Traffic Operations Division

Austin, TX, United States

Traffic Operations Division

Austin, TX, United States

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Chen C.,University of Hawaii at Manoa | Zhang G.,University of Hawaii at Manoa | Liu X.C.,University of Utah | Ci Y.,Harbin Institute of Technology | And 4 more authors.
Accident Analysis and Prevention | Year: 2016

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention. © 2016 Elsevier Ltd


PubMed | Traffic Operations Division, Beijing University of Technology, Central South University, University of Hawaii at Manoa and 2 more.
Type: | Journal: Accident; analysis and prevention | Year: 2016

There is a high potential of severe injury outcomes in traffic crashes on rural interstate highways due to the significant amount of high speed traffic on these corridors. Hierarchical Bayesian models are capable of incorporating between-crash variance and within-crash correlations into traffic crash data analysis and are increasingly utilized in traffic crash severity analysis. This paper applies a hierarchical Bayesian logistic model to examine the significant factors at crash and vehicle/driver levels and their heterogeneous impacts on driver injury severity in rural interstate highway crashes. Analysis results indicate that the majority of the total variance is induced by the between-crash variance, showing the appropriateness of the utilized hierarchical modeling approach. Three crash-level variables and six vehicle/driver-level variables are found significant in predicting driver injury severities: road curve, maximum vehicle damage in a crash, number of vehicles in a crash, wet road surface, vehicle type, driver age, driver gender, driver seatbelt use and driver alcohol or drug involvement. Among these variables, road curve, functional and disabled vehicle damage in crash, single-vehicle crashes, female drivers, senior drivers, motorcycles and driver alcohol or drug involvement tend to increase the odds of drivers being incapably injured or killed in rural interstate crashes, while wet road surface, male drivers and driver seatbelt use are more likely to decrease the probability of severe driver injuries. The developed methodology and estimation results provide insightful understanding of the internal mechanism of rural interstate crashes and beneficial references for developing effective countermeasures for rural interstate crash prevention.


Chen C.,University of New Mexico | Zhang G.,University of New Mexico | Tarefder R.,University of New Mexico | Ma J.,Traffic Operations Division | And 2 more authors.
Accident Analysis and Prevention | Year: 2015

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance. © 2015 Elsevier Ltd. All rights reserved.


PubMed | Beijing University of Technology, University of Cincinnati, University of New Mexico and Traffic Operations Division
Type: | Journal: Accident; analysis and prevention | Year: 2015

Rear-end crash is one of the most common types of traffic crashes in the U.S. A good understanding of its characteristics and contributing factors is of practical importance. Previously, both multinomial Logit models and Bayesian network methods have been used in crash modeling and analysis, respectively, although each of them has its own application restrictions and limitations. In this study, a hybrid approach is developed to combine multinomial logit models and Bayesian network methods for comprehensively analyzing driver injury severities in rear-end crashes based on state-wide crash data collected in New Mexico from 2010 to 2011. A multinomial logit model is developed to investigate and identify significant contributing factors for rear-end crash driver injury severities classified into three categories: no injury, injury, and fatality. Then, the identified significant factors are utilized to establish a Bayesian network to explicitly formulate statistical associations between injury severity outcomes and explanatory attributes, including driver behavior, demographic features, vehicle factors, geometric and environmental characteristics, etc. The test results demonstrate that the proposed hybrid approach performs reasonably well. The Bayesian network reference analyses indicate that the factors including truck-involvement, inferior lighting conditions, windy weather conditions, the number of vehicles involved, etc. could significantly increase driver injury severities in rear-end crashes. The developed methodology and estimation results provide insights for developing effective countermeasures to reduce rear-end crash injury severities and improve traffic system safety performance.


Wu Y.-P.,Beijing University of Technology | Zhao X.-H.,Beijing University of Technology | Rong J.,Traffic Operations Division | Ma J.-M.,Traffic Operations Division
Journal of Beijing Institute of Technology (English Edition) | Year: 2012

In China, chevron alignment signs are widely used as guide signs to guide driving direction in curve roads, but the impact of guide chevron alignment sign on driver performance was not explicit. The results of a driving simulator study that focused on the influence of guide chevron alignment signs on driver performance was described. The data of driver performance were respectively collected in a simulated ramp with or without guide chevron alignment signs. The analysis indexes mainly included speed, lateral placement, the angle of steering, times of brake and accelerator. The results indicated that the distance of vehicles deviating right from the center of the lane would be larger, and driver's will to control speed was higher in the scenario with guide chevron alignment signs, which demonstrated that guide chevron alignment signs did affect driver performance, and the influence was positive to driving safety. © right.


Wu Y.,Beijing University of Technology | Zhao X.,Beijing University of Technology | Rong J.,Beijing University of Technology | Ma J.,Traffic Operations Division
Transportation Research Record | Year: 2013

In China, the chevron alignment sign (a vertical rectangle with a white arrow and border on a blue background) has been widely used on roadway curves to provide advance warning and positive guidance through curves. Chevron alignment signs are retroreflective guidance devices installed near roadway edges indicating roadway alignment. But the effects of the China chevron alignment signs on drivers' eye movements, driving performance, and stress have rarely been studied. Few guidelines have been established for the size and application of chevron signs in China. This paper reports on a driving simulator experiment that measured the data on drivers' eye movements, inputs, and electrocardiograms through an urban expressway ramp with and without chevron alignment signs. A comparative analysis was conducted to examine the changes in drivers' eye movements, driving performance, and heart rates. Results show that drivers pay more attention to the roadside near chevrons; they are also more relaxed and tend to drop their speed more when chevrons are present. This finding indicates that chevron alignment signs do provide advance warnings and positive guidance and make drivers tend to drop speed more through curves, which improves safety and uniformity in curve delineation on urban expressway ramps.


PubMed | Beijing University of Technology and Traffic Operations Division
Type: Journal Article | Journal: International journal of environmental research and public health | Year: 2016

The objective of this paper is to explore the effects of longitudinal speed reduction markings (LSRMs) on vehicle maneuvering and drivers operation performance on interchange connectors with different radii. Empirical data were collected in a driving simulator. Indicators-relative speed change, standard deviation of acceleration, and gas/brake pedal power-were proposed to characterize driving behavior. Statistical results revealed that LSRMs could reduce vehicles travel speed and limit drivers willingness to increase speed in the entire connector. To probe the impacts of LSRMs, the connecter was split into four even sections. Effects of LSRMs on driving behavior were stronger in the second and the final sections of connectors. LSRMs also enhanced drivers adaptability in the first three quarters of a connector when the radius was 50 m. Drivers gas pedal operation would be impacted by LSRMs in the entire connector when the radius was 50 m. LSRMs could only make drivers press brake pedal more frequently in the second section with 80 m and 100 m radius. In the second quarter section of a connector-from the FQP (the first quartile point) to the MC (the middle point of curve)-LSRMs have better effects on influencing vehicle maneuvering and drivers operation performance.

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