Multi‑Objective Hazardous Materials Routing in Urban Environments Using Hybrid Algorithms and GIS‑Based Spatial Analysis

The transportation of hazardous materials within densely populated urban environments represents one of the most complex and high risk challenges in modern transportation planning and public safety management. This complexity arises from the severe human, environmental, and infrastructural consequences that may result from accidental releases, leaks, or explosions involving hazardous cargo. A fundamental trade-off lies at the core of this problem: although minimizing travel distance is typically desirable for operational efficiency and cost reduction, doing so in metropolitan areas often increases exposure to vulnerable populations and sensitive urban facilities. Consequently, the shortest route is frequently the most hazardous one, making it essential to achieve an optimal balance between minimizing distance and reducing risk.

To address this challenge, the present study develops a multi-objective optimization framework aimed at identifying routing alternatives that simultaneously minimize travel distance and reduce risk imposed on the urban environment. The framework integrates advanced spatial analysis techniques within a Geographic Information System (GIS) to construct a detailed representation of urban vulnerability. Three concentric hazard impact zones are defined around the transportation network, each weighted according to the sensitivity of surrounding land uses. This spatially explicit approach enables a more accurate estimation of the risk imposed on critical urban facilities and densely populated areas, thereby capturing the complex interaction between route selection, urban form, and population exposure.

To solve the resulting large-scale optimization problem, an enhanced evolutionary algorithm based on the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is proposed, termed DR NSGA II. This algorithm incorporates several methodological innovations tailored to the unique challenges of hazardous materials routing in complex urban networks. These include hybridized path-generation operators, structural correction mechanisms to ensure route continuity and feasibility, and diversity-preserving strategies to avoid premature convergence. The algorithm is specifically designed to navigate the vast combinatorial search space associated with routing problems while maintaining the ability to generate topologically valid solutions that respect the physical constraints of the transportation network. The structural refinement procedures embedded within the algorithm play a crucial role in eliminating redundant loops, correcting infeasible segments, and ensuring that each candidate solution represents a continuous and realistic path.

The proposed framework is applied to the transportation network of Tehran, a megacity characterized by high structural complexity, heterogeneous land-use patterns, and significant population density. The empirical results demonstrate that relying solely on the shortest-path criterion substantially increases the risk imposed on both the environment and urban residents. In contrast, the Pareto-optimal solutions generated by the DR NSGA II algorithm show that a modest increase of only 3% in travel distance can lead to a remarkable 64% reduction in overall risk. This finding highlights the importance of adopting multi-objective optimization approaches rather than traditional single-objective routing strategies, particularly in densely populated metropolitan settings where safety considerations are paramount.

Furthermore, integrating the optimization results with GIS provides decision-makers with a powerful visual and analytical tool for evaluating trade-offs between distance and risk. The resulting set of Pareto-efficient solutions offers flexibility for policymakers, enabling them to prioritize different objectives depending on operational needs and safety requirements. Overall, the proposed methodology offers a robust and scalable framework for enhancing the safety and sustainability of hazardous materials transportation in urban environments.