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Prof. Nengchao Lyu
Intelligent Transportation Systems Research Center, Wuhan University of Technology

Basic Info


Research Keywords & Expertise

0 intelligent vehicle
0 advanced driver assistance system
0 Smart highway
0 Driving Behavior And Traffic Safety
0 Cooperative Vehicle Infrastructure System

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Short Biography

Nengchao Lyu was born in Hubei, China, in 1982. He is currently an Associate Professor with the Intelligent Transportation Systems Research Center, Wuhan University of Technology, China. He visited the University of Wisconsin-Madison, as a Visiting Scholar, from 2008 to 2009. From 2012 to 2013, he worked in the Intelligent Transportation Systems Association, China. During his research career, he published more than 80 articles. His research interests include advanced driver assistance system (ADAS) and intelligent vehicle (IV), traffic safety operation management, and traffic safety evaluation. He has hosted three National Natural Science Funds related to driving behavior and traffic safety; he has completed several basic research projects sponsored by the National Science and Technology Support Plan and Ministry of Transportation. He has practical experience in safety evaluation, hosted over ten highway safety evaluation projects. He has won four technical invention awards of Hubei Province, Chinese Intelligent Transportation Association, and Chinese Artificial Intelligence Institute.

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Original research paper
Published: 18 August 2021 in IET Intelligent Transport Systems
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The deceleration lane isbefore numbers to minus signs. a critical part of the freeway, enabling vehicles to exit the expressway safely and in an orderly fashion. However, drivers are human and thus make subjective decisions while driving; as such, each driver may approach and traverse the deceleration lane differently. This variance, which can cause major traffic disruptions and collisions, can be observed – and even mitigated, as proposed herein – based on a driver's particular characteristics. To study the variances in the longitudinal vehicle positions and microscopic operating characteristics of different drivers on a freeway exit area, a field operational test involving 46 subjects was carried out to collect data on driver characteristics, vehicle motion postures, micro-driving operations, and road geometric elements. The 46 participants were observed based on experience and gender, and the mathematical statistical method was used to analyse driving differences in the deceleration lane. The results show that (1) the vehicle motion state can be divided into four operational stages on the deceleration lane: the pre-deceleration process, the dynamic adjustment process, the first braking process, and the second braking process; (2) drivers generally adopted deceleration behaviour rather than maintain uniform speed when driving in the taper; (3) about 50% of drivers braked after entering the deceleration lane; (4) male drivers and skilled drivers were more inclined to drive at a higher speed on the deceleration lane, and female drivers showed a sharp increase in braking frequency once they travelled 110–150 m downstream of the taper starting point. The results of this study provide data and insights for deceleration lane design, traffic management, and driver training.

ACS Style

Nengchao Lyu; Yugang Wang; Chaozhong Wu; Haoran Wu; Jiaqiang Wen. Exploring longitudinal driving behaviour on a freeway deceleration lane using field operational test data. IET Intelligent Transport Systems 2021, 1 .

AMA Style

Nengchao Lyu, Yugang Wang, Chaozhong Wu, Haoran Wu, Jiaqiang Wen. Exploring longitudinal driving behaviour on a freeway deceleration lane using field operational test data. IET Intelligent Transport Systems. 2021; ():1.

Chicago/Turabian Style

Nengchao Lyu; Yugang Wang; Chaozhong Wu; Haoran Wu; Jiaqiang Wen. 2021. "Exploring longitudinal driving behaviour on a freeway deceleration lane using field operational test data." IET Intelligent Transport Systems , no. : 1.

Journal article
Published: 18 March 2021 in IEEE Access
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This study aimed to examine the driving actions of at-fault older drivers, and investigate the interrelations between the unobservable factors. To reach the goal, a Bayesian bivariate ordered probit model was proposed, which addressed the driving actions of different drivers simultaneously, and accommodated the interrelations between the unobservables by covariance. The data with 27 arterials from 2014 to 2017 were collected from ArcGIS open data site maintained by Nevada Department of Transportation (NDOT). Compared to individual Bayesian random parameter ordered probit model, the proposed model outperformed according to goodness-of-fit. Results revealed that injury severity and total vehicles were potentially significant factors for actions of at-fault older drivers, while total vehicle and vehicle condition were significant for actions of not-at-fault drivers. The findings can provide potential insights for practitioners to apply the new technology and remind the driving actions of older drivers.

ACS Style

Daiquan Xiao; Xuecai Xu; Changxi Ma; Nengchao Lyu. Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis. IEEE Access 2021, 9, 45803 -45811.

AMA Style

Daiquan Xiao, Xuecai Xu, Changxi Ma, Nengchao Lyu. Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis. IEEE Access. 2021; 9 (99):45803-45811.

Chicago/Turabian Style

Daiquan Xiao; Xuecai Xu; Changxi Ma; Nengchao Lyu. 2021. "Addressing Driving Actions of At-Fault Older Drivers: Bayesian Bivariate Ordered Probit Analysis." IEEE Access 9, no. 99: 45803-45811.

Research article
Published: 19 February 2021 in Advances in Civil Engineering
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Existing studies had shown that advanced driver assistance systems (ADAS) and driver individual characteristics can significantly affect driving behavior. Therefore, it is necessary to consider these factors when building the car-following model. In this study, we established a car-following model based on risk homeostasis theory, which uses safety margin (SM) as the risk level quantization parameter. Firstly, three-way Analysis of Variance (ANOVA) was used to analyze the influencing factors of car-following behavior. The results showed that ADAS and driving experience have a significant effect on the drivers’ car-following behavior. Then, according to these two significant factors, the car-following model was established. The statistical method was used to calibrate the parameter reaction response τ. Other four parameters (SMDL, SMDH, α1, and α2) were calibrated using a classical genetic algorithm, and the effects of ADAS and driving experience in these four parameters were analyzed using T-test. Finally, the proposed model was compared with the GHR model, and the result showed that the proposed model has a smaller Root Mean Square Error (RMSE) than the GHR model. The proposed model is a method of simulating different driving behaviors that are affected by ADAS and individual characteristics. Considering more driver individual characteristics, such as driving style, is the future research goal.

ACS Style

Yugang Wang; Nengchao Lyu. A Car-Following Model Based on Safety Margin considering ADAS and Driving Experience. Advances in Civil Engineering 2021, 2021, 1 -10.

AMA Style

Yugang Wang, Nengchao Lyu. A Car-Following Model Based on Safety Margin considering ADAS and Driving Experience. Advances in Civil Engineering. 2021; 2021 ():1-10.

Chicago/Turabian Style

Yugang Wang; Nengchao Lyu. 2021. "A Car-Following Model Based on Safety Margin considering ADAS and Driving Experience." Advances in Civil Engineering 2021, no. : 1-10.

Journal article
Published: 05 February 2021 in International Journal of Environmental Research and Public Health
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In complex traffic environments, collision warning systems that rely only on in-vehicle sensors are limited in accuracy and range. Vehicle-to-infrastructure (V2I) communication systems, however, offer more robust information exchange, and thus, warnings. In this study, V2I was used to analyze side-collision warning models at non-signalized intersections: A novel time-delay side-collision warning model was developed according to the motion compensation principle. This novel time-delay model was compared with and verified against a traditional side-collision warning model. Using a V2I-oriented simulated driving platform, three vehicle-vehicle collision scenarios were designed at non-signalized intersections. Twenty participants were recruited to conduct simulated driving experiments to test and verify the performance of each collision warning model. The results showed that compared with no warning system, both side-collision warning models reduced the proportion of vehicle collisions. In terms of efficacy, the traditional model generated an effective warning in 84.2% of cases, while the novel time-delay model generated an effective warning in 90.2%. In terms of response time and conflict time difference, the traditional model gave a longer response time of 0.91 s (that of the time-delay model is 0.78 s), but the time-delay model reduced the driving risk with a larger conflict time difference. Based on an analysis of driver gaze change post-warning, the statistical results showed that the proportion of effective gaze changes reached 84.3%. Based on subjective evaluations, drivers reported a higher degree of acceptance of the time-delay model. Therefore, the time-delay side-collision warning model for non-signalized intersections proposed herein can improve the applicability and efficacy of warning systems in such complex traffic environments and provide reference for safety applications in V2I systems.

ACS Style

Nengchao Lyu; Jiaqiang Wen; Chaozhong Wu. Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication. International Journal of Environmental Research and Public Health 2021, 18, 1520 .

AMA Style

Nengchao Lyu, Jiaqiang Wen, Chaozhong Wu. Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication. International Journal of Environmental Research and Public Health. 2021; 18 (4):1520.

Chicago/Turabian Style

Nengchao Lyu; Jiaqiang Wen; Chaozhong Wu. 2021. "Novel Time-Delay Side-Collision Warning Model at Non-Signalized Intersections Based on Vehicle-to-Infrastructure Communication." International Journal of Environmental Research and Public Health 18, no. 4: 1520.

Journal article
Published: 04 September 2020 in IEEE Transactions on Intelligent Transportation Systems
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Side collisions caused by sudden vehicle cut-ins comprise a significant proportion of traffic accidents. Due to the complex and dynamic nature of traffic environments, the warning algorithms in advanced driving assistant systems (ADAS) often misjudge and misdiagnose risk and omit necessary warnings, because they rely solely on the sensing information of the single vehicle equipped with ADAS and have limited insights from and communication with the surrounding vehicles and traffic environment. To improve the effectiveness of ADAS in cut-in scenarios, this study established a collision warning model in a vehicle-to-vehicle (V2V) communication environment. Firstly, based on the support vector machine-recursive feature elimination (SVM-RFE) lane-change intent-recognition model, the lane-change feasibility and the change rate of the lateral offset, the logical ``and'' was used to establish a lane-change behavior prediction model, and a trajectory prediction model was established based on the long short-term memory (LSTM). Then, based on the proposed comprehensive prediction model for lane-change behavior, the driving trajectory prediction model, and the oriented bounding box (OBB) detection algorithm, a collision warning model was established for a V2V environment. Finally, based on a driving simulation platform and a real-world vehicle test, a cut-in experiment in a V2V environment was designed and implemented. By comparing the warning confusion matrix and warning time, it was found that the proposed cut-in collision warning model is superior to the traditional collision warning model. The results of this study can provide new modeling ideas and a theoretical basis for ADAS to further optimize for a cut-in scenario.

ACS Style

Nengchao Lyu; Jiaqiang Wen; Zhicheng Duan; Chaozhong Wu. Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment. IEEE Transactions on Intelligent Transportation Systems 2020, PP, 1 -16.

AMA Style

Nengchao Lyu, Jiaqiang Wen, Zhicheng Duan, Chaozhong Wu. Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment. IEEE Transactions on Intelligent Transportation Systems. 2020; PP (99):1-16.

Chicago/Turabian Style

Nengchao Lyu; Jiaqiang Wen; Zhicheng Duan; Chaozhong Wu. 2020. "Vehicle Trajectory Prediction and Cut-In Collision Warning Model in a Connected Vehicle Environment." IEEE Transactions on Intelligent Transportation Systems PP, no. 99: 1-16.

Journal article
Published: 30 June 2020 in IET Intelligent Transport Systems
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Operating speed is a basic measure for studying the safety of freeways. However, whether the designed speed matches the actual operating speed of large vehicles is worth studying. As such, this study included a large number of aggregated vehicle global positioning system (GPS) datasets used to analyse the actual speed of large vehicles under different alignment conditions on the freeway; and supplement the theoretical operating speed model. Using the traditional operating speed prediction model, the theoretical speed of each section was calculated. Two months of vehicle GPS data from freeways was extracted from a vehicle monitoring platform, the actual operating speed of each section was obtained. The similarities and differences between the theoretical operating speed and the actual vehicle speed on the horizontal and vertical curves were analysed and compared. The results indicate that the actual speed of large vehicles was obviously higher than the theoretical speed operating downhill. When the vehicle was operating uphill, the vehicle's actual speed was significantly lower than that of the theoretical speed. The actual vehicle operating speed data can be used as a new method for evaluating the safety of large vehicles on the freeway.

ACS Style

Wei Hou; Nengchao Lyu; Zhaoxin Liu; Xu Wang. Modelling large vehicles operating speed characteristics on freeway alignment based on aggregated GPS data. IET Intelligent Transport Systems 2020, 14, 857 -865.

AMA Style

Wei Hou, Nengchao Lyu, Zhaoxin Liu, Xu Wang. Modelling large vehicles operating speed characteristics on freeway alignment based on aggregated GPS data. IET Intelligent Transport Systems. 2020; 14 (8):857-865.

Chicago/Turabian Style

Wei Hou; Nengchao Lyu; Zhaoxin Liu; Xu Wang. 2020. "Modelling large vehicles operating speed characteristics on freeway alignment based on aggregated GPS data." IET Intelligent Transport Systems 14, no. 8: 857-865.

Journal article
Published: 19 April 2020 in Sensors
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Driving risk varies substantially according to many factors related to the driven vehicle, environmental conditions, and drivers. This study explores the contributing historical factors of driving risk with hierarchical clustering analysis and the quasi-Poisson regression model. The dataset of the study was collected from two sources: naturalistic driving experiments and self-reports. The drivers who participated in the naturalistic driving experiment were categorized into four risk groups according to their near-crash frequency with the hierarchical clustering method. Moreover, a quasi-Poisson model was used to identify the essential factors of individual driving risk. The findings of this study indicated that historical driving factors have substantial impacts on individual risk of drivers. These factors include the total number of miles driven, the driver’s age, the number of illegal parking (past three years), the number of over-speeding (past three years) and passing red lights (past three years). The outcome of the study can help transportation officials, educators, and researchers to consider the influencing factors on individual driving risk and can give insights and provide suggestions to improve driving safety.

ACS Style

Hasan A.H. Naji; Qingji Xue; Ke Zheng; Nengchao Lyu. Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model. Sensors 2020, 20, 2331 .

AMA Style

Hasan A.H. Naji, Qingji Xue, Ke Zheng, Nengchao Lyu. Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model. Sensors. 2020; 20 (8):2331.

Chicago/Turabian Style

Hasan A.H. Naji; Qingji Xue; Ke Zheng; Nengchao Lyu. 2020. "Investigating the Significant Individual Historical Factors of Driving Risk Using Hierarchical Clustering Analysis and Quasi-Poisson Regression Model." Sensors 20, no. 8: 2331.

Journal article
Published: 03 March 2020 in Journal of Safety Research
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Introduction: Highway expansions and upgrades are often required to increase road network capacity. The widening of one side of a highway, referred to as ‘one-side widening,’ is sometimes implemented in these highway expansion projects. During one-side widening, to save costs, openings can be configured on existing medians (as opposed to removing the existing medians altogether). The median openings allow vehicles in the outer lanes to enter the inner lanes, but they also raise safety concerns and may require alternate open-median management strategies for traffic authorities. There is little existing research that has evaluated the safety effect of these open-median management strategies. Method: To bridge this gap, this study proposes a procedure that evaluates the safety of open-median management strategies for one-side widened highways. The proposed procedure was implemented through driving simulation experiments on a section of Binlai Freeway in Shandong, China. First, the minimum location requirements for median openings were determined by calculating the short length of the weaving segment. Then, simulation tests were carried out to observe driving performance and workload measures. Results: The results indicate that the procedure successfully evaluates the safety effect of open-median management strategies for one-side widened freeways. It was also found that driving performance and workload are sensitive to the opening length and traffic flow. Conclusions: Therefore, median opening placement should be carefully selected in consideration of not only driving performance and workload but also traffic volume predictions. Practical Applications: The findings in this study can guide open-median management strategies for traffic safety one-side widened highways.

ACS Style

Xu Wang; Yue Cao; Peiyu Jiang; Lei Niu; Nengchao Lyu. The safety effect of open-median management on one-side widened freeways: A driving simulation evaluation. Journal of Safety Research 2020, 73, 57 -67.

AMA Style

Xu Wang, Yue Cao, Peiyu Jiang, Lei Niu, Nengchao Lyu. The safety effect of open-median management on one-side widened freeways: A driving simulation evaluation. Journal of Safety Research. 2020; 73 ():57-67.

Chicago/Turabian Style

Xu Wang; Yue Cao; Peiyu Jiang; Lei Niu; Nengchao Lyu. 2020. "The safety effect of open-median management on one-side widened freeways: A driving simulation evaluation." Journal of Safety Research 73, no. : 57-67.

Research article
Published: 30 May 2019 in Advances in Mechanical Engineering
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Road alignment, traffic flow, and sign information interact and create complex traffic situations. To evaluate the effects of freeway horizontal radius, slope grade, traffic flow, and sign information on subjective driving workload and performance, a simulated driving experiment based on an orthogonal test design was conducted. The National Aeronautics and Space Administration Task Load Index was used to measure self-reported workload. Data regarding speed and lane deviation were collected through the driving simulator. A multivariate analysis of variance results indicated that the radius of the horizontal curve significantly influenced workload score, average speed, and lane deviation. It was observed that in a high workload environment, participants exerted more effort on the driving task; however, driving performance still decreased. Although there was no significant correlation between slope grade and subjective driving workload or performance, the primary effect of slope grade on workload and average speed was statistically significant. The amount of sign information significantly influenced the driver’s perceived workload; however, it did not significantly impact driving performance. In addition, the low correlation coefficient between subjective workload and performance was obtained. These research findings can provide insights for the design of freeway alignments and traffic signs to maintain optimal workload and minimize safety risks.

ACS Style

Lian Xie; Chaozhong Wu; Nengchao Lyu; Zhicheng Duan. Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance. Advances in Mechanical Engineering 2019, 11, 1 .

AMA Style

Lian Xie, Chaozhong Wu, Nengchao Lyu, Zhicheng Duan. Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance. Advances in Mechanical Engineering. 2019; 11 (5):1.

Chicago/Turabian Style

Lian Xie; Chaozhong Wu; Nengchao Lyu; Zhicheng Duan. 2019. "Studying the effects of freeway alignment, traffic flow, and sign information on subjective driving workload and performance." Advances in Mechanical Engineering 11, no. 5: 1.

Journal article
Published: 03 May 2019 in IEEE Access
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In conditionally automated driving, due to the possible system limits, the driver is required to take-over the vehicle control if a so-called take-over request is issued. This study investigates the effect of scenarios and secondary tasks on the take-over performance and safety. The experiment was conducted in a real vehicle-based driving simulator. The manual driving, the 1-back cognitive secondary task and the letter game task were tested. Participants experienced three different traffic scenarios, including a non-critical scenario and two critical scenarios. Results indicated the scenarios and secondary tasks impacts taking-over characteristics; at the meanwhile, a strong influence of the take-over time and driver’s workload on the take-over safety. Specifically, the steering reaction was generally slower than the braking reaction, indicating that the lateral operation requires more cognitive and decision-making time. In extreme cases, the braking operation is not sufficient to ensure safety and the steering operation must be taken into account. When obstacles are not easy to detect, or when the driver is engaged in a visual secondary task, the steering reaction time increases significantly. This study can provide data support for take-over safety evaluation of conditionally automated driving.

ACS Style

Chaozhong Wu; Haoran Wu; Nengchao Lyu; Mengfan Zheng. Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving. IEEE Access 2019, 7, 136924 -136933.

AMA Style

Chaozhong Wu, Haoran Wu, Nengchao Lyu, Mengfan Zheng. Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving. IEEE Access. 2019; 7 (99):136924-136933.

Chicago/Turabian Style

Chaozhong Wu; Haoran Wu; Nengchao Lyu; Mengfan Zheng. 2019. "Take-Over Performance and Safety Analysis Under Different Scenarios and Secondary Tasks in Conditionally Automated Driving." IEEE Access 7, no. 99: 136924-136933.

Journal article
Published: 17 September 2018 in Accident Analysis & Prevention
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Deceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management.

ACS Style

Nengchao Lyu; Yue Cao; Chaozhong Wu; Jin Xu; Lian Xie. The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data. Accident Analysis & Prevention 2018, 121, 82 -93.

AMA Style

Nengchao Lyu, Yue Cao, Chaozhong Wu, Jin Xu, Lian Xie. The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data. Accident Analysis & Prevention. 2018; 121 ():82-93.

Chicago/Turabian Style

Nengchao Lyu; Yue Cao; Chaozhong Wu; Jin Xu; Lian Xie. 2018. "The effect of gender, occupation and experience on behavior while driving on a freeway deceleration lane based on field operational test data." Accident Analysis & Prevention 121, no. : 82-93.

Journal article
Published: 13 August 2018 in Sustainability
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With the considerable increase in ownership of motor vehicles, traffic crashes have become a challenge. This paper presents a study of naturalistic driving conducted to collect driving data. The experiments were performed on different road types in the city of Wuhan in China. The collected driving data were used to develop a near-crash database, which covers driving behavior, near-crash factors, driving environment, time, demographics, and experience. A new definition of near-crash events is also proposed. The new definition considers potential risks in driving behavior, such as braking pressure, time headway, and deceleration. A clustering analysis was carried out through a K-means algorithm to classify near-crash events based on their risk level. In addition, a mixed-ordered logit model was used to examine the contributing factors associated with the driving risk of near-crash events. The results indicate that ten factors significantly affect the driving risk of near-crash events: deceleration average, vehicle kinetic energy, near-crash causes, congestion on roads, time of day, driving miles, road types, weekend, age, and experience. The findings may be used by transportation planners to understand the factors that influence driving risk and may provide valuable insights and helpful suggestions for improving transportation rules and reducing traffic collisions thus making roads safer.

ACS Style

Hasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability 2018, 10, 2868 .

AMA Style

Hasan. Naji, Qingji Xue, Nengchao Lyu, Chaozhong Wu, Ke Zheng. Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model. Sustainability. 2018; 10 (8):2868.

Chicago/Turabian Style

Hasan. Naji; Qingji Xue; Nengchao Lyu; Chaozhong Wu; Ke Zheng. 2018. "Evaluating the Driving Risk of Near-Crash Events Using a Mixed-Ordered Logit Model." Sustainability 10, no. 8: 2868.

Journal article
Published: 28 February 2017 in Sensors
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In road traffic accidents, the analysis of a vehicle’s collision angle plays a key role in identifying a traffic accident’s form and cause. However, because accurate estimation of vehicle collision angle involves many factors, it is difficult to accurately determine it in cases in which less physical evidence is available and there is a lack of monitoring. This paper establishes the mathematical relation model between collision angle, deformation, and normal vector in the collision region according to the equations of particle deformation and force in Hooke’s law of classical mechanics. At the same time, the surface reconstruction method suitable for a normal vector solution is studied. Finally, the estimation model of vehicle collision angle is presented. In order to verify the correctness of the model, verification of multi-angle collision experiments and sensitivity analysis of laser scanning precision for the angle have been carried out using three-dimensional (3D) data obtained by a 3D laser scanner in the collision deformation zone. Under the conditions with which the model has been defined, validation results show that the collision angle is a result of the weighted synthesis of the normal vector of the collision point and the weight value is the deformation of the collision point corresponding to normal vectors. These conclusions prove the applicability of the model. The collision angle model proposed in this paper can be used as the theoretical basis for traffic accident identification and cause analysis. It can also be used as a theoretical reference for the study of the impact deformation of elastic materials.

ACS Style

Nengchao Lyu; Gang Huang; Chaozhong Wu; Zhicheng Duan; Pingfan Li. Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data. Sensors 2017, 17, 482 .

AMA Style

Nengchao Lyu, Gang Huang, Chaozhong Wu, Zhicheng Duan, Pingfan Li. Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data. Sensors. 2017; 17 (3):482.

Chicago/Turabian Style

Nengchao Lyu; Gang Huang; Chaozhong Wu; Zhicheng Duan; Pingfan Li. 2017. "Modeling Vehicle Collision Angle in Traffic Crashes Based on Three-Dimensional Laser Scanning Data." Sensors 17, no. 3: 482.

Comparative study
Published: 17 February 2017 in International Journal of Environmental Research and Public Health
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Complex traffic situations and high driving workload are the leading contributing factors to traffic crashes. There is a strong correlation between driving performance and driving workload, such as visual workload from traffic signs on highway off-ramps. This study aimed to evaluate traffic safety by analyzing drivers’ behavior and performance under the cognitive workload in complex environment areas. First, the driving workload of drivers was tested based on traffic signs with different quantities of information. Forty-four drivers were recruited to conduct a traffic sign cognition experiment under static controlled environment conditions. Different complex traffic signs were used for applying the cognitive workload. The static experiment results reveal that workload is highly related to the amount of information on traffic signs and reaction time increases with the information grade, while driving experience and gender effect are not significant. This shows that the cognitive workload of subsequent driving experiments can be controlled by the amount of information on traffic signs; Second, driving characteristics and driving performance were analyzed under different secondary task driving workload levels using a driving simulator. Drivers were required to drive at the required speed on a designed highway off-ramp scene. The cognitive workload was controlled by reading traffic signs with different information, which were divided into four levels. Drivers had to make choices by pushing buttons after reading traffic signs. Meanwhile, the driving performance information was recorded. Questionnaires on objective workload were collected right after each driving task. The results show that speed maintenance and lane deviations are significantly different under different levels of cognitive workload, and the effects of driving experience and gender groups are significant. The research results can be used to analyze traffic safety in highway environments, while considering more drivers’ cognitive and driving performance.

ACS Style

Nengchao Lyu; Lian Xie; Chaozhong Wu; Qiang Fu; Chao Deng. Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China. International Journal of Environmental Research and Public Health 2017, 14, 203 .

AMA Style

Nengchao Lyu, Lian Xie, Chaozhong Wu, Qiang Fu, Chao Deng. Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China. International Journal of Environmental Research and Public Health. 2017; 14 (2):203.

Chicago/Turabian Style

Nengchao Lyu; Lian Xie; Chaozhong Wu; Qiang Fu; Chao Deng. 2017. "Driver’s Cognitive Workload and Driving Performance under Traffic Sign Information Exposure in Complex Environments: A Case Study of the Highways in China." International Journal of Environmental Research and Public Health 14, no. 2: 203.