Railroad Derailment Factors Affecting Hazardous Materials Transportation Risk

2003 ◽  
Vol 1825 (1) ◽  
pp. 64-74 ◽  
Author(s):  
Christopher P. L. Barkan ◽  
C. Tyler Dick ◽  
Robert Anderson
Keyword(s):  
Performance Measures ◽  
Predictive Factors ◽  
Hazardous Materials ◽  
Release Rates ◽  
Factors Affecting ◽  
Hazardous Materials Transportation ◽  
Transportation Risk ◽  
Proxy Variables ◽  
Safety Improvement ◽  
Highly Correlated

U.S. freight railroad accident and hazardous materials release rates have declined substantially since 1980. Ironically, this trend has made the identification and implementation of further safety improvement options more challenging because less empirical information exists on which accident causes present the greatest risks. Consequently, more sophisticated methods are needed to identify the best options for transportation risk reduction. Of particular interest is identifying the principal causes of accidents that can result in a tank car release of hazardous materials, which can harm people, property, and the environment. Because large hazardous materials release accidents are relatively rare, railroads cannot effectively manage safety improvement efforts solely in response to the causes of specific accidents. Instead, a risk-based approach is needed to better understand predictive factors for conditions that can cause a release. Railroad derailment data were analyzed to identify the conditions most likely to lead to a release accident. The objective was to identify proxy variables that can be used as performance measures. The speed of derailment and number of derailed cars highly correlated with hazardous materials releases. Some accident causes are much more likely to lead to release conditions than others. Accident prevention efforts to reduce these causes are more likely to reduce the risk of major railroad hazardous materials release accidents.

Research Status and Development Trends of Hazardous Materials Transportation

2011 ◽  
Vol 361-363 ◽  
pp. 1230-1239
Author(s):  
Zong Feng Zou ◽  
Bao Quan Zhang
Keyword(s):  
Hazardous Materials ◽  
Future Research ◽  
Joint Control ◽  
Large Area ◽  
Role Of Government ◽  
Hazardous Materials Transportation ◽  
Transportation Risk ◽  
Hazmat Transportation ◽  
Research Situation

The related issues of hazardous materials transportation in recent years are summarized and reviewed from the following aspects: hazardous materials transportation risk evaluation models, road routing models, the application of related technology, early warning for emergency response and joint action mechanism and platform construction, the research situation and development pattern of unified monitoring platform, etc. Analysis shows that it is essential to establish more in-depth and scientific quantitative models based on the attainment of more comprehensive and continuous data as well as the consideration of various constraints. It is a direction for future research to develop comprehensive application of technology and to establish HAZMAT transportation joint control platform in large area, and the leading and facilitating role of government should be paid more attention on joint control platform construction in large area.

scholarly journals Fleet Scheduling Optimization of Hazardous Materials Transportation: A Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Changxi Ma ◽  
Jibiao Zhou ◽  
Xuecai (Daniel) Xu ◽  
Fuquan Pan ◽  
Jin Xu
Keyword(s):  
Hazardous Materials ◽  
Route Optimization ◽  
Research Progress ◽  
Accident Rate ◽  
Scheduling Optimization ◽  
Research Directions ◽  
Hazardous Materials Transportation ◽  
Research Results ◽  
Transportation Risk ◽  
Scheduling Method

To comprehensively understand the research progress of the fleet scheduling for hazardous materials, the study has summarized the corresponding research results from three aspects (a) hazardous materials transportation risk, (b) route optimization, and (c) fleet scheduling, and then pointed out potential problems from six aspects: (a) the coupling risk of the transport fleet; (b) the screening of time and space for the transport of hazardous materials; (c) the scheduling optimization for transport fleets; (d) taking insufficient account of transport risks fairness; (e) insufficient robustness of scheduling schemes; and (f) lacking of research results on fleet scheduling of transport in the context of antiterrorism. After that, by considering the existing shortcomings of the current research, five research directions are presented that should be further explored in the future. Subsequently, both rough set and association rule theory is applied to explore the cause chain of transportation accidents for hazardous materials, and analyze the mechanism of transport accident for hazardous materials. Next, the Bayesian network is presented to predict the accident rate of hazardous materials transportation under different temporal and spatial conditions, and the dynamic rolling scheduling method of hazardous materials transport fleet is constructed under normal and antiterrorism background.

Risk Comparison of Transporting Hazardous Materials in Unit Trains versus Mixed Trains

2017 ◽  
Vol 2608 (1) ◽  
pp. 134-142 ◽  
Author(s):  
Xiang Liu
Keyword(s):  
Decision Support Tool ◽  
Hazardous Materials ◽  
Rolling Stock ◽  
Traffic Density ◽  
Support Tool ◽  
Hazardous Materials Transportation ◽  
Traffic Demand ◽  
Transportation Risk ◽  
Rail Line ◽  
Train Length

This research developed an integrated, generalized risk analysis methodology for comparing hazardous materials transportation risk in unit trains versus mixed trains for the same amount of traffic demand. The risk methodology accounted for FRA track class, method of operation, annual traffic density, train length, speed, point of derailment, the number and placement of tank cars in a train, tank car placement, tank car safety design, and population density along the rail line. With these inputs, the methodology estimates train derailment rate, the probability of tank car derailment and release, and release consequence by train configuration. The analysis showed that tank car positions could affect the risk comparison between unit trains and mixed trains in transporting hazardous materials. In particular, if all tank cars were in positions that were least prone to derailment, distributing tank cars to many unit trains could reduce the overall risk. Otherwise, consolidating tank cars into unit trains could lead to a lower risk. The methodology has been implemented in a computer-aided decision support tool that automatically calculates the risk values for various track, rolling stock, and operational characteristics.

scholarly journals Multi-Depot Vehicle Routing Optimization Considering Energy Consumption for Hazardous Materials Transportation

2018 ◽  
Vol 10 (10) ◽  
pp. 3519 ◽  
Author(s):  
Cunrui Ma ◽  
Baohua Mao ◽  
Qi Xu ◽  
Guodong Hua ◽  
Sijia Zhang ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Energy Consumption ◽  
Vehicle Routing ◽  
Hazardous Materials ◽  
Transportation Energy ◽  
Pareto Solution ◽  
Hazardous Materials Transportation ◽  
Multi Objective ◽  
Transportation Risk ◽  
Multi Objective Genetic Algorithm

Focusing on the multi-depot vehicle routing problem (MDVRP) for hazardous materials transportation, this paper presents a multi-objective optimization model to minimize total transportation energy consumption and transportation risk. A two-stage method (TSM) and hybrid multi-objective genetic algorithm (HMOGA) are then developed to solve the model. The TSM is used to find the set of customer points served by each depot through the global search clustering method considering transportation energy consumption, transportation risk, and depot capacity in the first stage, and to determine the service order of customer points to each depot by using a multi-objective genetic algorithm with the banker method to seek dominant individuals and gather distance to keep evolving the population distribution in the second stage, while with the HMOGA, customer points serviced by the depot and the serviced orders are optimized simultaneously. Finally, by experimenting on two cases with three depots and 20 customer points, the results show that both methods can obtain a Pareto solution set, and the hybrid multi-objective genetic algorithm is able to find better vehicle routes in the whole transportation network. Compared with distance as the optimization objective, when energy consumption is the optimization objective, although distance is slightly increased, the number of vehicles and energy consumption are effectively reduced.

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