Ying Yan1, Youhua Dai2, Xiaodong Li1, Jinjun Tang3, Zhongyin Guo4,5. 1. School of Automobile, Key Laboratory of Automobile Transportation Safety Support Technology, Chang'an University, Xi'an, China. 2. Department of Operation Management, Guangdong Nanyue Transportation Investment & Construction Co. Ltd, Guangzhou, China. 3. School of Traffic and Transportation Engineering, Smart Transport Key Laboratory of Hunan Province, Central South University, Changsha, China. 4. College of Transportation Engineering, Tongji University, Shanghai, China. 5. Department of Transportation Institute, Shandong Road Region Safety and Emergency Support Laboratory, Jinan, China.
Abstract
Objective: Driving behavior is the key feature for determining the nature of traffic stream qualities and reflecting the risk of operating environments. However, evaluating the driving risk accurately and practically in continuous tunnels (tunnels with a space more than 250 m and less than 1000 m) still faces severe challenges due to the complex driving conditions. The objective of this study is to predict the driving risk indicators and determine different risk levels. Methods: The naturalistic driving system equipped with a road environment and driving behavior data acquisition system combined with the fixed-point test method was used for data collection in 130 tunnels on four highways. A traditional AASHTO braking model and convex hull algorithm were adopted to predict the critical safety speed and the critical time headway of each risk feature point in tunnels. According to the risk constraints under free-flow, car-following and lane-changing conditions, the average traffic flow risk index (TFRI) representing six risk levels and the safety threshold of the corresponding risk indicators were determined. Results: The findings of this study revealed that the critical safety speed at nighttime is slower than in other daytime conditions in continuous tunnels. The time headway slightly changes under 90 km/h. As the speed continues to increase, speed has a significant influence on the critical time headway. The only reliable interaction involved the different adverse weather conditions on the mean critical safety speed in the continuous tunnels (short plus long) (F = 9.730, p＜0.05) and single long tunnels (F = 12.365, p＜0.05).Conclusions: It can be concluded that driving behaviors significantly vary in different tunnel risk feature points and the combined effect of high speed and luminance variation may result in high driving risk. The performance validation indicted that the risk assessment level determined by the proposed approach is consistent with the real safety situations. The study provides an effective and generally acceptable method for identifying driving risk criteria that can also be applied for traffic management and safety countermeasures with a view to possible implementation in continuous tunnels.
Objective: Driving behavior is the key feature for determining the nature of traffic stream qualities and reflecting the risk of operating environments. However, evaluating the driving risk accurately and practically in continuous tunnels (tunnels with a space more than 250 m and less than 1000 m) still faces severe challenges due to the complex driving conditions. The objective of this study is to predict the driving risk indicators and determine different risk levels. Methods: The naturalistic driving system equipped with a road environment and driving behavior data acquisition system combined with the fixed-point test method was used for data collection in 130 tunnels on four highways. A traditional AASHTO braking model and convex hull algorithm were adopted to predict the critical safety speed and the critical time headway of each risk feature point in tunnels. According to the risk constraints under free-flow, car-following and lane-changing conditions, the average traffic flow risk index (TFRI) representing six risk levels and the safety threshold of the corresponding risk indicators were determined. Results: The findings of this study revealed that the critical safety speed at nighttime is slower than in other daytime conditions in continuous tunnels. The time headway slightly changes under 90 km/h. As the speed continues to increase, speed has a significant influence on the critical time headway. The only reliable interaction involved the different adverse weather conditions on the mean critical safety speed in the continuous tunnels (short plus long) (F = 9.730, p＜0.05) and single long tunnels (F = 12.365, p＜0.05).Conclusions: It can be concluded that driving behaviors significantly vary in different tunnel risk feature points and the combined effect of high speed and luminance variation may result in high driving risk. The performance validation indicted that the risk assessment level determined by the proposed approach is consistent with the real safety situations. The study provides an effective and generally acceptable method for identifying driving risk criteria that can also be applied for traffic management and safety countermeasures with a view to possible implementation in continuous tunnels.