Design Optimization of a HAZMAT Multimodal Hub-and-Spoke Network with Detour

The growing transportation risk of hazardous materials (hazmat) is an important threat to public safety. As an efficient and reliable mode of transportation, the multimodal hub-and-spoke transport network helps to achieve economies of scale and reduce costs. Considering the dual goals of risk and cost management of hazmat transportation, a novel optimization model of a multimodal hub-and-spoke network with detour (MHSNWD) for hazmat on the strategic level is designed. It integrates the planning of hub location and route selection based on the risk quantification for different transportation modes. Additionally, a detour strategy is applied, which allows for more than two hub nodes to be selected to form an optimal path between any supply and demand nodes in a hub-and-spoke network. Then, the risk is taken as the main objective and the cost is converted into a budget constraint to solve the model by using CPLEX. Additionally, a numerical study is conducted based on a CAB dataset to find the influence of the number of hubs and budget constraints on the optimization results. In addition, a counterpart model of the multimodal hub-and-spoke network without detour (MHSNOD) is tested to validate the advantages of the proposed model of MHSNWD. The numerical experiment shows that an appropriate increase in the number of hubs and the cost budget can remarkably reduce network risk. Compared with MHSNOD, the optimal result of MHSNWD can achieve a marginal improvement in risk reduction. This work may provide an informative decision-making reference for planning a hazmat transportation network.

Applying Data Mining Approaches for Analyzing Hazardous Materials Transportation Accidents on Different Types of Roads

With the increase in the demand for and transportation of hazardous materials (Hazmat), frequent Hazmat road transport accidents, high death tolls and property damage have caused widespread societal concern. Therefore, it is necessary to carry out risk factor analysis of Hazmat transportation; predict the severity of accidents; and develop targeted, extensive and refined preventive measures to guarantee the safety of Hazmat road transportation. Based on the philosophy of graded risk management, this study used a priori algorithms in association rule mining (ARM) technology to analyze Hazmat transport accidents, using road types as classification criteria to find rules that had strong associations with property-damage-only (PDO) accidents and casualty (CAS) accidents under different road types. The results indicated that accidents involving PDO had a strong association with weather (WEA), traffic signals (TS), surface conditions (SC), fatigue (FAT) and vehicle safety status (VSS), and that accidents involving CAS had a strong association with VSS, equipment safety status (ESS), time of day (TOD) and WEA when urban roads were used for Hazmat transportation. Among Hazmat transport incidents on rural roads, the incidence of PDO accidents was associated with intersections (IN), SC, WEA, vehicle type (VT), and segment type (ST), while the occurrence of CAS accidents was associated with qualification (QUA), ESS, TS, VSS, SC, WEA, TOD, and month (MON). Strong associations between the occurrence of PDO accidents and related items, such as IN, SC, WEA and FAT, and the occurrence of CAS accidents and related items, such as ESS, TOD, VSS, WEA and SC, were identified for Hazmat road transport accidents on highways. The accident characteristics exemplified by strongly correlated rules were used as the input to the prediction model. Considering the scarcity of these events, four prediction models were selected to predict the severity of Hazmat accidents on each road type employing four analyses, and the most suitable prediction model was determined based on the evaluation criteria. The results showed that extreme gradient boosting (XGBoost) is preferable for predicting the severity of Hazmat accidents occurring on urban roads and highways, while nearest neighbor classification (NNC) is more suitable for predicting the severity of Hazmat accidents occurring on rural roads.

Comparisons of a Multi-Objective Compromise Weight Model and a Multi-Objective Evolutionary Algorithm in Hazmat Transportation Route Planning

On July 31, 2014, a series of gas pipeline explosions occurred in Kaohsiung City, causing 32 deaths and 321 injuries. Following this accident, the Kaohsiung City government decided to replace the use of pipelines with trucks for transporting hazmat, transfering the risk from beneath the road to the surface. This means that careful consideration needs to be given to safety and cost in hazardous material (hazmat) transportation route planning. The issue of how to design optimal routes for the transportation of hazmat is, therefore, important and involves consideration of various criteria. This research focuses on three objectives: cost, risk, and emergency response capability. It then constructs two solution algorithms, the compromise weight model and the evolutionary algorithm, to solve the multi-objective problem. The results from these two algorithms are observed and compared. In addition, this research also provides some recommendations for stakeholders, including the hazmat industry, government, and residents.

Core Competencies of Truck Drivers Responding to Emergencies during Transportation of Hazardous Materials

Background. Hazardous material (HAZMAT) transportation drivers are responsible for safe delivery of consignments and face multiple challenges carrying out their duties. Drivers are also the first to respond to emergencies and accidents. Objectives. The purpose of the present study was to identify the essential competencies needed by HAZMAT transportation drivers to deal with emergencies. Methods. Three rounds of focus groups were conducted using expert panels comprised of HAZMAT specialists, health, safety and emergency representatives, security experts and transportation advisors from June to July 2019. The panel discussed competencies, gathered from a literature review, for emergency responders. Results. The panel identified six (6) core and 23 sub-competencies of HAZMAT drivers. This is the first study in low- and middle-income countries (LMIC) to identify core competencies of HAZMAT truck drivers. Conclusions. The integration of these competencies into a development and training program for drivers will better enable drivers to handle emergencies in an efficient and effective manner. Participant Consent. Obtained Ethics Approval. The Graduate Advisory Committee of Comsats University approved study protocols. Participant Consent. Obtained Competing Interests. The authors declare no competing financial interests.

Bilevel Optimization for the Hazmat Transportation Problem with Lane Reservation

In this study, we investigate a bilevel optimization model for the hazmat transportation problem with lane reservation. The problem lies in selecting lanes to be reserved in the network and planning paths for hazmat transportation tasks. The trade-off among transportation cost, risk, and impact on the normal traffic is considered. By using the traffic flow theory, we quantify the impact on the normal traffic and modify the traditional risk measurement model. The problem is formulated as a multiobjective bilevel programming model involving the selection of reserved lanes for government and planning paths for hazmat carriers. Two hybrid metaheuristic algorithms based on the particle swarm optimization algorithm and the genetic algorithm, respectively, are proposed to solve the bilevel model. Their performance on small-scale instances is compared with exact solutions based on the enumeration method. Finally, the computational results on large-scale instances are compared and sensitivity analysis on the key parameters is presented. The results indicate the following: (1) Both algorithms are effective methods for solving this problem, and the method based on the particle swarm optimization algorithm requires a shorter computation time, whereas the method based on the genetic algorithm shows more advantages in optimality. (2) The bilevel model can effectively reduce the total risk of the hazmat transportation while considering the interests of hazmat carriers and ordinary travellers. (3) The utilization rate of reserved lanes increases with an increasing number of tasks. Nevertheless, once the proportion of hazmat vehicles becomes excessive, the advantage of reducing the risk of the reserved lanes gradually decreases.

A Literature Review on Rail Transport of Hazmat Release Risk Analysis

Rail plays an important role in hazmat transportation, transporting over two million carloads of hazardous materials (hazmat) in the United States annually. Compared with a truck trailer carrying a single hazmat car, a train has much more severe consequence of hazmat release due to carrying multiple connected hazmat cars (e.g., 50 to 120 flammable liquid cars). It is of high priority for the government and railroad companies to enhance the railroad hazmat transportation safety since the train accidents can cause severe railroad hazmat release incidents. Based on the data provided by Federal Railroad Administration (FRA) of the U.S. Department of Transportation (U.S.DOT), there are over 300 accident causes, including infrastructure failure defects, rolling stock failures, human errors, weather conditions, etc. It is significant to understand the relationship between hazmat transportation risk and accident cause to provide guidance for developing, evaluating, and prioritizing accident prevention strategies, thereby mitigating hazmat transportation risk. Therefore, this paper reviews the literature on rail transport of hazmat release risk analysis in order to capture the event chain leading to hazmat release, possible risk factors, and the state of the art on existing risk analysis methodologies. We reviewed the related references based on a five-step process: (1) train accident occurrence, (2) number of cars derailed, (3) number of hazardous material cars derailed, (4) number of hazmat cars releasing, and (5) release consequences. First, many severe hazmat release incidents are caused by train accidents, particularly train derailments. Prior research found that over 70% of freight train mainline derailments were caused by either infrastructure defects or rolling stock failures. Possible strategies for reducing the probability of train accidents include the prevention of track defects, equipment condition monitoring to reduce in-service failures, and the use of more advanced train control technologies to reduce human error. Second, number of cars derailed is an important factor causing hazmat releasing. Based on the reviewed literature, the total number of cars derailed depends on accident cause, speed, train length, and point of derailment. Third, the literature implied that the total number of hazmat cars derailed is related to train length, number of hazmat cars and non-hazmat cars in a train, and their placement. Fourth, the number of hazmat cars releasing contents is influenced by hazardous materials car safety design, accident speed, etc. Finally, the consequences of a release can be measured by different metrics, such as property damage, environmental impact, traffic delay, or the affected population. Geographical information systems (GIS) can be used for consequence analysis integrated with other databases such as census and rail network data.