A Novel Heuristic Approach for Simplifying Urban Risk Zones in Distribution Route Planning
Keywords:
Vehicle Routing Problem, Urban Risk, Spatial Clustering, Geometric Heuristics, Risk-Aware Logistics.Abstract
Urban logistics operations are increasingly affected by traffic accidents and criminal incidents that may compromise transportation efficiency and operational safety. This study proposes a geometric heuristic framework for incorporating urban risk structures into a Capacitated Vehicle Routing Problem (CVRP). The methodology combines georeferenced accident and robbery records, spatial clustering, overlap analysis, transformation of irregular conflict regions into circular risk zones, and a secant-based mechanism for evaluating route exposure to risk. The resulting zones are integrated into the routing model through a multiplicative penalty structure that balances transportation cost and risk avoidance. The framework was evaluated using real-world data from Irapuato, Mexico, including accident and crime records collected between 2020 and 2025 and a distribution network of 97 customers. Results show that the proposed methodology successfully identifies urban conflict structures and incorporates them into routing decisions. Sensitivity analysis revealed a clear trade-off between routing efficiency and risk exposure, reducing conflict-zone crossings from six to zero with a moderate increase in routing cost. The findings demonstrate the practical applicability of the proposed geometric heuristic for risk-aware urban logistics planning.
References
Sar, K., & Ghadimi, P. (2023). A systematic literature review of the vehicle routing problem in reverse logistics operations. Computers & Industrial Engineering, 177, 109011. https://doi.org/10.1016/j.cie.2023.109011
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. In Management Science (Vol. 6, Issue 1, pp. 80–91). Institute for Operations Research and the Management Sciences (INFORMS). https://doi.org/10.1287/mnsc.6.1.80
Tang, J., Yu, Y., & Li, J. (2015). An exact algorithm for the multi-trip vehicle routing and scheduling problem of pickup and delivery of customers to the airport. https://doi.org/10.1016/j.tre.2014.11.001
Vidal, T., Battarra, M., Subramanian, A., & Erdogan, G. (2015). Hybrid metaheuristics for the clustered vehicle routing problem. Computers & Operations Research, 58, 87–99. http://dx.doi.org/10.1016/j.cor.2014.10.019.
Güner, A. R., Murat, A., & Chinnam, R. B. (2017). Dynamic routing for milk-run tours with time windows in stochastic time-dependent networks. Transportation Research Part E: Logistics and Transportation Review, 97, 251–267. https://doi:10.1016/j.tre.2016.10.014
Torres, F., Gendreau, M., & Rei, W. (2022). Crowdshipping: An open VRP variant with stochastic destinations. Transportation Research Part C Emerging Technologies, 140, 103677. https://doi.org/10.1016/j.trc.2022.103677
Wen, L., & Eglese, R. (2014). Minimum cost VRP with time-dependent speed data and congestion charge. Computers & Operations Research, 56, 41–50. https://doi.org/10.1016/j.cor.2014.10.007
Xiong, Y., & Li, X. (2024). The dynamic mechanism of tentative governance for emerging technologies: A case study of China’s new energy vehicle subsidy. In Journal of Cleaner Production (Vol. 484, p. 144328). Elsevier BV. https://doi.org/10.1016/j.jclepro.2024.144328
Haarstad, H., Rosales, R., & Shrestha, S. (2024). Freight logistics and the city. Urban Studies. Advance online publication. https://doi.org/10.1177/00420980231177265
Yang, Y., Chen, Y., Bai, Y., Liu, J., & Cheng, S. (2025). Mobility data uncover spillover impacts of freight activities on urban neighborhoods. Transportation Research Part D: Transport and Environment, 138, 103902. https://doi.org/10.1016/j.scs.2025.107032
Zhao, P., Li, Z., He, Z. et al. Reducing the road freight emissions through integrated strategy in the port cities. Nat Commun 16, 2563 (2025). https://doi.org/10.1038/s41467-025-57861-z
Espadaler-Clapés, J., Barmpounakis, E., & Geroliminis, N. (2023). Traffic congestion and noise emissions with detailed vehicle trajectories from UAVs. Transportation Research Part D: Transport and Environment, 118, 103822. https://doi.org/10.1016/j.trd.2023.103822
Fried, T., Tejada, C., Dennis-Bauer, S., Bolbaatar, O., Goodchild, A., Marshall, J. D., Olmedo, O., & García, L. (2026). Logistics of Zoning, Zoning for Logistics: Toward Healthy and Equitable Development for Urban Freight. Journal of the American Planning Association, 92(1), 15–32. https://doi.org/10.1080/01944363.2025.2515134
Ma, Y., Ampong, D.K. & Mészáros, F. Access restrictions in urban logistics: a systematic review of policy effectiveness and sustainability. Futur Bus J 11, 259 (2025). https://doi.org/10.1186/s43093-025-00675-8
Şahin, M. K., & Yaman, H. (2024). The vehicle routing problem with access restrictions. Transportation Science, 58(5), 1101–1120. https://doi.org/10.1287/trsc.2023.0261
Boriboonsomsin, K., Tanvir, S., & Barth, M. (2024). Modeling the impacts of low emission zone on route diversion and emissions of heavy-duty trucks: A Southern California case study. Transportation Research Record, 2678(1), 794–805. https://doi.org/10.1177/03611981231172747
Álvarez-Horcajo, J., Oliet-Villalba, J. M., Moreno-Saavedra, L. M., Carral, J. A., & Salcedo-Sanz, S. (2025). Optimal reassignment of green vehicles for improving last-mile delivery distribution in cities with low emissions zones. Cleaner Logistics and Supply Chain, 17, 100282. https://doi.org/10.1016/j.clscn.2025.100282
Bruglieri, M., Çatay, B., Keskin, M., Mancini, S., & Pisacane, O. (2025). The mixed-fleet vehicle routing problem with low emission zones. Transportation Research Part E: Logistics and Transportation Review, 201, Article 104230. https://doi.org/10.1016/j.tre.2025.104230
Barrera-Hernández, H., & Stevenson, M. (2025). Multilevel análisis of urban form and road safety: An application for developing-country cities. Latin American Transport Studies, 4, 100053. https://doi.org/10.1016/j.latran.2026.100053
Rodríguez, S., Corzo-Forero, J., Guerrero-Guevara, L. F., León-Prieto, C., & Rodríguez-Jaime, M. F. (2026). Profile of Bogotá: Urban dynamics, transportation, and mobility challenges in a Latin American city. Latin American Transport Studies, 4, 100059. https://doi.org/10.1016/j.latran.2026.100059
Román-Cedillo, D., & Bautista-Hernández, D. A. (2026). Transportation systems in Mexico: Challenges and trends. Latin American Transport Studies, 4, 100060. https://doi.org/10.1016/j.latran.2026.100060
Arellano-Avelar, M., Medina, M. G., Martínez-Abarca, J., & Preciado-Caballero, N. (2025). Contaminación auditiva por autobuses de tránsito rápido. Entropía y neguentropía en la metrópoli de Guadalajara, México. CG Ciudad Glocal Revista Científica Mexicana de Movilidad Urbana, Transporte y Territorio. 1. https://doi.org/10.65937/ciudadglocal.2025.4.v1.n1
Tiwari, A., Singhal, B., Aditya, A., Tiwari, A., Jain, S., Mukherjee, D., & Mukherjee, D. (2023). A unified geospatial clustering framework to identify varying density clusters in e-commerce logistics. Proceedings of the AAAI Conference on Artificial Intelligence. https://doi.org/10.1609/aaai.v40i47.41488
Zhang, M. (2022). Weighted clustering ensemble: A review. Pattern Recognition, 124, 108428. https://doi.org/10.1016/j.patcog.2021.108428
Zhang, X., Jia, Y., Song, M., & Wang, R. (2025). Similarity and dissimilarity guided co-association matrix construction for ensemble clustering. IEEE Transactions on Knowledge and Data Engineering, 37(11), 6694–6707. https://doi.org/10.1109/CEEICT.2018.8628138
Tu, X., Fu, C., Huang, A., Chen, H., & Ding, X. (2022). DBSCAN spatial clustering analysis of urban production–living–ecological space based on POI data. International Journal of Environmental Research and Public Health, 19(9), 5153. https://doi.org/10.3390/ijerph19095153
Khan, M. M. R., Siddique, M. A. B., Arif, R. B., & Oishe, M. R. (2018). ADBSCAN: Adaptive density-based spatial clustering of applications with noise for identifying clusters with varying densities. In Proceedings of the 4th IEEE International Conference on Electrical Engineering and Information & Communication Technology (iCEEiCT 2018).
