Hazardous Materials Transportation Research Articles

A dynamic domino effect risk analysis model for rail transport of hazardous material

Rail transport of hazardous material (RTHM) plays a vital role in the supply chain of raw materials and products. However, RTHM can pose severe risks due to the large quantities of flammable and explosive chemicals transported over rail tracks crossing residential and industrial areas and possible human and technical failures. Among the potential safety issues, the domino effect is one of the most feared events, which can have devastating consequences despite its relatively low probability. As the first study, the present investigation develops a dynamic risk analysis model for analyzing domino effects in RTHM based on Dynamic Bayesian Network. Accident scenarios such as pool fire, flash fire, fire ball, vapor cloud explosion, and BLEVE are considered to analyze domino effects. The model performance is tested on a real RTHM (i.e., gasoline transportation), demonstrating the effectiveness of the proposed model in simulating the domino-driven effects in terms of both consequences and probability escalation and in dealing with the parameter and model uncertainties.

Risk Assessment of Hazmat Road Transportation Considering Environmental Risk under Time-Varying Conditions.

Hazardous materials shipments are integral to the development of industrial countries. Significant casualties and severe environmental pollution quickly ensue when accidents occur. Currently, relevant research on risk assessment of hazardous materials’ road transportation remains limited when both the population exposure risk and environmental risk are considered, especially in regard to analyzing the differences of accident impacts in different populations and environments. This paper adopts a Gaussian plume model to simulate dynamic areas at three levels of population exposure and assesses the pollution scope of air, groundwater, lakes, and rivers with a variety of diffusion models. Then, we utilize various costs to analyze the differences of accident impacts in population exposure and environmental pollution. Finally, a risk assessment model of hazardous materials road transportation under time-varying conditions is presented by considering the bearing capacity of the assessed area. Furthermore, this model is applied to a case study involving a risk assessment of hazardous materials transportation of a highly populated metropolitan area of Shanghai, China. The resulting analyses reveal that the safety of hazardous materials transportation could be effectively improved by controlling certain model parameters and avoiding road segments with a high risk of catastrophic accident consequences.

A time-dependent location-routing problem of hazardous material transportation with edge unavailability and time window

The transportation of hazardous materials (hazmat) is an inseparable component of any industrial society, where truck transportation is the most applied transportation solution for hazmat due to its versatility and flexibility, especially for short-distance and direct shipments. This paper explores the application of robust optimization in hazmat location-routing problems with edge unavailability, time-dependent parameters, and delivery time window. To be specific, random disruptions are formulated as a scenario-based robust optimization model, which is integrated with a vehicle routing problem with time windows, and then solved by an augmented epsilon constraint method. In applying the robust optimization, variabilities in cost and risk functions are introduced as critical indicators for designing robust and reliable transportation plans. In the end, the model is applied to a real-world hazmat transportation network, from which practical insights are derived. The resulting assessments shed light on the trade-off between hazmat risk versus cost, and introduce several useful indicators to facilitate the carrier in making appropriate decisions.

Research on Route Optimization of Hazardous Materials Transportation Considering Risk Equity

The consequences of a hazmat accident can be catastrophic due to the characteristics of hazardous materials. Different from the models, which are constructed from the perspective of “government-carrier”, this paper considers the three objectives of the risk, the cost, and the compensation cost from the “government-carrier-public” perspective, so as to construct a route optimization model of hazmat transportation considering risk equity. Moreover, considering that the difference in regional emergency response time will significantly affect the risk, this research incorporates the emergency response time into the transportation risk assessment function, and realizes risk equity by minimizing the total compensation cost based on the difference in regional emergency response time. To solve the proposed model, a multi-objective genetic algorithm based on linear weighting is designed. The results obtained from the case study verify the necessity of considering the risk equity in the route optimization model of hazardous materials transportation and prove that the established model and algorithm can find an optimal route that meets the expectations of the government, the carrier, and the public.

A subsidy policy to managing hazmat risk in railroad transportation network

Hazardous materials (hazmat) shipments are integral to any modern society, and railroad is the primary transportation mode in North America. Given the catastrophic nature of rail hazmat incidents, every effort should be made to mitigate risk. This study investigates a novel risk mitigation tool, i.e., subsidy, to induce railroad operators to take alternate routes that are away from high-risk portions of the network such as population centers. To this end, a bi-level optimization program is developed that captures both the risk perspective of the leader (i.e., government) and the cost concerns of the follower (i.e., railroad operator). Model complexity necessitated the development of a customized solution technique, and the resulting methodology was used to study problem instances generated on the realistic infrastructure of a railroad operator in Midwest United States. Analyses underscore the significance of using subsidy as a risk mitigation tool and in ensuring a fairer distribution of risk, in demonstrating how significant improvement in risk can be achieved by offering modest subsidy, and in defining the threshold level of the subsidy budget beyond which no improvement should be expected.

Deep learning and mobile control system for hazardous materials transportation

Artificial intelligence is a powerful tool to learn and predict traffic effects according to drivers' behavior and make effective predictions to support traffic management team. This paper presents a proposal using reinforcement deep learning, simulation, and performance analysis of road systems with improvement in hazardous materials transportation control. The analysis reports the reduction of accident detection time and damages caused by traffic jams. The use of smartphone sensors, artificial intelligence, and an integrated control system for tracking, management, monitoring, and control of hazardous materials transportation allows for the reduction in detection time.

Hub network design for hazardous-materials transportation under uncertainty

This paper considers a hazardous materials (hazmat) transportation network design problem based on hub location topology under uncertainty. The control variables include decisions regarding location of hubs and hazmat response teams, along with the routing schemes. The model aims to minimize the total risk in the network. The measure of risk incorporates average response time to hazmat incidents as well as waiting time at hubs. To tackle the uncertainty of the problem, an efficient interactive approach using mean-absolute deviation and possibilistic programming is developed. Two heuristic algorithms based on lower bound procedure and rolling horizon scheme are presented to solve the model in large scale instances. The advantages of the presented methodologies are demonstrated through numerical experiments based on transportation networks from Sioux Falls, SD, and Western New York. We investigate different design schemes and discuss optimal decisions under different configurations.

Development of a Novel Framework for Hazardous Materials Placard Recognition System to Conduct Commodity Flow Studies Using Artificial Intelligence AlexNet Convolutional Neural Network

Conducting hazardous materials (HAZMAT) commodity flow studies (CFS) is crucial for emergency management agencies. Identifying the types and amounts of hazardous materials being transported through a specified geographic area will ensure timely response if a HAZMAT incident takes place. CFS are usually conducted using manual data collection methods, which may expose the personnel to some risks by them being subjected to road traffic and different weather conditions for several hours. On other hand, the quality and accuracy of the collected HAZMAT data are affected by the skill and alertness of the data collectors. This study introduces a framework to collect HAZMAT transportation data exploiting advanced image processing and machine learning techniques on video feed. A promising convolutional neural network (CNN), named AlexNet was used to develop and test the automatic HAZMAT placard recognition framework. A solar-powered mobile video recording system was developed using high-resolution infra-red (IR) cameras, connected to a network video recorder (NVR) mounted on a mobile trailer. The developed system was used as the continuous data collection system. Manual data collection was also conducted at the same locations to calibrate and validate the newly developed system. The results showed that the proposed framework could achieve an accuracy of 95% in identifying HAZMAT placard information. The developed system showed significant benefits in reducing the cost of conducting HAZMAT CFS, as well as eliminating the associated risks that data collection personnel could face.

A Safety Function Deployment Approach to Risk Management of HazMat Highway Transportation

Various methods have been developed to ensure the safe transportation of hazardous materials (HazMat). These methods span from conventional statistical methods to modern risk management approaches....

도로 위험물질 수송사고 예방을 위한 정책방향 고찰 - 사매2터널 화재사고를 중심으로 -

On February 17 2020, the fire accident of the Samae 2 tunnel toward Wanju on the Suncheon Wanju Line resulted in 48 casualties including 5 deaths and 43 injuries. In addition, 14 vehicles were burned down (33 vehicles damaged), resulting in enormous social and economic damage. In this paper, we will examine the status of fire accidents in Samae 2 tunnel in terms of transport accidents of dangerous substances, and consider the role of the government and road management agencies in reducing transport accidents of dangerous substances on roads. When the characteristic information such as the traffic volume, traffic volume of hazardous materials, and the status of hazardous materials accidents in the section between North Namwon and Osu of the Suncheon Wanju Line where the Samae 2 tunnel accident occurred, the traffic volume, the traffic volume of the hazardous materials, and the accidents of the hazardous materials were found to be much lower than average . The lesson to be learned from the fire accident at the Samae 2 tunnel is that dangerous material accidents do not occur frequently, but once they occur, large-scale human damage will occur. Therefore, to prevent and respond to road hazardous material transport accidents, strategies for transporting hazardous substances on the road, such as vehicle operation management, facility management, and traffic management, are needed. In particular, there are currently no national statistics on the traffic volume of dangerous substances on the road, so a national survey on the transport status of dangerous substances must be preceded. In addition, as the number of accidents in transporting dangerous substances transported by general cargo trucks is on the rise, a policy that mandates a completely closed loader should be preceded in the case of general cargo trucks transporting dangerous substances.

Too many or not enough? A methodology for evaluating fire and gas detector layouts at LNG facilities

AbstractNFPA 59A Standard for the Production, Storage, and Handling of Liquefied Natural Gas (LNG) requires facilities to have the equipment necessary for the detection and control of fires, leaks and spills of hazardous materials, yet provides no requirements or guidance on the location of the detectors. Literature available in the public domain outlines key factors to consider in developing layouts for flame and gas detectors, but a methodology to use this information in evaluating a hazard detector layout is not clearly defined; instead, generic and non‐quantifiable terminology such as “quick and reliable” is often used. As a result, there is no consistent approach to developing flame and gas detector layouts for land‐based LNG facilities and there is no systematic method for regulators to evaluate these designs. The ability to quantify hazard detector performance and define performance targets are of particular interest to the Department of Transportation (DOT) Pipeline and Hazardous Materials Safety Administration (PHMSA), which regulates numerous LNG facilities in the United States. Blue Engineering and Consulting is working on a DOT‐PHMSA sponsored project to develop a risk‐based approach and criteria for hazard detector layouts at LNG facilities. This project builds upon performance‐based design principles outlined in NFPA 72 National Fire Alarm and Signaling Code and the International Society of Automation (ISA) technical report 84.00.07 Guidance on the Evaluation of Fire, Combustible Gas, and Toxic Gas System Effectiveness. The methodology divides the LNG facility into Detection Areas based on the hazards present and the plant layout, identifies the appropriate hazard scenarios to evaluate the detector layout, establishes performance targets for each Detection Area, and quantifies hazard detector coverage. This paper will describe and demonstrate the proposed methodology. It should be noted that, while performance targets must be chosen for the purposes of the demonstrative included in this paper, these targets should not be interpreted as requirements of DOT‐PHMSA nor as acceptable to them.

Exposure to hazmat road accidents – Toxic release simulation and GIS-based assessment method

Industrial demand has intensified production, utilization, and transport of hazardous materials (hazmat). As the production and use of hazardous materials are frequently carried out at different locations, accidents during the transport of hazmats constantly occur. The most critical points on the hazmat routs within inhabited urban or rural areas are centres where the population with increased vulnerability reside. This study aims to assess the exposure of people with disabilities to the impact of hazmat road accidents, in order to provide evidence-based knowledge necessary for the establishment of competent disaster preparedness procedures. A case study was developed for ammonia release from a tanker truck in the vicinity of the Institution for Children and Youth with Disabilities in Veternik, a suburban settlement of Novi Sad, Serbia. An integrated methodology for the risk assessment of the identified problem was proposed focusing on environmental and human-induced variables with a significant impact on the hazard magnitude. The simulation conducted with ALOHA (Areal Locations of Hazardous Atmospheres) confirmed assumption that the combination of high temperature and low wind speed can cause the worst-case scenario i.e. to expose a larger surface area to the influence of released ammonia. In addition, hazard maps were developed by importing the simulated area of ammonia spread into Quantum GIS (QGIS) software.

Designing emergency response network for rail hazmat shipments under uncertainties: Optimization model and case study

Railroad is one of the primary transportation modes for hazardous materials (hazmat) shipments in North America. Designing an emergency response network capable of providing commensurate response is one of the primary levers to contain (or mitigate) the adverse consequences from rail hazmat incidents. To this end, we propose a two-stage stochastic programming model to determine the location of response facilities and equipment packages to be stockpiled therein. Several publicly available reports were consulted to estimate the different model parameters, which in turn facilitated the analyses of the case study on a realistic railroad network in Ontario (Canada). Our study suggests that the strategic emergency response network design decision made only on the basis of empirical data would undermine the effectiveness of the resulting network. We also explore several underlying trade-offs in our model, evaluate the incremental benefit of establishing (and stockpiling) additional response facilities. Furthermore, our analyses highlight that the mean coverage can be improved by redistributing the equipment in the network, purchasing equipment with higher containment capacity, and making use of a disutility multiplier factor.

Development of fire risk visualization tool based on heat map

At present, relatively few tools are able to visualize regional fire risks both dynamically and efficiently. As a general method of density clustering and visualization, heat maps can be used to analyze the spatial distribution characteristics of fire risks. In this study, based on combining a transformed sigmoid function with a heat map method, a transformed sigmoid risk model (TSRM) was proposed. A numerical mapping method between fire risk values and colors was established based on an RGB color model. By using HTML 5 and JavaScript, a fire risk visualization tool (FRVT) was developed based on the TSRM. Baidu Map was used as the geographic information system engine for the FRVT by calling its JavaScript application program interface. To validate the TSRM and FRVT with case studies, 16,448 historical fires from 2013 to 2017 in Changsha, China, were gathered and visualized. The visualization results indicated that the high-risk areas were mainly distributed in urban areas, and that the medium-risk and low-risk areas were primarily distributed in rural areas. The FRVT can also be applied to visualize and analyze process-related fire risks from a regional perspective. In further research, it will be important to update the FRVT, e.g., to visualize the fire risks posed by the transportation of hazardous materials.

Severity analysis of road transport accidents of hazardous materials with machine learning

Objective The aim of this study was to explore a suitable method for analyzing road transport accidents that involve hazardous materials and to explore the main factors that influence the occurrence of accidents of varying severity. Methods The 2015–2019 reported crash data from the Ministry of Transport of the People's Republic of China were obtained, and road transport crashes involving hazardous materials were extracted as the analysis data. The dataset was classified into three injury severity categories: property damage only (PDO), injured (INJ), and fatal (FAT). A statistical model and three machine learning-based models were developed: a random parameters logit model (RPLM), multilayer perceptron (MLP), decision tree C5.0 (C5.0) and support vector machine (SVM). The four models were trained/estimated using the training/estimation dataset, and the best model was used to model accidents of the three different severity levels. The main factors that influence the occurrence of accidents at each crash severity level were obtained. Results C5.0 had the best modeling performance. The direct accident form (DAF), indirect accident form (IAF) and road segment (RS) were determined to be the critical determinants of PDO accidents. The DAF, IAF, road type, RS and time had a substantial effect on INJ accidents. The DAF, IAF, hazardous material type (HMT) and road surface condition were important factors in the occurrence of FAT accidents. Conclusions Different data have unique characteristics, and the best modeling and analysis method should be chosen accordingly. The safety of road transport of hazardous materials in China is poor, and the losses caused by accidents are substantial. Strengthening the monitoring of travel speed and travel time; improving driver safety awareness, driving skills and the ability to mitigate emergencies; improving the configuration of vehicle safety equipment and the linkage with the control center and rescue center; improving the environmental differences between inside a tunnel and outside a tunnel; reducing the design of long downhill and steep slope sections; reducing the transport plan in unsafe environments; and improving the ability of road management to mitigate bad environments can be effective measures to reduce the severity of road transport accidents involving hazardous materials.

Macro-level hazardous material transportation safety analysis in China using a Bayesian negative binomial model combined with conditional autoregression prior

Traffic safety for hazardous material (hazmat) transportation has not been studied well at a macro level in recent years. A Bayesian negative binomial conditional autoregressive safety model was used within Chinese provinces and cities. A total of 1,229 hazmat transportation crashes in China were collected from the years 2015 to 2017. The frequency of hazmat transportation crashes and the frequency of severe crashes including fatalities and serious injuries were studied in relation to socioeconomic factors, road classification, and the scale of hazmat transportation. The results show that higher crash frequencies are associated with a greater gross domestic product index, increasing road densities, and number of hazmat transportation vehicles and hazmat drivers per vehicle. The frequency of severe crashes tends to be higher in provinces with greater populations, increasing road densities, mileage of low-grade roads, and number of companies. The urban road mileage and number of hazmat loaders are negatively associated with the total number of hazmat crashes and severe crashes. Additionally, the hospital density also has a negative correlation with the frequency of severe traffic crashes. These results could help hazmat transportation managers and planners determine the risk factors of hazmat crashes on a macro level and develop appropriate measures for improving hazmat transportation safety.

Pythagorean fuzzy AHP based risk assessment methodology for hazardous material transportation: an application in Istanbul.

Environment and social life are open to hazards, because of the distribution, diffusion, and conversion processes of chemicals contained in hazardous materials. These chemicals are very dangerous. Various precautions should be taken into consideration during the displacement of hazardous materials. Therefore, it is important to identify and minimize the risks in the transportation of hazardous material. This work investigates to identify the critical risk factors and their weights for hazardous material transportation operations. The literature is reviewed, critical risk factors for hazardous material transportation are defined, and data from different experts is collected. A two-level hierarchical structure is established to evaluate risk factors. Then, the experts' evaluations of main and sub-risk factors are consolidated using the modified Delphi method. Weights of main and sub-risk factors are obtained using the Pythagorean fuzzy analytic hierarchy process method. To show the robustness of the proposed decision-making methodology, a sensitivity analysis is conducted.

Big data analytics for sustainable cities: An information triangulation study of hazardous materials transportation

Big data analytics (BDA) is regarded as an advanced tool for achieving sustainable development as part of the grand challenges (GCs). However, it is not clear how BDA can be used by data scientists to solve the GCs with multisource data in a cross-disciplinary approach. Based on a case study of city-based dangerous goods transportation (DGT), this paper explores how data scientists use BDA to triangulate data, methods, knowledge and solutions for solving GCs. The contribution of this study is threefold: (1) it contributes to research on GCs and discusses how BDA can be used in problem solving for multidomain GCs from a management perspective; (2) it enriches the theory of information triangulation and proposes several steps for information triangulation in BDA to solve GCs; and (3) it contributes some practical implications for the management of organizations when solving social problems and pursuing sustainable development.

Electrochemical Disinfection in Water and Wastewater Treatment: Identifying Impacts of Water Quality and Operating Conditions on Performance.

Electrochemical disinfection—a method in which chemical oxidants are generated in situ via redox reactions on the surface of an electrode—has attracted increased attention in recent years as an alternative to traditional chemical dosing disinfection methods. Because electrochemical disinfection does not entail the transport and storage of hazardous materials and can be scaled across centralized and distributed treatment contexts, it shows promise for use both in resource limited settings and as a supplement for aging centralized systems. In this Critical Review, we explore the significance of treatment context, oxidant selection, and operating practice on electrochemical disinfection system performance. We analyze the impacts of water composition on oxidant demand and required disinfectant dose across drinking water, centralized wastewater, and distributed wastewater treatment contexts for both free chlorine- and hydroxyl-radical-based systems. Drivers of energy consumption during oxidant generation are identified, and the energetic performance of experimentally reported electrochemical disinfection systems are evaluated against optimal modeled performance. We also highlight promising applications and operational strategies for electrochemical disinfection and propose reporting standards for future work.

Robust Hazardous Materials Closed-Loop Supply Chain Network Design with Emergency Response Teams Location

Hazardous materials (hazmat) storage and transportation pose threats to people’s safety and the environment, which creates a need for governments and local authorities to regulate such shipments. This paper proposes a novel mathematical model for what is termed the hazmat closed-loop supply chain network design problem. The model, which can be viewed as a way to combine several directions previously considered in the literature, includes two echelons in the forward direction (production and distribution centers), three echelons in the backward direction (collection, recovery, and disposal centers), and emergency response team positioning. The two objectives of minimizing the strategic, tactical, and operational costs as well as the risk exposure on road networks are considered in this model. Since the forward flow of hazmat is directly related to the reverse flow, and since hazmat accidents can occur at all stages of the lifecycle (storage, shipment, loading, and unloading, etc.), it is argued that such a unified framework is essential. A robust framework is also presented to hedge the optimization model in case of demand and return uncertainty. The performance of both models is evaluated based on a standard dataset from Albany, NY. Considering the trade-offs between cost and risk, the results demonstrate the design of efficient hazmat closed-loop supply chain networks where the risk exposure can be reduced significantly by employing the proposed models.