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Driving the wrong way on freeways has been a nagging traffic safety problem since the interstate highway system was founded in the 1950s. Despite four decades of highway striping and sign improvements at freeway interchanges, the problem persists. This paper is to determine the contributing factors to wrong-way driving on freeways and to develop promising, cost-conscious countermeasures to reduce driving errors and related crashes. Based on the collected wrong-way crash data, the safety performance function (SPF) for wrong-way crashes on freeway was developed with the annual average daily traffic (AADT) and segment length being the independent variables. The procedures for candidate wrong-way crash sites diagnoses with crash data, historic site data, field condition and other information were described step by step. The methods for contributing factors identification were proposed and the Haddon matrix for wrong-way crashes on freeway was constructed finally. Methods for selecting wrong-way crash countermeasures from the perspective of "four E's" based on crash analysis finding, site-specific contributing factors and geographical characteristics were discussed, and research needs on wrong-way crash management in the future were recommended.
"[This report] is a handbook to help reduce the risk of wrong-way driving crashes. The handbook was developed on the basis of a systematic literature review, collection and analysis of wrong-way driving incident and crash data, and evaluation of a range of relevant traffic control devices and other technologies. The handbook provides evidence-based information to support implementation of traffic control devices, advanced technologies, geometric design features, and education and enforcement strategies to significantly reduce the number of wrong-way driving incidents and crashes on freeways and divided highways. This handbook will be of interest to state departments of transportation and other stakeholders concerned with reducing the number of wrong-way driving incidents and crashes that occur on freeways and divided highways." -- foreword
Drivers who make wrong-way entries onto freeways pose a serious risk to the safety of other motorists and themselves. This report documents the recommended guidelines and best practices developed during the project. The research team based the guidelines and best practices on the results of the literature review, surveys, analysis of freeway-related wrong-way crashes in Texas, and evaluation of available countermeasures. This report also provides a wrong-way entry checklist for engineers and field crews to use for reviewing wrong-way entry issues or suspected problem locations. This checklist was based on one currently used by the California Department of Transportation with some additions based on project findings.
Each year, hundreds of fatal wrong-way driving (WWD) crashes occur across the United States, and thousands of injuries are reported in traffic crashes caused by wrong-way drivers. Although WWD crashes have been a concern since the advent of access-controlled, divided roadways, the problem persists despite efforts to address it over time. The objective of this book is to provide guidance for implementing traditional and advanced safety countermeasures to achieve a significant reduction in the number of WWD incidents and crashes on freeways.
Motor vehicle crashes in the United States continue to be a serious safety concern for state highway agencies, with over 30,000 fatal crashes reported each year. The World Health Organization (WHO) reported in 2016 that vehicle crashes were the eighth leading cause of death globally. Crashes on roadways are rare and random events that occur due to the result of the complex relationship between the driver, vehicle, weather, and roadway. A significant breadth of research has been conducted to predict and understand why crashes occur through spatial and temporal analyses, understanding information about the driver and roadway, and identification of hazardous locations through geographic information system (GIS) applications. Also, previous research studies have investigated the effectiveness of safety devices designed to reduce the number and severity of crashes. Today, data-driven traffic safety studies are becoming an essential aspect of the planning, design, construction, and maintenance of the roadway network. This can only be done with the assistance of state highway agencies collecting and synthesizing historical crash data, roadway geometry data, and environmental data being collected every day at a resolution that will help researchers develop powerful crash prediction tools. The objective of this research study was to predict vehicle crashes in real-time. This exploratory analysis compared three well-known machine learning methods, including logistic regression, random forest, support vector machine. Additionally, another methodology was developed using variables selected from random forest models that were inserted into the support vector machine model. The study review of the literature noted that this study's selected methods were found to be more effective in terms of prediction power. A total of 475 crashes were identified from the selected urban highway network in Kansas City, Kansas. For each of the 475 identified crashes, six no-crash events were collected at the same location. This was necessary so that the predictive models could distinguish a crash-prone traffic operational condition from regular traffic flow conditions. Multiple data sources were fused to create a database including traffic operational data from the KC Scout traffic management center, crash and roadway geometry data from the Kanas Department of Transportation; and weather data from NOAA. Data were downloaded from five separate roadway radar sensors close to the crash location. This enable understanding of the traffic flow along the roadway segment (upstream and downstream) during the crash. Additionally, operational data from each radar sensor were collected in five minutes intervals up to 30 minutes prior to a crash occurring. Although six no-crash events were collected for each crash observation, the ratio of crash and no-crash were then reduced to 1:4 (four non-crash events), and 1:2 (two non-crash events) to investigate possible effects of class imbalance on crash prediction. Also, 60%, 70%, and 80% of the data were selected in training to develop each model. The remaining data were then used for model validation. The data used in training ratios were varied to identify possible effects of training data as it relates to prediction power. Additionally, a second database was developed in which variables were log-transformed to reduce possible skewness in the distribution. Model results showed that the size of the dataset increased the overall accuracy of crash prediction. The dataset with a higher observation count could classify more data accurately. The highest accuracies in all three models were observed using the dataset of a 1:6 ratio (one crash event for six no-crash events). The datasets with1:2 ratio predicted 13% to 18% lower than the 1:6 ratio dataset. However, the sensitivity (true positive prediction) was observed highest for the dataset of a 1:2 ratio. It was found that reducing the response class imbalance; the sensitivity could be increased with the disadvantage of a reduction in overall prediction accuracy. The effects of the split ratio were not significantly different in overall accuracy. However, the sensitivity was found to increase with an increase in training data. The logistic regression model found an average of 30.79% (with a standard deviation of 5.02) accurately. The random forest models predicted an average of 13.36% (with a standard deviation of 9.50) accurately. The support vector machine models predicted an average of 29.35% (with a standard deviation of 7.34) accurately. The hybrid approach of random forest and support vector machine models predicted an average of 29.86% (with a standard deviation of 7.33) accurately. The significant variables found from this study included the variation in speed between the posted speed limit and average roadway traffic speed around the crash location. The variations in speed and vehicle per hour between upstream and downstream traffic of a crash location in the previous five minutes before a crash occurred were found to be significant as well. This study provided an important step in real-time crash prediction and complemented many previous research studies found in the literature review. Although the models investigate were somewhat inconclusive, this study provided an investigation of data, variables, and combinations of variables that have not been investigated previously. Real-time crash prediction is expected to assist with the on-going development of connected and autonomous vehicles as the fleet mix begins to change, and new variables can be collected, and data resolution becomes greater. Real-time crash prediction models will also continue to advance highway safety as metropolitan areas continue to grow, and congestion continues to increase.
The Florida Department of Transportation has studied wrong way crashes occurring on interestate freeways and expressways throughout the state of Florida. In the past five years (2009-2013), 280 crashes have occurred on Florida's freeways and expressways resulting in more than 400 injuries and 75 deaths. This study analyzed trends and contributing factors surrounding wrong way driving on freeways and expressways. It proposed systemic countermeasures to prevent or discourage wrong way occurrences, reducing wrong way crashes and driving down fatalities on Florida's freeways and expressways.
Transportation Research Record contains the following papers: Method for identifying factors contributing to driver-injury severity in traffic crashes (Chen, WH and Jovanis, PP); Crash- and injury-outcome multipliers (Kim, K); Guidelines for identification of hazardous highway curves (Persaud, B, Retting, RA and Lyon, C); Tools to identify safety issues for a corridor safety-improvement program (Breyer, JP); Prediction of risk of wet-pavement accidents : fuzzy logic model (Xiao, J, Kulakowski, BT and El-Gindy, M); Analysis of accident-reduction factors on California state highways (Hanley, KE, Gibby, AR and Ferrara, T); Injury effects of rollovers and events sequence in single-vehicle crashes (Krull, KA, Khattack, AJ and Council, FM); Analytical modeling of driver-guidance schemes with flow variability considerations (Kaysi, I and Ail, NH); Evaluating the effectiveness of Norway's speak out! road safety campaign : The logic of causal inference in road safety evaluation studies (Elvik, R); Effect of speed, flow, and geometric characteristics on crash frequency for two-lane highways (Garber, NJ and Ehrhart, AA); Development of a relational accident database management system for Mexican federal roads (Mendoza, A, Uribe, A, Gil, GZ and Mayoral, E); Estimating traffic accident rates while accounting for traffic-volume estimation error : a Gibbs sampling approach (Davis, GA); Accident prediction models with and without trend : application of the generalized estimating equations procedure (Lord, D and Persaud, BN); Examination of methods that adjust observed traffic volumes on a network (Kikuchi, S, Miljkovic, D and van Zuylen, HJ); Day-to-day travel-time trends and travel-time prediction form loop-detector data (Kwon, JK, Coifman, B and Bickel, P); Heuristic vehicle classification using inductive signatures on freeways (Sun, C and Ritchie, SG).
This book combines comprehensive multi-angle discussions on fully connected and automated vehicle highway implementation. It covers the current progress of the works towards autonomous vehicle highway development, which encompasses the discussion on the technical, social, and policy as well as security aspects of Connected and Autonomous Vehicles (CAV) topics. This, in return, will be beneficial to a vast amount of readers who are interested in the topics of CAV, Automated Highway and Smart City, among many others. Topics include, but are not limited to, Autonomous Vehicle in the Smart City, Automated Highway, Smart-Cities Transportation, Mobility as a Service, Intelligent Transportation Systems, Data Management of Connected and Autonomous Vehicle, Autonomous Trucks, and Autonomous Freight Transportation. Brings together contributions discussing the latest research in full automated highway implementation; Discusses topics such as autonomous vehicles, intelligent transportation systems, and smart highways; Features contributions from researchers, academics, and professionals from a broad perspective.
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