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Although traffic conflict techniques have proven to be effective means for road safety analysis, they still suffer from incomplete conceptualization, observer subjectivity, and high data collection cost. To address these problems, video analysis has been increasingly applied to gain a better understanding of the behaviors of road users based on detailed motion data. However, the motion patterns underlying these data are rarely extracted to study the safety of their interactions. This article presents a vision-based method of traffic conflict detection through learning motion patterns from trajectories, for which an original algorithm was established through clustering and subsequent modeling. Using the extracted path and velocity information, we clustered trajectories hierarchically by applying an improved fuzzy K-means algorithm with a modified Hausdorff distance. Each obtained cluster was taken as a labeled set to determine the structure and train the parameters of a hidden Markov model (HMM) that encoded the spatiotemporal characteristics of the trajectories as motion patterns. Based on the targeted trajectory predictions by the learned HMMs following the conflict development, a probabilistic model was developed to estimate the collision likelihood between vehicles to identify traffic conflicts. The experimental results obtained using actual traffic videos demonstrated the applicability of the algorithms for learning motion patterns and the feasibility of the approach for traffic conflict detection. The predicted trajectories were sufficiently accurate to calculate the collision probability, which was qualified as an indicator for measuring the conflict severity. These findings will have important implications for effective improvements in active road safety.
Zongyuan Sun; Yuren Chen; Pin Wang; Shouen Fang; Boming Tang. Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction. IEEE Access 2021, 9, 34558 -34569.
AMA StyleZongyuan Sun, Yuren Chen, Pin Wang, Shouen Fang, Boming Tang. Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction. IEEE Access. 2021; 9 ():34558-34569.
Chicago/Turabian StyleZongyuan Sun; Yuren Chen; Pin Wang; Shouen Fang; Boming Tang. 2021. "Vision-Based Traffic Conflict Detection Using Trajectory Learning and Prediction." IEEE Access 9, no. : 34558-34569.
Tunnel–bridge–tunnel groups (TBTGs) are emerging roads that often involve simple road alignments, but complex driving environments. Investigating crashes occurred in TBTGs is essential for revealing the driving environment–adaptability relationships for such roads. This study seeks to analyze the crash characteristics of component sections in TBTGs with different driving environments and compare the impact of differences in the key factor on the crashes. After TBTGs were defined through a proposed safety-critical distance metric determined via visual theory and actual crash analyses, an eight-zone analytical method considering road types and lighting was developed to probe into crashes in TBTGs. The results show that the proper safety-critical distances for bridge–tunnel and tunnel–tunnel groups are 150 and 500 m, respectively. In TBTGs, the crash rate in ordinary sections is higher than that in bridges and tunnels, particularly in the access zone. The first passed tunnel witnesses a higher proportion of crashes at the access zone and transition zone than the second tunnel. The influence of bridge and tunnel ratios on crashes is related to the ratio and type of bridges and tunnels. The findings presented herein can provide evidence-based guidance for the safety design and management of TBTGs.
Zongyuan Sun; Shuo Liu; Jie Tang; Peng Wu; Boming Tang. Exploring the Impacts of Driving Environment on Crashes in Tunnel–Bridge–Tunnel Groups: An Eight-Zone Analytic Approach. Sustainability 2021, 13, 2272 .
AMA StyleZongyuan Sun, Shuo Liu, Jie Tang, Peng Wu, Boming Tang. Exploring the Impacts of Driving Environment on Crashes in Tunnel–Bridge–Tunnel Groups: An Eight-Zone Analytic Approach. Sustainability. 2021; 13 (4):2272.
Chicago/Turabian StyleZongyuan Sun; Shuo Liu; Jie Tang; Peng Wu; Boming Tang. 2021. "Exploring the Impacts of Driving Environment on Crashes in Tunnel–Bridge–Tunnel Groups: An Eight-Zone Analytic Approach." Sustainability 13, no. 4: 2272.
Mountainous freeways with high bridge and tunnel ratios are a new type of road that rarely contain many special road sections formed by various structures. The crash characteristics of the road are still unclear, but it also provides conditions for studying how various road environments affect traffic. In view of the various structures and differences in the driving environments, a scenario-based discretization method for such a road was established. The traffic-influence areas of elementary and composite structures were proposed and defined. Actual data were analyzed to investigate the crash patterns in an entire freeway and in each special section through statistical and comparative research. The results demonstrate the applicability and validity of this method. The crash rates were found to be the highest in interchange and service areas, lower in ordinary sections, and the lowest in tunnels, being mostly attributed to collisions with fixtures. The crash severity on bridges and bridge groups was significantly higher than that on the other types of road sections, being mostly attributed to single-vehicle crashes. The annual average daily traffic and driving adaptability were found to be related to crashes. The findings shed some light on the road design and traffic management implications for strengthening the traffic safety of mountainous freeways.
Zongyuan Sun; Shuo Liu; Dongxue Li; Boming Tang; Shouen Fang. Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization. PLOS ONE 2020, 15, e0237408 .
AMA StyleZongyuan Sun, Shuo Liu, Dongxue Li, Boming Tang, Shouen Fang. Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization. PLOS ONE. 2020; 15 (8):e0237408.
Chicago/Turabian StyleZongyuan Sun; Shuo Liu; Dongxue Li; Boming Tang; Shouen Fang. 2020. "Crash analysis of mountainous freeways with high bridge and tunnel ratios using road scenario-based discretization." PLOS ONE 15, no. 8: e0237408.