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Wang X.,Tongji University | Wang X.,Road and Traffic Key Laboratory | Xu C.,Southwest Jiaotong University | Xu C.,National United Engineering Laboratory of Integrated and Intelligent Transportation
Accident Analysis and Prevention | Year: 2015

Objectives: Drowsy driving is a serious highway safety problem. If drivers could be warned before they became too drowsy to drive safely, some drowsiness-related crashes could be prevented. The presentation of timely warnings, however, depends on reliable detection. To date, the effectiveness of drowsiness detection methods has been limited by their failure to consider individual differences. The present study sought to develop a drowsiness detection model that accommodates the varying individual effects of drowsiness on driving performance. Methods: Nineteen driving behavior variables and four eye feature variables were measured as participants drove a fixed road course in a high fidelity motion-based driving simulator after having worked an 8-h night shift. During the test, participants were asked to report their drowsiness level using the Karolinska Sleepiness Scale at the midpoint of each of the six rounds through the road course. A multilevel ordered logit (MOL) model, an ordered logit model, and an artificial neural network model were used to determine drowsiness. Results: The MOL had the highest drowsiness detection accuracy, which shows that consideration of individual differences improves the models' ability to detect drowsiness. According to the results, percentage of eyelid closure, average pupil diameter, standard deviation of lateral position and steering wheel reversals was the most important of the 23 variables. Conclusion: The consideration of individual differences on a drowsiness detection model would increase the accuracy of the model's detection accuracy. © 2015 Elsevier Ltd.

Wang X.,Road and Traffic Key Laboratory | Wang X.,Tongji University | Yu R.,Road and Traffic Key Laboratory | Yu R.,Tongji University | Zhong C.,Texas A&M University
Transportation Research Part F: Traffic Psychology and Behaviour | Year: 2016

Red-Light-Running (RLR) is the major cause of severe injury crashes at signalized intersections for both China and the US. As several studies have been conducted to identify the influencing factors of RLR behavior in the US, no similar studies exist in China. To fill this gap, this study was conducted to identify the key factors that affect RLR and compare the contributing factors between US and China. Data were collected through field observations and video recordings; four intersections in Shanghai were selected as the study sites. Both RLR drivers and comparison drivers, who had the opportunity to run the light but did not, were identified. Based on the collected data, preliminary analyses were firstly conducted to identify the features of the RLR and comparison groups. It was determined that: around 57% of RLR crossed the stop line during the 0-0.4 second time interval after red-light onset, and the numbers of red light violators decreased as the time increased; among the RLR vehicles, 38% turned left and 62% went straight; and at the onset of red, about 88% of RLR vehicles were in the middle of a vehicle platoon. Furthermore, in order to compare the RLR group and non-RLR group, two types of logistic regression models were developed. The ordinary logistic regression model was developed to identify the significant variables from the aspects of driver characteristics, driving conditions, and vehicle types. It was concluded that RLR drivers are more likely to be male, have local license plates, and are driving passenger vehicles but without passengers. Large traffic volume also increased the likelihood of RLR. However, the ordinary logistic regression model only considers influencing factors at the vehicle level: different intersection design and signal settings may also have impact on RLR behaviors. Therefore, in order to account for unobserved heterogeneity among different types of intersections, a random effects logistic regression model was adopted. Through the model comparisons, it has been identified that the model goodness-of-fit was substantially improved through considering the heterogeneity effects at intersections. Finally, benefits of this study and the analysis results were discussed. © 2015 Elsevier Ltd. All rights reserved.

Wang X.,Tongji University | Wang X.,Road and Traffic Key Laboratory | Wu X.,Tongji University | Wu X.,Road and Traffic Key Laboratory | And 3 more authors.
Accident Analysis and Prevention | Year: 2013

The analysis of road network designs can provide useful information to transportation planners as they seek to improve the safety of road networks. The objectives of this study were to compare and define the effective road network indices and to analyze the relationship between road network structure and traffic safety at the level of the Traffic Analysis Zone (TAZ). One problem in comparing different road networks is establishing criteria that can be used to scale networks in terms of their structures. Based on data from Orange and Hillsborough Counties in Florida, road network structural properties within TAZs were scaled using 3 indices: Closeness Centrality, Betweenness Centrality, and Meshedness Coefficient. The Meshedness Coefficient performed best in capturing the structural features of the road network. Bayesian Conditional Autoregressive (CAR) models were developed to assess the safety of various network configurations as measured by total crashes, crashes on state roads, and crashes on local roads. The models' results showed that crash frequencies on local roads were closely related to factors within the TAZs (e.g., zonal network structure, TAZ population), while crash frequencies on state roads were closely related to the road and traffic features of state roads. For the safety effects of different networks, the Grid type was associated with the highest frequency of crashes, followed by the Mixed type, the Loops & Lollipops type, and the Sparse type. This study shows that it is possible to develop a quantitative scale for structural properties of a road network, and to use that scale to calculate the relationships between network structural properties and safety. © 2013 Elsevier Ltd. All rights reserved.

Wang X.,Tongji University | Wang X.,Road and Traffic Key Laboratory | Wang T.,Tongji University | Wang T.,Road and Traffic Key Laboratory | And 2 more authors.
Accident Analysis and Prevention | Year: 2015

Combined horizontal and vertical alignments are frequently used in mountainous freeways in China; however, design guidelines that consider the safety impact of combined alignments are not currently available. Past field studies have provided some data on the relationship between road alignment and safety, but the effects of differing combined alignments on either lateral acceleration or safety have not systematically examined. The primary reason for this void in past research is that most of the prior studies used observational methods that did not permit control of the key variables. A controlled parametric study is needed that examines lateral acceleration as drivers adjust their speeds across a range of combined horizontal and vertical alignments. Such a study was conducted in Tongji University's eight-degree-of-freedom driving simulator by replicating the full range of combined alignments used on a mountainous freeway in China. Multiple linear regression models were developed to estimate the effects of the combined alignments on lateral acceleration. Based on these models, domains were calculated to illustrate the results and to assist engineers to design safer mountainous freeways. © 2015 Elsevier Ltd.

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