Hazardous Materials Transportation Research Articles

Safety of Land Transport of Hazardous Materials in Poland

The article describes a topic of the transport of hazardous materials in Poland. The risks during transport are presented, as well as the regulations that must be observed when trans-porting hazardous materials. It also discusses the basic rules that apply during an emergency. The purpose of this article is to present a simulation of the effects of gas explosion. Significant factors that determine the effects of an explosion include: spatial conditions, including mainly buildings and terrain conditions. The place that was selected for the simulation of the event is a road junction located in the area of Bielany Wrocławskie (in Poland), with particular emphasis on the A4 highway, which is crossed by over 30,000 vehicles every day. As a result, based on the ALOHA program, a hazard map was created with a division into hazard zones for people who will be at the place of the incident. A conducted assessment of the effects of this event will allow to assess what pollutants and how far they will get from the event site. Weather conditions were also included in the analysis, wind direction and speed.

Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

Multi-Depot Heterogeneous Vehicle Routing Optimization for Hazardous Materials Transportation

Derailment Analysis for Prediction of Damage and Probability of Release for Novel Railroad Tank Car Designs

Over the past 15 years, there has been substantial research performed to improve the safety of tank cars carrying hazardous materials. This has included a series of full-scale tank car impact tests and the development of impact/puncture models capable of evaluating the performance of existing and novel tank car designs under specific impact conditions. In parallel, statistical information was developed on tank car performance based on data from past accidents. However, such data cannot predict the performance of novel tank car designs involving new materials and configurations. This paper presents an innovative analytical framework to address this gap by simulating railroad tank cars behaviors under various train accident and rolling stock features. The finite element method-based simulation model is developed to generate impact forces on railcars after the initiation of train derailments. The results of the modeling were compared, validated, and calibrated with the real-world train derailment to assure the practicality of the analytical tool. This research is novel as it is the first quantitative, analytical methodology that can accurately and practically assess the physical behaviors of railcar movements and impact forces in train derailments. The results of this study provide new and important information for tank car performance in train derailments, which contributes to the literature in structural modeling of rail tank cars. It also specifies an approach to building a bridge between analytical methods and real-world scenarios and data for estimating tank car conditional probability of release (a frequently used metric for hazardous materials transportation safety).

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.

Real-Time Risk Assessment for Road Transportation of Hazardous Materials Based on GRU-DNN with Multimodal Feature Embedding

In this paper, a gated recurrent unit–deep neural network (GRU-DNN) model integrated with multimodal feature embedding (MFE) is developed to evaluate the real-time risk of hazmat road transportation based on various types of data for contributing factors. MFE was incorporated into the framework of a deep learning model in which discrete variables, continuous variables, and images were uniformly embedded. GRU is a pre-trained sub-model, and the DNN is able to directly use the relative structure and weights of the GRU, improving the poor classification and recognition results due to insufficient samples. Additionally, the model is trained and validated based on hazmat road transportation database consisting of 2100 samples with 20 real-time contributing factors and four risk levels in China. The accuracy (ACC), precision (PR), recall (RE), F1-score (F1), and areas under receiver-operating-characteristic curves (AUC) of the proposed model and other commonly used models are compared as performance measurements in numerical examples. Finally, Carlini & Wagner attack and three defenses of adversarial training, dimensionality reduction and prediction similarity are proposed in the training to improve the robustness of the model, alleviating the impact of noise and error on small-sized samples. The results demonstrate that the average ACC of the model reaches 93.51% and 87.6% on the training and validation sets, respectively. The prediction of accidents resulting in injury is the most accurate, followed by fatal accidents. Combined with the RE of 89.0%, the model exhibits excellent performance. In addition, the proposed model outperforms other widely used models based on the overall comparisons of ACC, AUC, F1 and PR-RE curve. Finally, prediction similarity can be used as an effective approach for robustness improvement, with the launched adversarial attacks being detected at a high success rate.

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.

Toward a high-fidelity model for the identification of underground gas flow regimes resulting from buried pipeline releases

The quantitative characterization of underground transport phenomena remains challenging due to the complex behavior of the gas movement in soil. Conversely, this inhibits the accurate prediction of the risk arising from the underground transport of hazardous materials. This work proposed and qualitatively evaluated a computational model that spans a wide range of underground gas flow regimes, ranging from gas migration, to ground uplift, and crater formation, depending on the release characteristics. The model followed the multiphase Eulerian approach and adopted the standard k-ω turbulence model and the kinetic theory of granular flow for the ground description with the Syamlal-O’Brien granular viscosity expression. The model's optimum configuration was checked against experimental data using a new mechanistic approach to link the qualitative observations with quantitative model outputs. The effect of pipeline pressure, burial depth, and release orientation on the regime was studied and the outcomes were utilized to enhance a literature nomograph for the flow regime identification. Emphasis was given to fill in the literature gaps and improve the delineation of the boundaries between the regimes rather than deriving specific quantities. The resulted nomograph is a cost-effective screening tool to identify the regime and select among the available strategies of risk assessment.

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.

Understanding Hazardous Materials Transportation Accidents Based on Higher-Order Network Theory.

In hazardous materials transportation systems, accident causation analysis is important to transportation safety. Complex network theory can be effectively used to understand the causal factors of and their relationships within accidents. In this paper, a higher-order network method is proposed to establish a hazardous materials transportation accident causation network (HMTACN), which considers the sequences and dependences of causal factors. The HMTACN is composed of 125 first- and 118 higher-order nodes that represent causes, and 545 directed edges that denote complex relationships among causes. By analyzing topological properties, the results show that the HMTACN has the characteristics of small-world networks and displays the properties of scale-free networks. Additionally, critical causal factors and key relationships of the HMTACN are discovered. Moreover, unsafe tank or valve states are important causal factors; and leakage, roll-over, collision, and fire are most likely to trigger chain reactions. Important higher-order nodes are discovered, which can represent key relationships in the HMTACN. For example, unsafe distance and improper operation usually lead to collision and roll-over. These results of higher-order nodes cannot be found by the traditional Markov network model. This study provides a practical way to extract and construct an accident causation network from numerous accident investigation reports. It also provides insights into safety management of hazardous materials transportation.

Management of transport of hazardous materials

Transportation of hazardous goods is a demanding and risky job. The greatest risks, dangers and consequences for the population and the environment are associated with the transport of hazardous goods. Today, numerous systems and software packages have been developed, the aim of which is to improve the performance of this type of transport and mitigate the consequences of possible accidents. By monitoring and adaptive management of the transport of hazardous goods in road traffic, the consequences of traffic accidents can be repaired more quickly and the movement of vehicles with hazardous goods can be directed to safer routes.

Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment

The complicated maritime environment and multiple risk coupling pose significant challenges to hazardous material transportation safety in coastal areas. This paper studies the risk coupling problems by analyzing 362 hazardous materials accidents. Firstly, the transportation risks are divided into 6 categories and 22 risk factors are identified. Then, the definition and types of risk coupling are determined. Finally, a novel risk coupling model is constructed by using analytic hierarchy process (AHP) and N-K model. AHP–N-K model is used to calculate the coupling degree of risk factors. Furthermore, the correlation mechanism between the risk values and the risk components in complicated maritime environment is analyzed. The results show that the risk of hazardous materials transportation has a gradually increasing trend with the increase of risk coupling factors. Owing to the risk coupling of driver factors and road environmental factors, the probability of accidents is high. The coupling risk of subjective and objective risk factors is more likely to cause accidents than subjective risk factors. The coupling effect of rainy days and curves with other risk factors is very strong.

Deep learning technique for examining the mechanism, transport, and behavior of oil-related hazardous material caused by wave breaking and turbulence

A marine oil spill produces oil-related hazardous material (OHM) which can cause damage to the marine ecological environment, and seriously affect coastal economic development such as tourism and aquaculture. The turbulent momentum and energy generated by the wave breaking process have a significant effect on accelerating the mixing of OHM and seawater, which is one of the main factors in oil becoming sunken or submerged. In order to explore the influence of offshore wave breaking on the formation and transportation of OHM, the wave breaking process was simulated in a 2D laboratory flume, and the behavior process of OHM was identified and tracked in this paper. Five groups of breaking waves of different significant wave height (SWH) were set up in the experiment, and then OHM with the same density and mass was added, respectively, in order to observe the sinking process under the action of wave-induced turbulence. The results show that the turbulence intensity is closely related to the phase of the wave, the turbulence activity is violent at the wave crest, and the vertical distribution of the turbulent energy dissipation rate in the turbulent mixing zone remains basically unchanged. Under the actions of wave breaking and turbulence, the OHM’s submergence depth shows a good binomial growth trend. For SWH = 12.45 cm, the OHM stays under the water for nearly 2.32 s, and it reaches the deepest position of 0.165 m. Compared with SWH = 12.45 cm, the submergence depths for waves with SWHs of 20.61 cm, 26.81 cm, 32.32 cm, and 36.54 cm are increased by 8%, 37%, 80%, and 159%, respectively. Then, the submergence depths due to the other four waves are increased progressively, and the growth rates are 8%, 26%, 31%, 44%, respectively (compared with the same period of the previous wave).

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.

Review of Research on Road Traffic Operation Risk Prevention and Control.

Road traffic safety can be ensured by preventing and controlling the potential risks in road traffic operations. The relevant literature was systematically reviewed to identify the research context and status quo in the road traffic operation risk prevention and control field and identify the key study contents needing further research. As research material, the related English and Chinese literature published between 1996 and 2021 (as of 31st December 2021) was obtained through the Web of Science Core Collection and Chinese Science Citation Database. These research materials include 22,403 English and 7876 Chinese papers. Based on the bibliometrics, this study used CiteSpace software to conduct keyword co-occurrence analysis in the field. The results show that the relevant research topics mainly covered the risks of drivers, vehicles, roads, and the traffic environment. In the aspect of driver risks, the studies focused on driving behavior characteristics. In terms of vehicle risks, the related studies were mainly about the vehicle control system, driving assistance system, hazardous material transportation, automated driving technology, safe driving speed, and vehicle collision prediction. For the road risks, the safe driving guarantee of high-risk road sections, driving risks at intersections, and safe road alignment design were the three study hotspots. In terms of traffic environment risks, identifying traffic risk locations and driving safety guarantees under adverse weather conditions were the two main research highlights. Moreover, mathematical modeling was the main method for studying road traffic operation risk. Furthermore, the impact of environmental factors on drivers, the emergency rescue system for road traffic accidents, the connection between automated driving technology and safe driving theory, and the man–machine hybrid traffic flow characteristics are the subjects needing further research.

Safe route-finding: A review of literature and future directions

While road navigation systems seek to determine the shortest routes between a given set of origin and destination points, there are certain situations in which the fastest route increases the risk of being involved in road crashes. This implies the necessity of integrating safe route-finding into road navigation systems. This study is designed to synthesize the literature on safe route-finding and identify the gaps in the literature for future research. Specifically, a scoping literature review methodology is applied to understand how safety is incorporated in route-finding, even beyond motor vehicle navigation systems. Three databases (Scopus, Web of Science, and IEEE Xplore) are explored, and controlling for inclusion criteria, 40 studies are included in this review. The findings of this review indicated five areas through which safety was considered in route-finding: motor vehicle navigation, public safety, public health, pedestrian and cyclist navigation, and hazardous material transportation. The measurement of safety was found challenging with inconsistencies in safety quantification approaches. The safe route-finding algorithms were investigated based on their predictive/reactive, static/dynamic, and centralized/decentralized characteristics. Based on the critical review of the safe route-finding algorithms, availability of real-time data sources, accurate real-time and disaggregated crash risk prediction models, trade-off between time and safety in road navigation tools, and centralized safe route-finding are highlighted as the requirements and challenges in considering safety in road navigation systems. This study outlines a research agenda to address the identified challenges in safe route-finding.

Cost and Risk Prediction in Road Transportation of Hazmat by ANFIS Trained with PSO, FA, HBBO and ICA

This paper proposes adaptive neuro-fuzzy inference system (ANFIS) to predict the risk with its aggregated cost (CR) of an accident in road transportation of hazardous material, the aim is to provide a more accurate and reliable data for the safety of transportation. The determination risk index by the conventional methods such as Risk graphs and deterministic approaches may lead to imprecise values due to the uncertainties, in both parameters and models. The proposed technique is a hybrid schema, which combines the main advantageous of fuzzy logic (address uncertainties) and neural network (learn from a given data). In other hand our study seeks to tune the parameters of the proposed model by particle swarm optimization (PSO), firefly algorithm (FA), imperialist competitive algorithm (ICA) and human based-behavior optimization (HBBO) and hence optimize the performance of ANFIS. The simulation result of this work and the comparative analysis shows that ANFIS yield height performance and the ANFIS-PSO was the outstanding one in the training phase, while ANFIS-FA gives better results in the testing process.

Risk assessment of hazardous materials transportation: A review of research progress in the last thirty years

In recent years, hazardous materials transportation accidents occur frequently, causing huge casualties and property losses. Risk assessment of hazardous materials transportation has been constantly investigated by researchers and decision-makers. In order to study the research progress of risk assessment of hazardous materials transportation, so as to better reveal relevant problems. This review systematically analyzes the existing literature, from 1991 to 2020, on risk assessment models and methods of hazardous materials transportation, and segregates the relevant studies based on unimodal and intermodal transportation systems. Additionally, an example is considered to analyze the characteristics of each risk assessment model of unimodal transportation, and a novel detailed classification is proposed for the risk assessment problems. The results indicate that the research on the risk assessment model of road transportation of hazardous materials is comprehensive, whereas that based on the railway and intermodal transportations is deficient. In comparison with the traditional risk assessment model, the conditional value-at-risk model presents a flexible decision-making framework for decision-makers and controls risk preference between risk neutrality and risk aversion. Owing to the difficulty in achieving door-to-door railway transportation and the increased risks associated with the intermodal transportation, the conclusion that the risk of railway and multimodal transportation of hazardous materials is lower than that of road transportation is not universal. Transport operators and governments can combine the advantages of different transportation modes, such as low risk, low cost, high flexibility, and high reliability, to achieve public safety and improve the competence of the system. Based on the proposed classification, this review identifies the existing trends and research gaps and summarizes future research directions. • Development process of risk assessment models is summarized. • Advantages and disadvantages of various transportation modes are outlined. • Characteristics of the unimodal transportation risk assessment model are analyzed using an example. • New classification for unimodal transportation is proposed.

Building proactive organizational resilience against the risk of major process safety incidents

AbstractMajor process safety incidents remain a key challenge to all industries involved in the handling, processing, manufacturing and transport of hazardous materials, including hydrocarbons. Over the past decade, there have been a number of incidents that have led to significant changes in the manner in which major industrial incidents are perceived and managed. Many companies adopt a traditional risk‐based approach when managing hazards. Larger organizations, such as oil and gas and petrochemical companies, can benefit from a proactive enterprise risk management (ERM) approach to build organizational resilience against potential threats to the business. While general risk management practices are focused on facility/asset level risks, the ERM approach offers a broader perspective when evaluating risks faced by businesses. The approach is flexible, dynamic, and cyclic in nature and provides a structured solution to the constantly evolving business risk environment. The results are intended to provide company executives with “a boardroom preview” of the risks faced and managed by the organization. This paper highlights the practical application of a six‐step ERM framework when assessing, analyzing, and recommending mitigation measures to bridge gaps related to managing major process safety risks. In addition, a structured methodology for the development and utilization of key risk indicators intended for continuous monitoring of process safety risks at a corporate level will be shared. Lessons learned from conducting this exercise will be discussed with practical advice on how to overcome commonly faced barriers.

Finding [formula omitted] dissimilar paths: Single-commodity and discretized flow formulations

While finding a path between two nodes is the basis for several applications, the need for alternative paths also may have various motivations. For instance, it can be of interest to ensure reliability in a telecommunications network, to reduce the consequences of possible accidents in the transportation of hazardous materials or to decrease the risk of robberies in money distribution. All these applications involve the search for a set of paths that are as dissimilar as possible, with respect to the arcs in them. In this work, we present linear integer programming formulations that intend to find K dissimilar paths. The new models derive from a straightforward single-commodity flow formulation of the K paths problem, to which extra constraints are added to account for the concept of dissimilarity. In particular, in each formulation a different measure of the similarity between the K paths is minimized: the number of repeated arcs, the number of arc reuses and the number of pairwise arc overlaps. The third measure originates a non-linear model that is linearized through a discretized model reformulation technique. These formulations are complemented by a polynomial algorithm that allows to extract K loopless paths from the solutions output by each of them.The formulations are tested for random and grid networks with respect to the solutions’ average and minimum dissimilarity and the run time. Two of them stand out in terms of both those measures, when compared to an iterative approach from the literature. The best one regarding the average dissimilarity finds 20 paths with average dissimilarity of 99.5% in about 168 s for random networks of 5000 nodes and 100000 arcs. The same approach finds 20 paths for grid instances of size 100 × 100 in less than 18 s. The other formulation requires about 11 s for the same random instances, with a 0.7% deviation from the best known average dissimilarity; while it requires less than 7 s to compute 20 paths in 100 × 100 grids with a 0.6% deviation to the optimal dissimilarity.

Risk parity: An alternative formulation for risk-averse stochastic optimization in presence of heavy-tailed distribution of losses

The concept of Risk Parity (or Equal Risk Contribution), which has been widely used in financial portfolio management, aims at explicitly enforcing diversification in a portfolio by ensuring equal contribution from each asset to the total volatility. While the Risk Parity condition has a straightforward use case in finance, several other application areas can be found in engineering and operations research. In these settings, the Risk Parity condition can be interpreted as enforcing the fairness of a decision or as a way to balance between a number of candidate solutions. In this paper, we consider Risk Parity in conjunction with modern risk-averse stochastic optimization (namely coherent measures of risk), study a generalized Risk Parity model, and propose a combined two-stage diversification-risk framework. We also introduce a bi-level formulation in a case when hierarchical decision-making is enforced. An approach to reformulate the Risk Parity problem as second-order cone programming is also proposed. We assess the performance of the proposed models based on a case study in hazardous materials transportation. The results show their effectiveness in terms of fairness and risk equity for decision-making under uncertainty with heavy-tailed distribution of losses.