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Optimizing urban transportation network reliability by analyzing road traffic accidents

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Urbanization has led to increased traffic congestion and road traffic accidents (RTAs), significantly impacting public health, urban mobility, and the efficiency of transportation systems. RTAs disrupt road transport networks, reducing their reliability and performance metrics, which are critical for economic and social activities. This study addresses these challenges by integrating statistical analysis and optimization modeling to enhance the reliability of urban transportation networks through targeted interventions. The proposed methodology builds upon the reliability model by Jovanović Dragan et al. (2011), utilizing statistical analysis of historical RTA data to evaluate transport network reliability. This assessment informs of a linear programming (LP) optimization framework designed to allocate intervention budgets effectively. The LP model incorporates road importance, defined by traffic volume, prioritizing investments on high-impact roads to mitigate RTAs and improve overall network performance. The methodology is demonstrated through a case study in Medellín, Colombia, a city facing significant congestion and high RTA rates (average 100 daily). Using geolocated accident data (2017–2019) and vehicle usage metrics, two model variations were tested: one including road importance and another without. Both models yielded efficient solutions using standard optimization solvers with minimal computational time. Findings demonstrate that the model incorporating road importance provides more targeted budget allocations, aligning better with practical priorities by focusing interventions on the busiest and least reliable road segments. This study highlights the value of combining RTA analysis and network reliability perspectives for data-driven strategic transportation planning. The approach offers actionable insights for policymakers and urban planners seeking to reduce accidents and enhance urban mobility through optimized resource allocation. Future research could expand this framework to include other disruption types (e.g., natural disasters) or validate intervention effectiveness through detailed simulation modeling.
Rocznik
Strony
155--177
Opis fizyczny
Bibliogr. 40 poz., il., tab., wykr.
Twórcy
  • Instituto Tecnológico Metropolitano, Facultad de Ciencias Económicas y Administrativas, Medellín, Colombia
  • Instituto Tecnológico Metropolitano, Facultad de Ciencias Económicas y Administrativas, Medellín, Colombia
Bibliografia
  • 1. Akopov, A.S., Beklaryan, L.A, & Beklaryan, A.L. (2021). Simulation-Based Optimisation for Autono-mous Transportation Systems Using a Parallel Real-Coded Genetic Algorithm with Scalable Nonuniform Mutation. Cybernetics and Information Technologies, 21(3), 127-144. https://doi.org/10.2478/cait-2021-0034.
  • 2. Alcadía de Medellín. (2020). Open Data Medellín. https://geomedellin-m-medellin.opendata.arcgis.com/.
  • 3. Area Metropolitana de Medellín. (2017). ENCUESTA ORIGEN DESTINO. https://www.metro-pol.gov.co/observatorio/Paginas/encuestaorigendestino.aspx.
  • 4. Bauer, M., Okraszewska, R., & Richter, M. (2021). Analysis of the causes and effects of cyclist-pedestrian accidents in biggest Polish cities. Archives of Transport, 58(2), 115-135. https://doi.org/10.5604/01.3001.0014.8970
  • 5. Bellman, R. (1957). Dynamic Programming. Princeton University Press.
  • 6. Bimpou, K., & Ferguson, N. S. (2020). Dynamic accessibility: Incorporating day-to-day travel time reliability into accessibility measurement. Journal of Transport Geography, 89, 102892. https://doi.org/10.1016/j.jtrangeo.2020.102892
  • 7. Briz-Redón, Á., Mateu, J., & Montes, F. (2021). Modeling accident risk at the road level through zero-inflated negative binomial models: A case study of multiple road networks. Spatial Statistics, 43, 100503. https://doi.org/10.1016/j.spasta.2021.100503.
  • 8. Castañeda, C. P., & Villegas, J. G. (2017). Analyzing the response to traffic accidents in Medellín, Colombia, with facility location models. IATSS Research, 41(1), 47-56. https://doi.org/10.1016/j.iatssr.2016.09.002.
  • 9. DANE. (2019). Resultados Censo Nacional de Población y Vivienda 2018 (National population census). https://www.dane.gov.co/index.php/servicios-al-ciudadano/60-espanol/demograficas/censos.
  • 10. Dantzig, G. B. (1957). Discrete-Variable Extremum Problems. Operations Research, 5(2), 266-277. https://doi.org/10.1287/opre.5.2.266.
  • 11. Figueira, A. da C., Pitombo, C. S., de Oliveira, P. T. M. e. S., & Larocca, A. P. C. (2017). Identification of rules induced through decision tree algorithm for detection of traffic accidents with victims: A study case from Brazil. Case Studies on Transport Policy, 5(2), 200-207. https://doi.org/10.1016/j.cstp.2017.02.004.
  • 12. Geneva: World Health Organization. (2018). Global status report on road safety 2018. https://www.who.int/publications/i/item/9789241565684.
  • 13. Green, C. P., Heywood, J. S., & Navarro, M. (2016). Traffic accidents and the London congestion charge. Journal of Public Economics, 133, 11-22. https://doi.org/10.1016/j.jpubeco.2015.10.005.
  • 14. Gutierrez-Osorio, C., & Pedraza, C. (2020). Modern data sources and techniques for analysis and forecast of road accidents: A review. Journal of Traffic and Transportation Engineering (English Edition), 7(4), 432-446. https://doi.org/10.1016/j.jtte.2020.05.002.
  • 15. INRIX. (2020). 2020 Global Traffic Scorecard. INRIX Research. https://inrix.com/scorecard/.
  • 16. Jain, S., & Jain, S. S. (2021). Development of Intelligent Transportation System and Its Applications for an Urban Corridor During COVID-19. Journal of The Institution of Engineers (India): Series B. https://doi.org/10.1007/s40031-021-00556-y.
  • 17. Jose, C., & Vijula Grace, K. S. (2020). Optimization based routing model for the dynamic path planning of emergency vehicles. Evolutionary Intelligence. https://doi.org/10.1007/s12065-020-00448-y.
  • 18. Jovanović Dragan, D., Bačkalić, T., & Bašić, S. (2011). The application of reliability models in traffic accident frequency analysis. Safety Science, 49(8-9), 1246-1251. https://doi.org/10.1016/j.ssci.2011.04.008.
  • 19. Kaddoura, I., & Nagel, K. (2018). Using real-world traffic incident data in transport modeling. Procedia Computer Science, 130, 880-885. https://doi.org/10.1016/j.procs.2018.04.084.
  • 20. Lam, W. H. K., Lo, H. K., & Wong, S. C. (2014). Advances in equilibrium models for analyzing transportation network reliability. Transportation Research Part B: Methodological, 66, 1-3. https://doi.org/10.1016/j.trb.2014.05.013.
  • 21. Lee, Y., Wei, C. H., & Chao, K. C. (2017). Non-parametric machine learning methods for evaluating the effects of traffic accident duration on freeways. Archives of Transport, 43(3), 91-104. https://doi.org/10.5604/01.3001.0010.4228.
  • 22. Li, T., Yang, Y., Wang, Y., Chen, C., & Yao, J. (2016). Traffic fatalities prediction based on support vector machine. Archives of Transport, 39(3), 21-30. https://doi.org/10.5604/08669546.122544.
  • 23. Martínez-Gabaldón, E., Méndez Martínez, I., & Martínez-Pérez, J. E. (2020). Estimating the impact of the Penalty Point System on road fatalities in Spain. Transport Policy, 86, 1-8. https://doi.org/10.1016/j.tranpol.2019.11.003.
  • 24. Mitsakou, C., Dimitroulopoulou, S., Heaviside, C., Katsouyanni, K., Samoli, E., Rodopoulou, S., Costa, C., Almendra, R., Santana, P., Dell’Olmo, M. M., Borell, C., Corman, D., Zengarini, N., Deboosere, P., Franke, C., Schweikart, J., Lustigova, M., Spyrou, C., de Hoogh, K., … Vardoulakis, S. (2019). Environmental public health risks in European metropolitan areas within the EURO-HEALTHY project. Science of the Total Environment, 658, 1630-1639. https://doi.org/10.1016/j.scitotenv.2018.12.130.
  • 25. Mohri, S. S., & Haghshenas, H. (2021). An ambulance location problem for covering inherently rare and random road crashes. Computers and Industrial Engineering, 151, 106937. https://doi.org/10.1016/j.cie.2020.106937.
  • 26. Muriel-Villegas, J. E., Alvarez-Uribe, K. C., Patiño-Rodríguez, C. E., & Villegas, J. G. (2016). Analysis of transportation networks subject to natural hazards - Insights from a Colombian case. Reliability Engineering and System Safety, 152, 151-165. https://doi.org/10.1016/j.ress.2016.03.006.
  • 27. Novikov, A., Shevtsova, A., & Vasilieva, V. (2020). Development of approach to reduce number of accidents caused by drivers. Transportation Research Procedia, 50, 491-498. https://doi.org/10.1016/j.trpro.2020.10.090.
  • 28. Oskarbski, J. (2017). Automatic road traffic safety management system in urban areas. MATEC Web of Conferences, 122, 03007. https://doi.org/10.1051/matecconf/201712203007.
  • 29. Pineda-Jaramillo, J., & Arbeláez-Arenas, Ó. (2021). Modelling road traffic collisions using clustered zones based on Foursquare data in Medellín. Case Studies on Transport Policy, 9(2), 958-964. https://doi.org/10.1016/j.cstp.2021.04.016.
  • 30. Ravi Sekhar, C., Madhu, E., Kanagadurai, B., & Gangopadhyay, S. (2013). Analysis of travel time reliability of an urban corridor using micro simulation techniques. Current Science, 105(3), 319-329.
  • 31. Rolison, J. J. (2020). Identifying the causes of road traffic collisions: Using police officers’ expertise to improve the reporting of contributory factors data. Accident Analysis and Prevention, 135, 105390. https://doi.org/10.1016/j.aap.2019.105390.
  • 32. Sayyadi, R., & Awasthi, A. (2020). A multi-objective optimization model for sustainable transportation network design. International Journal of Systems Science: Operations & Logistics, 7(4), 323-336. https://doi.org/10.1080/23302674.2018.1550929.
  • 33. Schoeman, I. M. (2018). Strategies to reduce traffic accident rates in developing countries Lessons learned for assessment and management. International Journal of Safety and Security Engineering, 8(1), 98-109. https://doi.org/10.2495/SAFE-V8-N1-98-109.
  • 34. Soltani-Sobh, A., Heaslip, K., & El Khoury, J. (2015). Estimation of road network reliability on resiliency: An uncertain based model. International Journal of Disaster Risk Reduction, 14, 536-544. https://doi.org/10.1016/j.ijdrr.2015.10.005.
  • 35. Soltani-Sobh, A., Heaslip, K., Scarlatos, P., & Kaisar, E. (2016). Reliability based pre-positioning of recovery centers for resilient transportation infrastructure. International Journal of Disaster Risk Reduction, 19, 324-333. https://doi.org/10.1016/j.ijdrr.2016.09.004.
  • 36. Srinivasan, D., Loo, W. H., & Cheu, R. L. (2003). Traffic incident detection using particle swarm optimization. Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706), Indianapolis, IN, USA, 144-151. https://doi.org/10.1109/SIS.2003.1202260.
  • 37. Sun, C., Pei, X., Hao, J., Wang, Y., Zhang, Z., & Wong, S. C. (2018). Role of road network features in the evaluation of incident impacts on urban traffic mobility. Transportation Research Part B: Methodological, 117, 101-116. https://doi.org/10.1016/j.trb.2018.08.013.
  • 38. Wajid, S., Nezamuddin, N., & Unnikrishnan, A. (2020). Optimizing Ambulance Locations for Coverage Enhancement of Accident Sites in South Delhi. Transportation Research Procedia, 48, 280-289. https://doi.org/10.1016/j.trpro.2020.08.022.
  • 39. Wallace, M., Kitson, C., Ormstrup, M., Cherian, J., & Saleh, J. H. (2021). Pedestrian and light transit accidents: An examination of street redesigns in Atlanta and their safety outcomes. Case Studies on Transport Policy, 9(2), 538-554. https://doi.org/10.1016/j.cstp.2021.02.009.
  • 40. Wen, H., Wu, J., Duan, Y., Qi, W., & Zhao, S. (2019). A Methodology of Timing Co-Evolutionary Path Optimization for Accident Emergency Rescue Considering Future Environmental Uncertainty. IEEE Access, 7, 131459-131472. https://doi.org/10.1109/ACCESS.2019.2940315.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-9587e28c-c0e5-4715-bdf5-f3bd82c0f564
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