Identificación de las variables que influyen en el origen de los puntos de congestión de tráfico
DOI:
https://doi.org/10.17533/udea.redin.20250261Palabras clave:
congestión urbana, patrones de tráfico, cuellos de botella en la movilidad urbanaResumen
La identificación de las variables que generan puntos de congestión vehicular en las ciudades se ha revelado como una tarea extraordinariamente compleja, que implica actividades de caracterización, predicción y proyección de los puntos de congestión vehicular. La revisión exhaustiva de la literatura indica que los factores clave que influyen en la congestión vehicular urbana incluyen los incidentes de tráfico, las condiciones de la infraestructura vial, el día de la semana, la hora del día, el mes, las semanas laborales y los períodos vacacionales. Sin embargo, existe una falta de representaciones cuantitativas del tráfico que permitan evaluar con precisión el grado de influencia de variables relevantes, como los incidentes y las ubicaciones de los servicios, en la generación de puntos de congestión vehicular. En este contexto, el objetivo principal de este trabajo de investigación se centra en identificar las variables más significativas que causan puntos de congestión vehicular mediante el uso de técnicas matemáticas. Esto busca contribuir al desarrollo de modelos para mitigar la congestión vehicular. En este trabajo se utiliza el estudio de caso del tráfico, los incidentes y los servicios en la Ciudad de México.
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Kardani-Yazd, N., Kardani-Yazd, N., and Mansouri Daneshvar, M. R. “A rapid method for evaluating the variables affecting traffic flow in a touristic road, Iran”. Environmental Systems Research., vol. 8, no. 34, 2019. [Online]. Available: https://doi.org/10.1186/s40068-019-0162-0
Afrin, T., and Yodo, N. “A survey of road traffic congestion measures towards a sustainable and resilient transportation system”. Sustainability, vol. 12, no. 11, 4660, 2020. [Online]. Available: https://doi.org/10.3390/su12114660
Jia, X. “Analysis on influencing factors of traffic congestion in Chongqing and study on countermeasures: Empirical analysis based on principal component analysis”. Atlantis Press International BV, pp. 814–822, 2023. [Online]. Available: https://doi.org/10.2991/978-94-6463-200-2_84
Iro, S., and Pat-Mbano, E. C. “Causes of traffic congestion; A study of owerri municipal area of IMO state”. American Journal of Environmental Sciences, vol. 18, no. 3, pp. 52–60, 2022. [Online]. Available: https://doi.org/10.3844/ajessp.2022.52.60
Chen, L., Shi, J., Cheng, M., Zhu, H., and Sun, L. “Characteristics of urban road non-recurrent traffic congestion based on floating car data”, in 4th International Conference on Electronic Information Technology and Computer Engineering. 2020, pp. 120-126. [Online]. Available: https://doi.org/10.1145/3443467.3443740
Bian, C., Yuan, C., Kuang, W., and Wu, D. “Evaluation, classification, and influential factors analysis of traffic congestion in Chinese cities using the online map data”. Mathematical Problems in Engineering, pp. 1–10, 2016. [Online]. Available: https://doi.org/10.1155/2016/1693729
Mahona, J., Mhilu, C., Kihedu, J., and Bwire, H. “Factors contributing to traffic flow congestion in heterogenous traffic conditions”. International Journal for Traffic and Transport Engineering, vol. 9, no. 2, pp. 238–254, 2019. [Online]. Available: https://doi.org/10.7708/ijtte.2019.9(2).09
Yu, J., Wang, L., and Gong, X. “Study on the status evaluation of urban road intersections traffic congestion base on AHP-TOPSIS modal”. Procedia, Social and Behavioral Sciences, vol. 96, pp. 609–616, 2013. [Online]. Available: https://doi.org/10.1016/j.sbspro.2013.08.071
Gullotta, G., Loret, E., Stewart, C., and Sarti, F. “Traffic attractors and congestion in the urban context, the case of the city of Rome”. Journal of Geographic Information System, vol. 12, no. 06, pp. 545–559, 2020. [Online]. Available: https://doi.org/10.4236/jgis.2020.126032
Rahman, M. M., Najaf, P., Fields, M. G., and Thill, J.-C. “Traffic congestion and its urban scale factors: Empirical evidence from American urban areas”. International Journal of Sustainable Transportation, vol. 16, no. 5, pp. 406–421, 2022. [Online]. Available: https://doi.org/10.1080/15568318.2021.1885085
Pi, M., Yeon, H., Son, H., and Jang, Y. “Visual Cause Analytics for Traffic Congestion”. IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 3, pp. 2186–2201, 2021. [Online]. Available: https://doi.org/10.1109/tvcg.2019.2940580
Yue, W., Li, C., Chen, Y., Duan, P., and Mao, G. “What is the root cause of congestion in urban traffic networks: Road infrastructure or signal control?”. IEEE Transactions on Intelligent Transportation Systems: A Publication of the IEEE Intelligent Transportation Systems Council, vol. 23, no. 7, pp. 8662–8679, 2022. [Online]. Available: https://doi.org/10.1109/tits.2021.3085021
Hüseyin, G., Youssef, K., Gazi, T. “Evaluation of Traffic Congestion and Level of Service at Major Intersections in Lefkoşa, Northern Cyprus”. International Journal of Innovative Technology and Exploring Engineering, vol. 8, no. 8, pp. 2150-2155, 2019.
Mohmad, A., Sukhdeep, S.” Congestion modelling and level of service assesment of urban roads in
developing countries”. Journal of Emerging Technologies and Innovative Research, vol. 7, no. 8, pp. 2230- 2240, 2020.
Ashhad Verdezoto, T. Z., Cabrera, F., and Roa Medina, O. B. “Analysis of vehicular congestion for the improvement of the main road in Guayaquil-Ecuador.” Gaceta Técnica, vol. 21, no. 2, pp. 4–23, 2020
Xu, D., Wang, Y., Peng, P., Lin, L., and Liu, Y.” The evaluation of the urban road network based on the complex network”. IEEE Intelligent Transportation Systems Magazine, vol. 14, no. 3, pp. 200–211, 2022. [Online]. Available: https://doi.org/10.1109/mits.2021.3049351
Badveeti, A., Mir, M. S., and Badweeti, K. “The evaluation of traffic congestion analysis for the Srinagar city under mixed traffic conditions”. In: Recent Advances in Traffic Engineering, vol. 69, pp. 85–98, 2020. [Online]. Available: https://doi.org/10.1007/978-981-15-3742-4_6
Lin, P., Weng, J., Yin, B., and Zhou, X. “Urban road network operation quality evaluation method based on high-frequency trajectory data,” in 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA, 2021, pp. 3602-3607.
Aarón, M. A., Gómez, C. A., Fontalvo, J., and Gómez, A. J. “Analysis of Vehicle Mobility in the Department of La Guajira using Simulation: The Case of Riohacha and Maicao.” CIT Información Tecnológica, vol. 30, no. 1, pp. 321–332, 2019. [Online]. Available: https://doi.org/10.4067/s0718-07642019000100321
Vázquez, R., Torres, C., and Mariguetti, J. O. “Data Fusion and Analysis for Decision Making in a Complex Vehicle Scenario.” Extensionismo, Innovación y Transferencia Tecnológica, vol. 7, no. 1, pp. 196-205, 2021. [Online]. Available: https://doi.org/10.30972/eitt.704777
Zhang, K., Chu, Z., Xing, J., Zhang, H., and Cheng, Q. “Urban traffic flow congestion prediction based on a data-driven model”. Mathematics, vol. 11, no. 19, 2023. [Online]. Available: https://doi.org/10.3390/math11194075
Tu, Y., Lin, S., Qiao, J., and Liu, B. “Deep traffic congestion prediction model based on road segment grouping”. Applied Intelligence, vol. 51, no. 11, pp. 8519–8541, 2021. [Online]. Available: https://doi.org/10.1007/s10489-020-02152-x
Cvetek, D., Muštra, M., Jelušić, N., and Tišljarić, L. “A survey of methods and technologies for congestion estimation based on multisource data fusion”. Applied Sciences (Basel, Switzerland), vol. 11, no. 5, pp. 1-19, 2021. [Online]. Available: https://doi.org/10.3390/app11052306
Diaz, C., Beltran, K., Diaz, C., and Baena, A. “Traffic flow indicators analysis to determine causes of vehicular congestion”. ParadigmPlus, vol. 2, no. 2, pp. 1–16, 2021. [Online]. Available: https://doi.org/10.55969/paradigmplus.v2n2a1
Wang, W.-X., Guo, R.-J., and Yu, J. “Research on road traffic congestion index based on comprehensive parameters: Taking Dalian city as an example”. Advances in Mechanical Engineering, vol. 10, no. 6, pp. 1-8, 2018. [Online]. Available: https://doi.org/10.1177/1687814018781482
Nguyen, D.-B., Dow, C.-R., and Hwang, S.-F. “An efficient traffic congestion monitoring system on Internet of Vehicles”. Wireless Communications and Mobile Computing, pp.1–17, 2018. [Online]. Available: https://doi.org/10.1155/2018/9136813
B. Pishue, “2022 INRIX Global Traffic Scorecard,” INRIX., Kirkland, USA, Tech. Rep.-, January. 2023.
TomTom Traffic Index, Mexico City Traffic Report, 2024. [Online]. Available: https://www.tomtom.com/traffic-index/mexico-city-traffic/. Accessed on: Jan. 29, 2024.
Here Maps, Here technologies documentation, 2024. [Online]. Available: https://www.here.com/docs/bundle/traffic-api-developer-guide-v6/page/topics/what-is.html. Accessed on: Jan. 29, 2024.
Here Maps. Developer Guide Routing, 2024. [Online]. Available: https://www.here.com/docs/bundle/sdk-for-ios-navigate-developer-guide/page/topics/routing.html. Accessed on: Jan. 30, 2024.
Here Maps. Incidents 6.2, 2024. [Online]. Available: https://www.here.com/docs/bundle/traffic-api-developer-guide-v6/page/topics/resource-parameters-incidents.html. Accessed on: Jan. 30, 2024.
Geoapify, APIs and components. 2018. [Online]. Available: https://www.geoapify.com/ . Accessed on: Jan. 30, 2024.
Mónica, M. 'Use of Spearman's Correlation in a Physiotherapy Intervention Study.' Movimiento Científico, vol. 8, no. 1, pp. 98-104, 2014. [Online]. Available: https://doi.org/10.33881/2011-7191.mct.08111
R Core Team. (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL: https://www.R-project.org/
De la Cruz-Nicolás, E., Martínez-Rebollar, A., Estrada-Esquivel, H., Pliego-Martínez, O.A, “Methodology to Obtain Traffic Data and Road Incidents Through Maps Applications”, In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-Cities 2023. Communications in Computer and Information Science, 2023, vol 1938, pp. 3-17.
Here Maps, Find Traffic Along a Route, 2024. [Online]. Available: https://developer.here.com/documentation/ios-sdk navigate/4.14.4.0/dev_guide/topics/routing.html#find-traffic-along-a-route . Accessed on: Feb. 26, 2024.
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