Análisis de Movilidad Urbana con Datos Geoespaciales en Tiempo Real y el Modelo de Gravedad
Resumen
La movilidad urbana en el Área Metropolitana de la Ciudad de Guatemala está influenciada por el uso del suelo, la congestión y la fricción del viaje. La integración de datos en tiempo real mediante herramientas digitales como Google Maps permitió identificar patrones de desplazamiento con mayor precisión. Aplicando el modelo de gravedad, se evidenció que el tiempo de viaje y la congestión son factores más determinantes que la distancia en kilómetros. Los modelos regionales mostraron mejor ajuste que el modelo general, lo que resalta la importancia de abordar la movilidad con un enfoque diferenciado. La falta de bases de datos estructuradas dificulta la planificación del transporte, por lo que se propone combinar datos en tiempo real y trabajo de campo para mejorar la toma de decisiones y optimizar la movilidad urbana.
Descargas
Citas
Abdel-Aal, M. M. M. (2014). Calibrating a trip distribution gravity model stratified by the trip purposes for the city of Alexandria. Alexandria Engineering Journal, 53(3), 677–689.
https://doi.org/10.1016/j.aej.2014.04.006
Ali, K. S., & Abid, N. M. (2021). The Importance of Google Maps for Traffic in Calculating the Level of Service for the Road and Traffic Delay. IOP Conference Series: Materials Science and Engineering, 1076(1). https://doi.org/10.1088/1757-899x/1076/1/012015
Baji, P. (2018). Using google maps road traffic estimations to unfold spatial and temporal inequalities of urban road congestion: A pilot study from budapest. Hungarian Geographical Bulletin, 67(1), 61–74. https://doi.org/10.15201/hungeobull.67.1.5
Batista, R. D. A., & Bazzan, A. L. C. (2015). Identification of Central Points in Road Networks using Betweenness Centrality Combined with Traffic Demand. Polibits, 52, 85–91. https://doi.org/10.17562/pb-52-9
Black, W. R. (1973). An analysis of gravity model distance exponents. Transportation, 2(3), 299–312. https://doi.org/10.1007/BF00243358
Caiza, L. J., Alvarez, R., Urquiza-Aguiar, L., Calderón-Hinojosa, X., & Zambrano, A. (2018). PE-WASUN’18, October 28-November 2, 2018, Montréal, Québec, Canada. In VTM: Vehicular Traffic Monitor via Images Processing of GoogleMaps (pp. 40–46). https://doi.org/10.1145/3243046.3243055
Chen, Y. (2015). The distance-decay function of geographical gravity model: Power law or exponential law? Chaos, Solitons and Fractals, 77, 174–189. https://doi.org/10.1016/j.chaos.2015.05.022
Chen, Y., Zhang, Z., & Liang, T. (2019). Assessing urban travel patterns: An analysis of traffic analysis zone-based mobility patterns. Sustainability, 11(19), 5452. https://doi.org/10.3390/su11195452
Cheng, G., Wilmot, C. G., & Baker, E. J. (2011). Dynamic gravity model for hurricane evacuation planning. Transportation Research Record, 1(2234), 125–134. https://doi.org/10.3141/2234-14
Duran-Fernandez, R., & Santos, G. (2014). Gravity, distance, and traffic flows in Mexico. Research in Transportation Economics, 46, 30–35. https://doi.org/10.1016/j.retrec.2014.09.003
Gao, S., Wang, Y., Gao, Y., & Liu, Y. (2013). Understanding urban traffic-flow characteristics: A rethinking of betweenness centrality. Environment and Planning B: Planning and Design, 40(1), 135–153. https://doi.org/10.1068/b38141
Gomez, L., & Ramirez, M. (2019). Expansión urbana y cambios en el uso del suelo en la ciudad de Curuzú Cuatiá, Corrientes, Argentina, entre los años 1990 y 2016. Revista de Estudios Marítimos y Sociales, 14, 123–149. Retrieved from file:///C:/Users/Usuario/Downloads/2019-RESM-Gomez_Ramirez.pdf
Hansen, W. (1962). Evaluation of Gravity Model Trip Distribution Procedures. Highway Research Board Bulletin, (1), 67–76.
Haynes, K. E., & Fotheringham, A. S. (2020). Gravity and spatial interaction models. (G. I. Thrall, Ed.). SAGE Publications.
Hong, I., & Jung, W. S. (2016). Application of gravity model on the Korean urban bus network. Physica A: Statistical Mechanics and Its Applications, 462, 48–55.
https://doi.org/10.1016/j.physa.2016.06.055
Jung, W. S., Wang, F., & Stanley, H. E. (2008). Gravity model in the Korean highway. Epl (Europhysics Letters), 81(4). https://doi.org/10.1209/0295-5075/81/48005
Kazerani, A., & Winter, S. (2009). Modified Betweenness Centrality for Predicting Traffic Flow. Proceedings of the 10th International Conference on GeoComputation, Sydney, Australia, November 30-December, 2.
Masucci, A. P., Serras, J., Johansson, A., & Batty, M. (2013). Gravity versus radiation models: On the importance of scale and heterogeneity in commuting flows. Physical Review E, 88(2), 022812. https://doi.org/10.1103/PhysRevE.88.022812
Mikkonen, K., & Luoma, M. (1999). The parameters of the gravity model are changing - how and why? Journal of Transport Geography, 7(4), 277–283. https://doi.org/10.1016/S0966-6923(99)00024-1
Euan Pablo, L. L. (2025). Impacto Social y Económico de la Trata de Personas en el Estado de Quintana Roo. Ciencia Y Reflexión, 4(1), 893–912. https://doi.org/10.70747/cr.v4i1.149
Del Valle Juarez, M. B. (2025). Optimización de la productividad en Anguiplast S.A. de C.V. mediante diseño de experimentos (DOE). Ciencia Y Reflexión, 4(1), 965–995. https://doi.org/10.70747/cr.v4i1.90
Naser, I. H., Mahdi, M. B., Meqtoof, F. H., & Etih, H. A. (2021). Modelling Trip Distribution Using the Gravity Model and Fratar’s Method. Mathematical Modelling of Engineering Problems, 8(2), 230–236. https://doi.org/10.18280/mmep.080209
Perumal, K., & Bhaskaran, R. (2010). Supervised classification performance of multispectral images. arXiv preprint arXiv:1002.4046.
Pokorný, P. (2017). Determining Traffic Levels in Cities Using Google Maps. In Determining Traffic Levels in Cities Using Google Maps (pp. 144–147). https://doi.org/10.1109/MCSI.2017.33
Rezzouqi, H., Gryech, I., Sbihi, N., Ghogho, M., & Benbrahim, H. (2018). Analyzing the accuracy of historical average for urban traffic forecasting using google maps. Advances in Intelligent Systems and Computing (Vol. 868). Springer International Publishing.
https://doi.org/10.1007/978-3-030-01054-6_79
Sayed, M. A., Rahman, M. M., Zaber, M. I., & Ali, A. A. (2017). Understanding Dhaka city traffic intensity and traffic expansion using gravity model. 2017 20th International Conference of Computer and Information Technology, ICCIT, 1–6.
https://doi.org/10.1109/ICCITECHN.2017.8281770
Simini, F., Barlacchi, G., Luca, M., & Pappalardo, L. (2020). A Deep Gravity model for mobility flows generation. https://doi.org/10.1038/s41467-021-26752-4
Sornsoongnern, P., Pueboobpaphan, S., & Pueboobpaphan, R. (2023). Innovative Dynamic Queue-Length Estimation Using Google Maps Color-Code Data. Sustainability, 15(3466).
Wang, S., Sun, L., Rong, J., & Yang, Z. (2014). Transit traffic analysis zone delineating method based on Thiessen polygon. Sustainability, 6(4), 1821–1832. https://doi.org/10.3390/su6041821
Wu, Z., Huang, M., Zhao, A., & Lan, Z. (2021). Urban Traffic Planning and Traffic Flow Prediction based on ulchis gravity model and Dijkstra algorithm. Journal of Physics: Conference Series, 1972(1), 012080. https://doi.org/10.1088/1742-6596/1972/1/012080
Yousif, R. A., Tayh, S. A., & Jasim, A. F. (2019). Trip Distribution Gravity Model of Al-Diwaniyah City: A Case Study. In AWAM International Conference on Civil Engineering (pp. 105–117). https://doi.org/10.1007/978-3-030-32816-0
Derechos de autor 2025 Mayra Virginia Carvajal Castillo , José Luis Argueta Mayorga
Esta obra está bajo licencia internacional Creative Commons Reconocimiento 4.0.
.png)
.png)
1.png)
