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Year 2023, Volume: 22, 99 - 110, 01.09.2023

Ibtissam El Khalaı Zoubida Chorfı Abdelaziz Berrado

https://doi.org/10.55549/epstem.1337640

Abstract

References

  • 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, F., Wu, J., Chen, X., Wang, J., & Wang, D. (2016). Benchmarking road safety performance : Identifying a meaningful reference (best-in-class). Accident Analysis & Prevention, 86, 76-89. https://doi.org/10.1016/j.aap.2015.10.018
  • 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

Year 2023, Volume: 22, 99 - 110, 01.09.2023

Ibtissam El Khalaı Zoubida Chorfı Abdelaziz Berrado

https://doi.org/10.55549/epstem.1337640

Abstract

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.

Keywords

Road safety Management system Road safety strategy Performance monitoring Road safety measures.

References

  • 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, F., Wu, J., Chen, X., Wang, J., & Wang, D. (2016). Benchmarking road safety performance : Identifying a meaningful reference (best-in-class). Accident Analysis & Prevention, 86, 76-89. https://doi.org/10.1016/j.aap.2015.10.018
  • 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
There are 19 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Articles
Authors

Ibtissam El Khalaı

Zoubida Chorfı

Abdelaziz Berrado

Early Pub Date August 4, 2023
Publication Date September 1, 2023
Published in Issue Year 2023 Volume: 22

Cite

APA 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
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