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.

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.

Smartphones and Integrated Control System for Hazardous Materials Transportation

Drivers’ behaviors are directly influenced by human beings and have different reactions. Artificial intelligence is a powerful tool to learn and predict the traffic effects according to drivers’ behavior and make predictions more effective to support hazardous material traffic management. This paper presents a proposal using deep learning, simulation, and performance analysis of road systems with improvement in hazardous materials transportation control. The analysis compares the reduction of accident detection time with their consequences such as damages caused by traffic jams, damages to human health, and environmental damages. The reduction in detection time is provided by the use of smartphones and an integrated control system for tracking, management, monitoring, and control of hazardous materials transportation.

Half Open Multi-Depot Heterogeneous Vehicle Routing Problem for Hazardous Materials Transportation

How to reduce the accidents of hazardous materials has become an important and urgent research topic in the safety management of hazardous materials. In this study, we focus on the half open multi-depot heterogeneous vehicle routing problem for hazardous materials transportation. The goal is to determine the vehicle allocation and the optimal route with minimum risk and cost for hazardous materials transportation. A novel transportation risk model is presented considering the variation of vehicle loading, vehicle types, and hazardous materials category. In order to balance the transportation risk and the transportation cost, we propose a bi-objective mixed integer programming model. A hybrid intelligent algorithm is developed based on the ε-constraint method and genetic algorithm to obtain the Pareto optimal solutions. Numerical experiments are provided to demonstrate the effectiveness of the proposed model. Compared with the close multi-depot heterogeneous vehicle routing problem, the average risk and cost obtained by the proposed bi-objective mixed integer programming model can be reduced by 3.99% and 2.01%, respectively. In addition, compared with the half open multi-depot homogeneous vehicle routing problem, the cost is significantly reduced with the acceptable risk.