Influential factors associated with consecutive crash severity: A two-level logistic modeling approach

A consecutive crash series is composed by a primary crash and one or more subsequent secondary crashes that occur immediately within a certain distance. The crash mechanism of a consecutive crash series is distinctive, as it is different from common primary and secondary crashes mainly caused by queuing effects and chain-reaction crashes that involve multiple collisions in one crash. It commonly affects a large area of road space and possibly causes congestions and significant delays in evacuation and clearance. This study identified the influential factors determining the severity of primary and secondary crashes in a consecutive crash series. Basic, random-effects, random-parameters, and two-level binary logistic regression models were established based on crash data collected on the freeway network of Guizhou Province, China in 2018, of which 349 were identified as consecutive crashes. According to the model performance metrics, the two-level logistic model outperformed the other three models. On the crash level, double-vehicle primary crash had a negative association with the severity of secondary consecutive crashes, and the involvement of trucks in the secondary consecutive crash had a positive contribution to its crash severity. On a road segment level, speed limit, traffic volume, tunnel, and extreme weather conditions such as rainy and cloudy days had positive effects on consecutive crash severity, while the number of lanes was negatively associated with consecutive crash severity. Policy suggestions are made to alleviate the severity of consecutive crashes by reminding the drivers with real-time potential hazards of severe consecutive crashes and providing educative programs to specific groups of drivers

Identifying High Crash Risk Roadways through Jerk-Cluster Analysis

The state-of-the-practice for most municipal traffic agencies seeking to identify high-risk road segments has been to use prior crash history. While historic traffic crash data is recognized to be valuable in improving roadway safety, it relies on prior observation rather than future crash likelihood. Recently, however, researchers are developing predictive crash methods based on “abnormal driving events.” These include abrupt and atypical vehicle movements thought to be indicative of crash avoidance maneuvers and/or near-crashes. Because these types of near-crash events occur far more frequently than actual crashes, it is hypothesized that they can be used as an indicator of high-risk locations and, even more valuably, to identify where crashes are likely to occur in the future. This thesis describes the results of research that used naturalistic driving data collected from global positioning system (GPS) sensors to locate high concentrations of abrupt and atypical vehicle movements in Baton Rouge, Louisiana based on vehicle rate of change of acceleration (jerk). Statistical analyses revealed that clusters of high magnitude jerk events while decelerating were significantly correlated to long-term crash rates at these same locations. These significant and consistent relationships between jerks and crashes suggest that these events can be used as surrogate measures of safety and as a way of predicting safety problems before even a single crash has occurred

Paying for Safety: Preferences for Mortality Risk Reductions on Alpine Roads

This paper presents a choice experiment, which values reductions in mortality risk on Alpine roads. These roads are on one hand threatened by common road hazards, on the other hand they are also endangered by natural hazards such as avalanches and rockfalls. Drawing on choice data from frequently exposed and barely exposed respondents, we are not only able to estimate the VSL but to explore how the respondents differ in their individual willingness-to-pay depending on personal characteristics. To address heterogeneity in preferences for risk reduction, we use a non-linear conditional logit model with interaction effects. The best estimate of the VSL in the context of fatal accidents on Alpine roads is in the range of €4.9–5.4 million with distinct differences between the urban and the mountain sample groups. We find the VSL to be significantly altered by socio-economic factors but only marginally altered by the type of hazard.Value of Statistical Life, Choice Experiment, Natural Hazard Mitigation, Traffic Safety

Book of abstracts of the 24th Euro Working Group on Transportation Meeting

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Coordinated Transit Response Planning and Operations Support Tools for Mitigating Impacts of All-Hazard Emergency Events

This report summarizes current computer simulation capabilities and the availability of near-real-time data sources allowing for a novel approach of analyzing and determining optimized responses during disruptions of complex multi-agency transit system. The authors integrated a number of technologies and data sources to detect disruptive transit system performance issues, analyze the impact on overall system-wide performance, and statistically apply the likely traveler choices and responses. The analysis of unaffected transit resources and the provision of temporary resources are then analyzed and optimized to minimize overall impact of the initiating event

Modeling Decision Making Related to Incident Delays During Hurricane Evacuations

Successful evacuations from metropolitan areas require optimizing the transportation network, monitoring conditions, and adapting to changes. Evacuation plans seek to maximize the city\u27s ability to evacuate traffic to flee the endangered region, but once an evacuation begins, real time events degrade even the best plans. To better understand behavioral responses made during a hurricane evacuation, a survey of potential evacuees obtained data on demographics, driving characteristics, and the traffic information considered prior to and during an evacuation. Analysis showed significant levels of correlation between demographic factors (e.g., gender, age, social class, etc.) and self-assessed driver characteristics, but limited correlation with the decision to take an alternate route. Survey results suggest evacuees\u27 decisions to divert are functions of the length of time a driver has been in congestion, the amount of travel information provided, and its method of delivery. This association differs significantly from those identified by other studies that focused on routine, non-evacuation, conditions. A decision-making model that forecasts decision tendencies using these factors was created. The model was integrated in and tested using a dynamic evacuation simulation. The combined model and simulation allow assessment of the impacts traveler information content, timing, and method of delivery have on traffic flow and evacuation times, imitating the impact of traffic information systems. The effectiveness of alternate route use was assessed by measurements of total vehicle volumes processed and queue persistence. Effectiveness was highly dependent on the road network in the immediate vicinity, especially the number of accesses to the alternate route and vehicle capacity on the alternate route and accesses. Integration of the decision-making model in a dynamic hurricane evacuation simulation is unique to this study. This study yields a greater understanding of evacuee decisions and factors associated with related travel decisions. It provides the novel integration of a behavioral model and a dynamic evacuation simulation, increasing the realism of evacuation planning and providing a valuable tool supporting the decision process. Understanding gained may contribute to reduced evacuation times and enhanced public safety

Statistical Investigation of Road and Railway Hazardous Materials Transportation Safety

Transportation of hazardous materials (hazmat) in the United States (U.S.) constituted 22.8% of the total tonnage transported in 2012 with an estimated value of more than 2.3 billion dollars. As such, hazmat transportation is a significant economic activity in the U.S. However, hazmat transportation exposes people and environment to the infrequent but potentially severe consequences of incidents resulting in hazmat release. Trucks and trains carried 63.7% of the hazmat in the U.S. in 2012 and are the major foci of this dissertation. The main research objectives were 1) identification and quantification of the effects of different factors on occurrence and consequences of hazmat-related incidents, towards identifying effective policies and countermeasures for improving safety and; 2) quantifying components of risk of hazmat transportation for costs prediction, planning purposes, or short-term decision-making. A comprehensive review of literature, study framework, and available data led to identification of six foci for this dissertation: 1) estimation of hazmat release statistical models for railroad incidents; 2) estimation of rollover and hazmat release statistical models for Cargo Tank Truck (CTT) crashes; 3) analyzing hazmat-involved crashes at highway-rail grade crossings (HRGCs); 4) model-based and non-model-based methods for classifying hazmat release from trains and CTTs; 5) estimation of macroscopic-level statistical models for frequency and severity of rail-based crude oil release incidents; and 6) estimation of statistical models for types and consequences of rail-based crude oil release incidents. Some of the findings of this research include: train derailments increased hazmat release probability more than other incident types; non-collision CTT crashes were more likely to result in rollovers, while rolling over increased the likelihood of hazmat release; at HRGCs, flashing signal lights were associated with lower hazmat release probability from trucks; increase in volume and distance of crude oil shipped from one state to another led to greater frequency and severity of incidents between the two states; and in rail-based crude oil release incidents, non-accident releases were associated with higher probability of gas dispersion, and lower probability of fire and explosion. Based on the results, recommendations regarding policies and countermeasures for improving safety are provided. Advisor: Aemal Khatta

Impact of Weather Conditions on Travel Demand – The Most Common Research Methods and Applied Models

This paper presents an overview of the applied research methodologies and developed travel demand models that take weather impact into account. The paper deals with trip generation and modal split as elements of travel demand that best describe changes in the travel behaviour in different weather conditions. The authors herein emphasize the importance of research in local conditions in all climate zones, especially in areas where climate and modal split characteristics are different from those in common research areas. This review is designed as a brief guide on how the impact of weather can be explored in order to encourage conducting research even in the countries where there is no systematic traffic and travel data collection. The stated adaptation technique followed by the panel household travel surveys may be particularly appropriate for those countries. It is concluded that small budgets should not be considered an obstacle, because it is possible to draw reliable conclusions based even on small samples. Moreover, modern research methods enable a cheaper survey process together with the possibility of obtaining higher quality of results. The increasing popularity of research in this field should contribute to the creation of more resilient transport systems all over the world. A special contribution of this paper is the review of research studies carried out in central, western and southern Europe and not mentioned in any review paper before.</p