. (2019). Evaluating the impact of connected and autonomous vehicles on traffic safety. Physica A: Statistical Mechanics and Its Applications, 526, 12–22. doi:10.1016/j.physa.2019.04.245.
[30] Szarata, A., Ostaszewski, P., & Mirzahossein, H. (2023). Simulating the impact of autonomous vehicles (AVs) on intersections traffic conditions using TRANSYT and PTV VISSIM. Innovative Infrastructure Solutions, 8(6), 164. doi:10.1007/s41062-023-01132-7.
[31] Fujiu, M., Morisaki, Y., & Takayama, J. (2024). Impact of Autonomous Vehicles on Traffic Flow in Rural and Urban Areas Using a Traffic Flow Simulator. Sustainability (Switzerland), 16(2), 658. doi:10.3390/su16020658.
[32] Ahmed, H. U., Ahmad, S., Yang, X., Lu, P., & Huang, Y. (2024). Safety and Mobility Evaluation of Cumulative-Anticipative Car-Following Model for Connected Autonomous Vehicles. Smart Cities, 7(1), 518–540. doi:10.3390/smartcities7010021.
[33] Lu, Z., Ding, N., Gao, J., Fu, C., & Zhang, H. (2023). Safety Benefits Evaluation of Mixed Traffic Flow with Connected and Automated Vehicles under Snowy Conditions. 7th IEEE International Conference on Transportation Information and Safety, ICTIS 2023, 380–385. doi:10.1109/ICTIS60134.2023.10243727.
[34] Hou, G. (2023). Evaluating Efficiency and Safety of Mixed Traffic with Connected and Autonomous Vehicles in Adverse Weather. Sustainability (Switzerland), 15(4), 3138. doi:10.3390/su15043138.
[35] Wiedemann, R., & Reiter, U. (1992). Microscopic traffic simulation: the simulation system Mission, background and actual state. Project ICARUS (V1052) Final Report. Brussels, CEC, 2, 1–53.
[36] Adebisi, A., Liu, Y., Schroeder, B., Ma, J., Cesme, B., Jia, A., & Morgan, A. (2020). Developing Highway Capacity Manual Capacity Adjustment Factors for Connected and Automated Traffic on Freeway Segments. Transportation Research Record, 2674(10), 401–415. doi:10.1177/0361198120934797.
[37] Sukennik, P. (2020). D2.11 Microsimulation Guide for Automated Vehicles. COEXIST, Version: 4.0, 723201.
[38] Fakhrmoosavi, F., Saedi, R., Zockaie, A., & Talebpour, A. (2020). Impacts of connected and autonomous vehicles on traffic flow with heterogeneous drivers spatially distributed over large-scale networks. Transportation research record, 2674(10), 817-830. doi:10.1177/0361198120940997.
[39] Asadi, F. E., Anwar, A. K., & Miles, J. C. (2019). Investigating the potential transportation impacts of connected and autonomous vehicles. 2019 8th IEEE International Conference on Connected Vehicles and Expo, ICCVE 2019 - Proceedings, 1–6. doi:10.1109/ICCVE45908.2019.8964994.
[40] He, S., He, S., Guo, X., Ding, F., Ding, F., Qi, Y., & Chen, T. (2020). Freeway Traffic Speed Estimation of Mixed Traffic Using Data from Connected and Autonomous Vehicles with a Low Penetration Rate. Journal of Advanced Transportation, 1361583. doi:10.1155/2020/1361583.
[41] Dowling, R., Skabardonis, A., & Alexiadis, V. (2004). Traffic Analysis Toolbox Volume III"¯: Guidelines for Applying Traffic Microsimulation Modeling Software. Report No. FHWA-HRT-04-040, U.S. DOT, Federal Highway Administration, Washington, D.C., United States.
[42] Park, B., & Schneeberger, J. D. (2003). Microscopic Simulation Model Calibration and Validation: Case Study of VISSIM Simulation Model for a Coordinated Actuated Signal System. Transportation Research Record, 1856, 185–192. doi:10.3141/1856-20.
[43] Gettman, D., Pu, L., Sayed, T., & Shelby, S. (2008). Surrogate Safety Assessment Model and Validation. Publication No. FHWA-HRT-08-051. Turner-Fairbank Highway Research Center, Virginia, United States.

Publication Start Year:	2015
JCR 2023 Impact Factor (IF):	4.3
JIF QUARTILE:	Q1
SJR 2023 Quartile:	Q1
Acceptance Rate:	27%
Review Speed (Average):	76 days
Issue Per Year:	12
Number of Volumes:	10
Number of Issues:	107
Number of Articles:	1552
Number of Reviewers:	3417
Number of Contributors:	3604
Contributing Countries:	86
No. of WoS Citations:	7986
WoS h-index:	34
Scopus CiteScore:	7.0
No. of Google Citations:	17324
Google h-index:	47
Google i10-index:	393
Abstract Views:	5,819,908
PDF Download:	3,790,637

Assessing the Impact of Adverse Weather on Performance and Safety of Connected and Autonomous Vehicles

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