Hazmat Transportation Research Articles

Incorporating Fairness Into the Gateway‐Based Risk Mitigation Policy for Hazmat Transport

ABSTRACTIn hazardous material transport on road networks, two conflicting objectives must be addressed simultaneously: minimizing risk and minimizing cost. Risk mitigation policies may yield as a secondary outcome uneven flow distribution on the network. This study empowers an existing risk mitigation policy based on gateways (GBP) to improve fairness. According to GBP, each vehicle is obliged to traverse a compulsory node (a gateway) on its minimum cost itinerary from origin to destination. Gateways must be located on a few network nodes and assigned to vehicles to minimize total risk, yielding a bi‐level optimization problem. GBP already proved able to reduce total risk by opening just a few gateways and to a limited detriment of total cost. However, gateways may end up acting as flow concentrators, thus hampering equity. This study aims to bridge the gap between risk mitigation and fairness. To this aim, we generalize the multi‐commodity flow formulation of the problem by imposing a capacity constraint on the nodes, discuss its impact on the model structure, and experimentally investigate whether it is possible to achieve a more equitable risk distribution and how total risk and total cost are affected.

Optimizing hazardous materials transportation network: A bi-level programming approach considering road blocking

Road transportation serves as the primary mode for hazardous materials (hazmat) transportation in China. However, the current academic literature lacks sufficient exploration of optimizing transportation networks through road-blocking strategies. This research proposed a bi-level programming model, where the government acts as the decision-maker in the upper-level programming, and the transportation enterprise operates at the lower level. Road-blocking strategies are employed by the government to mitigate transportation risks. We developed exact solution algorithms for the upper programming and employed genetic algorithms for the lower model separately. Finally, using a real road network in Beijing as a case study, we showcased the effectiveness of our approach. We computed government decision schemes and enterprise transportation routes for 13 scenarios, and conducted a further discussion and analysis on scenarios with road service levels of 83% and 80.9%. The method and results presented in our study can adeptly dissect the transportation of hazmat in city areas, offering insightful perspectives and robust support for devising more streamlined management strategies.

A learning optimization for resilience enhancement of risk-informed traffic control system with hazardous materials transportation under uncertainty

A learning optimization is proposed to enhance overall resilience of interdependent traffic systems with hazardous materials (hazmat) transportation under uncertainty. To this end, three interconnected systems are proposed against traffic dynamics and congestion in a complex environment of regular traffic and hazmat carriers incurred with time-varying travel cost. In order to increase control resilience against traffic instability, a reinforcement learning hazmat network is proposed. In order to efficiently improve computation resilience against disruption of risk uncertainty, a resilience-aware learning optimization is proposed to obtain optimal solutions. In order to demonstrate computational performance of proposed approach, numerical experiments are performed at a real-world city under various kinds of traffic conditions. Computational comparisons are numerically made with stochastic and robust optimization at large-scale traffic control systems. As it reported, the proposed learning-based optimization can better greatly enhance overall traffic system resilience, control resilience and computation resilience compared to other models under mixed risk of hazmat transportation and uncertain demand.

Risk assessment of hazmat road transportation accidents before, during, and after the accident using Bayesian network

Risk exists in the whole process of before, during and after the accident of hazardous materials (hazmat) transportation, which usually lead to serious consequences. Risk assessment is a significant way to identify hidden dangers and their interactions, which can curb accidents happen. The purpose of this study is to analyze the risk factors of hazmat road transportation accidents through entire process risk assessment and propose management strategies. Considering nodes dependency, i.e. accident type and rescue time, this study applies the concept of Tree Augmented Naive Bayes (TAN) and built a hazmat road transportation Bayesian network (HRT-BN). Mutual information and model validation are carried on to better facilitate convenient inference and diagnosis of risk nodes and their interactions. Results show that in causal inference with single factors, human risk factors are more significant, which are likely to lead to leakage accident with slight consequences and short rescue time (s2). Meanwhile, with coupling effect of multiple factors, accident types become more diverse with serious consequence. In diagnostic inference, the rescue time is linked to seasonal and regional factors, e.g., the rescue time in summer is longer. Based on these analysis, targeted recommendations have been proposed at the end. This study provides a whole angle of view to assess risks in hazmat transportation, which has significant practical feasibility for risk management and planning of hazmat road transportation.

Which route is better for hazmat transport? An optimised routing index for urban networks

The safety of vehicles and citizens on urban road networks is a major subject, especially if these vehicles carry hazardous materials (hazmats). The paper focuses on developing an optimised index to identify the safest routes between any origin–destination pair within urban networks. The considered parameters include population, traffic safety, volume–capacity ratio, emergency response time, type of hazmat and sensitive and vulnerable places. Notably, this study is the first to incorporate the last two parameters and the combination of all parameters in route optimisation. The research formulates a hazardous material vehicle routing problem and proposes an algorithm to solve it. The approach is applied to a case study in Mashhad city, presenting the safest routes for transporting three types of hazmats. The findings indicate that the reduction in average risk score is comparable with the increase in average travel time; however, gain of the safest routes is lower than loss of the longer distance. The proposed method is practical and applicable to diverse urban networks, aiding in the routing and planning of any hazmat-type transportation.

Globalized robust bilevel optimization model for hazmat transport network design considering reliability

Globalized robust bilevel optimization model for hazmat transport network design considering reliability

Regulating hazardous material transportation: a scenario-based network design approach with integrated risk-mitigation mechanisms

Integrating both proactive and reactive risk-mitigation mechanisms, this research constructs a bi-level network design problem for hazardous materials (hazmats) to regulate the carriers' use of roads, such that the environmental impact is minimized. In more detail, by embedding the emergency response time into the risk assessment, the locations of hazmat response teams are determined along with toll schemes, road closures, and new road constructions. The uncertainties of demand, including differences in the number of shipments, origin/destination changes, and amount variations, are also taken into account by applying a scenario-based approach. The proposed bi-level model is first solved optimally by a single-level reformulation, and then by a three-stage heuristic method for larger instances. Through numerical experiments on a real-world road network in Nanchang, a city in China, a set of management insights are derived to facilitate policy-making in regulating hazmat transportation.

A toll-based approach for regulating hazmat transportation network considering boundedly rational route choice

This paper introduces a novel toll charge to regulate transportation of hazardous materials (hazmat), taking into account that carriers are bounded rational decision-makers. In the case of bounded rationality, the route choice behaviour of carriers is described based on the link-based perception error model. Then, a novel bi-level programming model is developed, which aims to minimise both total tolls and maximum total risk, while finding the shortest path according to certain perception errors. To effectively solve the proposed model, it is decomposed into two subproblems, and the particle swarm optimisation algorithm and K-shortest paths algorithm are designed. Furthermore, an extended model is developed to ensure risk equity between different links, where the constraints on maximum link risk are added. Numerical experiments are conducted to validate the proposed models and algorithms, demonstrating that maximum total risk can be effectively mitigated and risk equity between different links can be achieved.

Is the analysis of territorial compatibility in the vicinity of road hazmat transport routes a necessity for developing countries? A case study of Romania

The way dangerous goods are transported on roads within the European Union is regulated by international agreements that have been transposed into national legislation. Unlike European policies that focus on preventing major accidents involving dangerous substances in the industry, the component of territorial planning in areas exposed to technological hazards is not given similar attention when it comes to transport routes of dangerous goods. Since both the transport of hazardous materials and the activities of large industrial operators involve the handling of the same types of dangerous substances, they share similar associated hazards. Within this framework, a question arises as to whether decision-makers should accord greater consideration to the land use policies in the areas adjacent to transportation routes. In this study, the case of Romania was analyzed in detail, with the objective to firstly identify some particularities in how the primary road infrastructure is developed in relation to other European countries. Since the territorial compatibility near large industrial operators is established based on specific regulations at the national level, but there is no similar regulation for the transport of dangerous substances, another objective was to adapt and implement a methodology for the analysis of the territorial compatibility in the proximity of the national roads network. The proposed methodology utilizes open-source data and Geographic Information Systems (GIS) for analysis. It also involves the extensive application of existing rules on territorial compatibility for technological risks, specifically in the context of hazmat transport. The results of this study indicate that the primary road transport network in Romania has different characteristics compared to that of other countries, which determines a higher level of exposure to the hazards specific to the road transport of hazardous materials. Additionally, from an analysis of gasoline and diesel transport on national roads and the implementation of a territorial compatibility matrix, we observed instances of territorial incompatibility in the current state, particularly in areas close to the road, especially for scenarios with a relatively high accident frequency. Thus, the way future large road infrastructure development projects address risks related to dangerous goods transportation and the implementation of environmentally conscious land management strategies can contribute to society's sustainable development.

Risk Assessment Framework for Selecting the Safer Route for Hazmat Transportation Based on Accident Database and Vulnerability Models

Risk Assessment Framework for Selecting the Safer Route for Hazmat Transportation Based on Accident Database and Vulnerability Models

Time-varying road network design for urban hazmat transportation

Hazmat transportation in cities faces significant risks that may cause catastrophic losses to humans. From the perspective of the regulator, the main responsibility is to mitigate hazmat transport risk by determining the availability of road networks to hazmat carriers. Based on the time-variant population distribution, the hazmat transport risk was assessed via the total population exposure associated with the resident and variable populations at different times. We propose a risk-minimizing urban hazmat road network design model for multiple types of hazmats, considering time-varying traffic. The model was applied to a realistic case study of hazmat transportation in a densely populated urban area with complex traffic in Shanghai, China.

Robust multimodal hub network design for hazmat considering a detour strategy

Most traditional hub networks focus on ordinary cargo transportation and usually limit the number of transshipment occurrences to minimise costs. However, this limitation is not conducive to the transportation of hazmat, which often requires detours to avoid high risks. Therefore, this paper proposes a new multimodal network model considering a detour strategy and discusses the uncertainty in the network from the demand point of view. The method of robust optimisation is used to address the uncertainty in the proposed model, and the Lagrange relaxation algorithm and augmented ɛ-constraint method are used to transform and simplify the model. In addition, simulated data of hazmat transportation in the Yangtze River Delta urban agglomeration are used for a case study. The results show that under the consideration of uncertain demand, changes in the demand uncertainty degree and transit discount factor cause changes in the total cost and risk of the multimodal hub network, thus affecting the carrier’s location and route decisions; the new model can better control and mitigate risk, which is more suitable for hazmat transportation. This study provides a reference for multimodal transport carriers in hazmat transportation decision making.

A machine learning and data analytics approach for predicting evacuation and identifying contributing factors during hazardous materials incidents on railways

An emergency evacuation order might be issued in response to a railway incident involving hazardous materials (hazmat), such as the February 2023 derailment at Palestine, Ohio. Due to the potential complexity of railway incidents involving hazmat, making an accurate and timely decision regarding evacuation orders can be very challenging. An appropriate framework is required to predict the need for evacuation after railway incidents and identify the contributing factors. This study aims to develop such a framework by incorporating machine learning techniques. First, various supervised machine learning models are implemented to analyze the effect of different factors on the prediction of evacuation immediately after railway incidents. Based on the factors considered in this study, the most accurate model is identified for predicting evacuation. This model is also used to identify the most significant factors affecting evacuation. The rules leading to the evacuation are then recognized along with the underlying causes of the evacuation. Second, natural language processing and co-occurrence network analysis are employed to analyze brief descriptions of railway incidents that resulted in the need to evacuate. This allows us to construct a network of causes and contributing factors to the evacuations and demonstrate a causal relationship between them. This study provides prediction models and conclusions that can be used to reduce the risks associated with the railway transportation of hazmat and enhance the effectiveness of safety intervention measures.

Multi-depot vehicle routing programming for hazmat transportation with weight variation risk

Reasonable transportation risk models are conducive to achieving the green reform of hazardous material logistics industry. However, existing multi-depot vehicle routing programming models for hazardous material transportation may result in overemphasis on either global risk or local risk. To overcome such shortcomings, we develop two novel two-stage programming models that consider weight variety in risk measures. The ordered weighted averaging risk-based model effectively reduces both the global risk and the maximum local risk with respect to the weight distribution in the aggregation process of local risks, and the state variable weight risk-based model helps reduce the global risk and the maximum local risk based on the variable weights associated with local risk values. Furthermore, we design a constraint reduction mechanism and a variable neighbourhood search-based hybrid parallel genetic algorithm to handle the proposed models, such that they could rapidly reach the near-optimal solution using multiplication processors. Experimental investigations demonstrate that the proposed models achieve a good balance between overall risk and local risk, and proposed algorithm can obtain a satisfactory approximate solution within an acceptable time frame.

Time-dependent location-routing problem for hazmat transportation with stop en route: a case study for fossil fuels distribution

Increased hazmat transportation and its undesirable effects have governments and researchers have special focus on this problem. Various aspects of this field have been studied, and most of them have tried to reduce the total risk, travel time and costs. In this study, we assume stops in the intermediate nodes are allowed because stops en route as a short break may reduce the risk of the trip. The proposed model mainly selects the best facility to assign to customer, finds the optimal route to serve the customers, and determines schedule for arrival and departure time from nodes. Given that the proposed model is NP-hard, a NSGA-II is used to solve it. In order to evaluate the efficiency of the algorithm, a real case of fossil fuels distribution in IRAN is implemented. The computational results show that the proposed algorithm is an efficient algorithm to achieve near-optimal solutions for the medium and large-scale problems.

A principal-agent model for hazmat transportation in China with risk perception and regulatory policy

A principal-agent model for hazmat transportation in China with risk perception and regulatory policy

Proposing risk equity approaches of risk and distance based in route selection and site location for hazardous material transportation

Hazmat transportation raises the concerns of potential risks to humans and the environment due to Hazmat immediate risks. Transport authorities are dealing with the selection of the best approach to share out Hazmat transport risk called risk equity. The main purpose of this study is to develop a procedure to share out Hazmat transport risk as well as to determine the optimum site locations for fuel distribution centers. The routes are selected based on a tradeoff approach between the travelled distance and transport risk associated to the road network. In the proposed two-stage procedure, the risk-based and distance-based approaches have been mathematically formulated in which the first stage obtains the shortest paths for carrying Hazmat and the second stage determines the best site locations for its distribution centers. Following the validation of the proposed procedure by selecting an experimental road network as a case study, the results including routes selection and sites allocation revealed the better outcomes are achieved when minimizing travelled distance is the objective function and accumulated risk is defined as its constraint. Authorities, who deal with Hazmat transport risk equity, are suggested to formulate the problem by minimizing travelled distance subject to tolerated risk.

Z-trapezoidal risk assessment for multi-objective Hazmat routing model with time windows

Hazardous material (Hazmat) transportation is an inseparable section of the industry, despite its major financial and health risks. In order to optimize Hazmat transportation, a multi-objective Hazmat routing model with time windows is employed where the risk and distance are minimized. Due to the uncertainty of Hazmat transportation risk, a Z-number fuzzy approach is used to estimate the risk, in which the probability of occurrence and the severity is considered in the context of Z-information. The severity of the event includes the affected population and depends on the amount of transported Hazmat and the number of individuals affected by the explosion. To tackle the proposed model, the present paper utilizes a multi-objective hybrid genetic algorithm, the validity of which is tested by Solomon’s problems. Furthermore, the optimization of a case study concerning the Hazmat distribution in Iran is analyzed using the suggested approach to assess the efficiency of the proposed fuzzy problem in real-world applications.

Mitigating Sustainability Risks in a Road Hazardous Materials Transport Network

Many materials which are important for a sustainable society are hazardous materials (hazmat), and there is the risk of potential threats with undesirable impacts on the sustainability of transport systems. The goal of this study is the mitigation of sustainability risks associated with hazmat transport processes. Moreover, it is necessary to design reliable road hazmat transport networks based on risk mitigation. This research combines route planning of hazmat shipments with location for emergency response centers in a road hazmat transport network. In addition, a new subsidy policy is also proposed. Hazmat carriers are able to choose routes for shipments in accordance with minimum transport costs. As a game scenario, a bi-level programming model is established to describe the problem. A genetic algorithm is developed to solve the problem, and computational experiments are made to testify the effectiveness of the proposed method. The results show the reasonableness of the model and the effectiveness of the algorithm, calibrating the influence of the parameters and the optimization characteristics of certain practical scenarios. Overall, the purpose of this paper is to develop a complete optimization approach for the design of road hazmat transport networks with sustainable risk as the key innovation.

Human vulnerability modeling and risk analysis of railway transportation of hazardous materials

People who live in densely populated regions could be at risk of mishaps that occur during hazardous materials (hazmat) transportation. Population vulnerability assessment is an essential parameter for developing risk mitigation strategies to prevent negative consequences of hazmat transportation incidents. In this study, a procedure is developed to estimate the risk of hazmat railway incidents, focused on two parts. First, the most probable and the most dangerous meteorological variables (e.g., stability class, wind speed, solar radiation, etc.) are processed to simulate threat zones and prepare hazard maps using the ALOHA software. Second, sociodemographic characteristics (e.g., age, sex, education, household, income, employment, etc.) of the affected population are used to create vulnerability maps along with their weights. As risk is a function of hazard and vulnerability, risk maps are generated by superimposing the hazard and vulnerability maps using ArcGIS software. This procedure is tested in a city in Canada and confirms the significance of integrating population vulnerability in risk assessment by revealing that the areas with high hazards indicated on the hazard maps are not necessarily considered as the areas with high risk indicated on the risk maps. The risk maps generated in this study can be used to provide recommendations to improve land-use planning, enhance the safety of the people, living in higher-risk areas, and prioritize emergency response decisions. The procedure developed in this study can also be applied to different types of railway incidents and other potential incidents in the transportation industry when all the necessary information is available, such as regional meteorological conditions and the characteristics of the affected population.