Bosso, M., Vasconcelos, K. L., Ho, L. L., & Bernucci, L. L. B. (2020). Use of regression trees to predict overweight trucks from historical weigh-in-motion data. Journal of Traffic and Transportation Engineering (English Edition), 7(6), 843-859. https://doi.org/10.1016/j.jtte.2018.07.004
Cafiso, S., & D’Agostino, C. (2016). Assessing the stochastic variability of the Benefit-Cost ratio in roadway safety management. Accident Analysis & Prevention, 93, 189-197. https://doi.org/10.1016/j.aap.2016.04.027
Chajmowicz, H., Saadé, J., & Cuny, S. (2019). Prospective assessment of the effectiveness of autonomous emergency braking in car-to-cyclist accidents in France. Traffic Injury Prevention, 20(sup2), S20-S25. https://doi.org/10.1080/15389588.2019.1679797
Chapelon, J., & Lassarre, S. (2010). Road safety in France : The hard path toward science-based policy. Safety Science, 48(9), 1151-1159. https://doi.org/10.1016/j.ssci.2010.04.015
Chen, F., Lyu, J., & Wang, T. (2020). Benchmarking road safety development across OECD countries : An empirical analysis for a decade. Accident Analysis & Prevention, 147, 105752. https://doi.org/10.1016/j.aap.2020.105752
Chen, J., Wang, S., He, E., Wang, H., & Wang, L. (2021). Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach. Biomedical Signal Processing and Control, 69, 102792. https://doi.org/10.1016/j.bspc.2021.102792
Cheng, X., Wu, Y., Ning, P., Cheng, P., Schwebel, D. C., & Hu, G. (2018). Comparing road safety performance across countries : Do data source and type of mortality indicator matter? Accident Analysis & Prevention, 121, 129-133. https://doi.org/10.1016/j.aap.2018.09.012
Coelho, M. C., & Guarnaccia, C. (2020). Driving Information in a Transition to a connected and autonomous vehicle environment : Impacts on pollutants, noise and safety. Transportation Research Procedia, 45, 740-746. https://doi.org/10.1016/j.trpro.2020.02.103
Coffey, S., & Park, S. (2020). Part-time shoulder use operational impact on the safety performance of interstate 476. Traffic Injury Prevention, 21(7), 470-475. https://doi.org/10.1080/15389588.2020.1795843
Connors, R. D., Maher, M., Wood, A., Mountain, L., & Ropkins, K. (2013). Methodology for fitting and updating predictive accident models with trend. Accident Analysis & Prevention, 56, 82-94. https://doi.org/10.1016/j.aap.2013.03.009
Corben, B. F., Logan, D. B., Fanciulli, L., Farley, R., & Cameron, I. (2010). Strengthening road safety strategy development ‘Towards Zero’ 2008–2020 – Western Australia’s experience scientific research on road safety management SWOV workshop 16 and 17 November 2009. Safety Science, 48(9), 1085-1097. https://doi.org/10.1016/j.ssci.2009.10.005
De Bartolomeo, D., Renzi, E., Tamasi, G., Palermo, G., & Nucci, F. D. (2023). The Italian risk-based approach for the development of an ıntegrated safety management system for road ınfrastructures and its relations with innovative guidelines on the risk management of existing bridges. Transportation Research Procedia, 69, 886-893. https://doi.org/10.1016/j.trpro.2023.02.249
Domenichini, L., Branzi, V., & Meocci, M. (2018). Virtual testing of speed reduction schemes on urban collector roads. Accident Analysis & Prevention, 110, 38-51. https://doi.org/10.1016/j.aap.2017.09.020
El-Sayed, H., Ignatious, H. A., Kulkarni, P., & Bouktif, S. (2020). Machine learning based trust management framework for vehicular networks. Vehicular Communications, 25, 100256. https://doi.org/10.1016/j.vehcom.2020.100256
El-Sayed, H., Zeadally, S., Khan, M., & Alexander, H. (2021). Edge-centric trust management in vehicular networks. Microprocessors and Microsystems, 84, 104271. https://doi.org/10.1016/j.micpro.2021.104271
Fu, Y., Li, C., Luan, T. H., Zhang, Y., & Mao, G. (2018). Infrastructure-cooperative algorithm for effective intersection collision avoidance. Transportation Research Part C: Emerging Technologies, 89, 188-204. https://doi.org/10.1016/j.trc.2018.02.003
Fwa, T. F. (2017). Skid resistance determination for pavement management and wet-weather road safety. International Journal of Transportation Science and Technology, 6(3), 217-227. https://doi.org/10.1016/j.ijtst.2017.08.001
Habtemichael, F. G., & Santos, L. de P. (2012). The need for transition from macroscopic to microscopic traffic management schemes to ımprove safety and mobility. Procedia - Social and Behavioral Sciences, 48, 3018-3029. https://doi.org/10.1016/j.sbspro.2012.06.1269
Road Safety Performance Monitoring Practices: A Literature Review
Road traffic crashes remain a major concern worldwide. They are considered by the World Health Organization as one of the leading causes of death worldwide. To address this road insecurity, many countries are developing national strategies and trying to put in place the necessary action plans for their implementation. At this stage, monitoring of performance is crucial to ensure the efficacy of these road safety systems. The primary objective of this study is to examine the state-of-the-art practices employed in researches for managing road safety systems, specifically performance monitoring, and present the results in an engaging and informative manner. Through a comprehensive review of existing literature, the study seeks to identify essential components to help policymakers develop and monitor the performance of their road safety systems. The findings of this study can serve as a foundation for decision-makers in their efforts to develop and manage effective road safety systems.
Bosso, M., Vasconcelos, K. L., Ho, L. L., & Bernucci, L. L. B. (2020). Use of regression trees to predict overweight trucks from historical weigh-in-motion data. Journal of Traffic and Transportation Engineering (English Edition), 7(6), 843-859. https://doi.org/10.1016/j.jtte.2018.07.004
Cafiso, S., & D’Agostino, C. (2016). Assessing the stochastic variability of the Benefit-Cost ratio in roadway safety management. Accident Analysis & Prevention, 93, 189-197. https://doi.org/10.1016/j.aap.2016.04.027
Chajmowicz, H., Saadé, J., & Cuny, S. (2019). Prospective assessment of the effectiveness of autonomous emergency braking in car-to-cyclist accidents in France. Traffic Injury Prevention, 20(sup2), S20-S25. https://doi.org/10.1080/15389588.2019.1679797
Chapelon, J., & Lassarre, S. (2010). Road safety in France : The hard path toward science-based policy. Safety Science, 48(9), 1151-1159. https://doi.org/10.1016/j.ssci.2010.04.015
Chen, F., Lyu, J., & Wang, T. (2020). Benchmarking road safety development across OECD countries : An empirical analysis for a decade. Accident Analysis & Prevention, 147, 105752. https://doi.org/10.1016/j.aap.2020.105752
Chen, J., Wang, S., He, E., Wang, H., & Wang, L. (2021). Recognizing drowsiness in young men during real driving based on electroencephalography using an end-to-end deep learning approach. Biomedical Signal Processing and Control, 69, 102792. https://doi.org/10.1016/j.bspc.2021.102792
Cheng, X., Wu, Y., Ning, P., Cheng, P., Schwebel, D. C., & Hu, G. (2018). Comparing road safety performance across countries : Do data source and type of mortality indicator matter? Accident Analysis & Prevention, 121, 129-133. https://doi.org/10.1016/j.aap.2018.09.012
Coelho, M. C., & Guarnaccia, C. (2020). Driving Information in a Transition to a connected and autonomous vehicle environment : Impacts on pollutants, noise and safety. Transportation Research Procedia, 45, 740-746. https://doi.org/10.1016/j.trpro.2020.02.103
Coffey, S., & Park, S. (2020). Part-time shoulder use operational impact on the safety performance of interstate 476. Traffic Injury Prevention, 21(7), 470-475. https://doi.org/10.1080/15389588.2020.1795843
Connors, R. D., Maher, M., Wood, A., Mountain, L., & Ropkins, K. (2013). Methodology for fitting and updating predictive accident models with trend. Accident Analysis & Prevention, 56, 82-94. https://doi.org/10.1016/j.aap.2013.03.009
Corben, B. F., Logan, D. B., Fanciulli, L., Farley, R., & Cameron, I. (2010). Strengthening road safety strategy development ‘Towards Zero’ 2008–2020 – Western Australia’s experience scientific research on road safety management SWOV workshop 16 and 17 November 2009. Safety Science, 48(9), 1085-1097. https://doi.org/10.1016/j.ssci.2009.10.005
De Bartolomeo, D., Renzi, E., Tamasi, G., Palermo, G., & Nucci, F. D. (2023). The Italian risk-based approach for the development of an ıntegrated safety management system for road ınfrastructures and its relations with innovative guidelines on the risk management of existing bridges. Transportation Research Procedia, 69, 886-893. https://doi.org/10.1016/j.trpro.2023.02.249
Domenichini, L., Branzi, V., & Meocci, M. (2018). Virtual testing of speed reduction schemes on urban collector roads. Accident Analysis & Prevention, 110, 38-51. https://doi.org/10.1016/j.aap.2017.09.020
El-Sayed, H., Ignatious, H. A., Kulkarni, P., & Bouktif, S. (2020). Machine learning based trust management framework for vehicular networks. Vehicular Communications, 25, 100256. https://doi.org/10.1016/j.vehcom.2020.100256
El-Sayed, H., Zeadally, S., Khan, M., & Alexander, H. (2021). Edge-centric trust management in vehicular networks. Microprocessors and Microsystems, 84, 104271. https://doi.org/10.1016/j.micpro.2021.104271
Fu, Y., Li, C., Luan, T. H., Zhang, Y., & Mao, G. (2018). Infrastructure-cooperative algorithm for effective intersection collision avoidance. Transportation Research Part C: Emerging Technologies, 89, 188-204. https://doi.org/10.1016/j.trc.2018.02.003
Fwa, T. F. (2017). Skid resistance determination for pavement management and wet-weather road safety. International Journal of Transportation Science and Technology, 6(3), 217-227. https://doi.org/10.1016/j.ijtst.2017.08.001
Habtemichael, F. G., & Santos, L. de P. (2012). The need for transition from macroscopic to microscopic traffic management schemes to ımprove safety and mobility. Procedia - Social and Behavioral Sciences, 48, 3018-3029. https://doi.org/10.1016/j.sbspro.2012.06.1269
El Khalaı, I., Chorfı, Z., & Berrado, A. (2023). Road Safety Performance Monitoring Practices: A Literature Review. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 22, 99-110. https://doi.org/10.55549/epstem.1337640