Traffic safety analysis of inter-tunnel weaving section with conflict prediction models

Zhou

Transportation Research Record: Journal of the Transportation R

et al. 2023

This study aims to assess the traffic risk of the lane-changing (LC) process in the urban inter-tunnel weaving (UIW) segment. Time to collision (TTC) and extended time to collision (ETTC) are selected as indices for traffic risk assessment. An instantaneous traffic risk level classification method integrating Pareto’s law and the K-means clustering algorithm is proposed. Based on the classification results, the study also proposes an overall LC risk assessment method. Field-collected trajectory data are used to evaluate and characterize the traffic risk associated with the LC process. The instantaneous traffic risk analysis shows that the high-risk state accounts for a high percentage of the LC process in the UIW segment, and the front vehicle on the starting lane has the highest potential for conflict with the target vehicle. The overall risk index analysis shows that the risk distribution of the LC process is significantly clustered in the weaving segment and that the safety level of the UIW segment needs to be improved. This study quantifies the safety level and analyzes the characteristics of traffic risks of the LC process in the UIW segment to provide a decision basis for the development of assisted driving schemes and improvement of traffic safety management in the UIW segment.

Section: Classification Of Risk Indicators Based On the K-means Clust...mentioning

confidence: 99%

Traffic Risk Assessment of Lane-Changing Process in Urban Inter-Tunnel Weaving Segment

Zhou

Transportation Research Record: Journal of the Transportation R

et al. 2023

“…When the main and ground auxiliary roads between adjacent tunnels exchange traffic, an urban intertunnel weaving (UIW) section is formed, as shown in Figure 1. Statistics reveal that 80% of traffic accidents in urban tunnels are caused by lane-changing behaviour and 32.6% of accidents in the UIW section are lane-changing accidents [3]. The structure of the UIW section is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

“…The concentration of lane-changing behaviours has a great impact on the main road traffic. (3) The merging and diversion of the weaving section of overpasses are accomplished by setting up ramps, and traffic congestion is common near the ramps. In the limited space of the UIW section, vehicles need to be merged in and out simultaneously, resulting in a large number of lane-changing behaviours and complex interweaving behaviour, which can affect mainline traffic.…”

Section: Introductionmentioning

confidence: 99%

Choice of Lane-Changing Point in an Urban Intertunnel Weaving Section Based on Random Forest and Support Vector Machine

Wang

et al. 2023

PROMTT

Urban intertunnel weaving (UIW) section is a special type of weaving section, where various lane-changing behaviours occur. To gain insight into the lane-changing behaviour in the UIW section, in this paper we attempt to analyse the decision feature and model the behaviour from the lane-changing point selection perspective. Based on field-collected lane-changing trajectory data, the lane-changing behaviours are divided into four types. Random forest method is applied to analyse the influencing factors of choice of lane-changing point. Moreover, a support vector machine model is adopted to perform decision behaviour modelling. Results reveal that there are significant differences in the influencing factors for different lane-changing types and different positions in the UIW segment. The three most important factor types are object vehicle status, current-lane rear vehicle status and target-lane rear vehicle status. The precision of the choice of lane-changing point models is at least 82%. The proposed method could reveal the detailed features of the lane-changing point selection behaviour in the UIW section and also provide a feasible choice of lane-changing point model.

“…The number of tunnels has increased dramatically over the past three decades, especially in metropolises [ 1 ], and the tunnels in a city are mainly road tunnels. The enclosed environment of a tunnel can provide a smooth and undisturbed traffic flow environment, but the traffic exchange between a tunnel and a surface road can form a fixed interweaving zone, as shown in Fig 1 , which will be referred to as “intertunnel weaving sections” [ 2 ] in the following paper. In-tunnel and off-tunnel vehicles need to interact in a compact space.…”

Section: Introductionmentioning

confidence: 99%

“…And lane-changing behavior is one of the most important factors that affects traffic safety in weaving sections. According to statistics, 80% of traffic accidents in urban tunnels are caused by lane-change behavior [ 3 ], and lane-changing accidents in intertunnel weaving sections account for 32.6% [ 2 ]. Many rear-end accidents are also caused by lane-change behavior.…”

Section: Introductionmentioning

confidence: 99%

Trajectory-based characteristic analysis and decision modeling of the lane-changing process in intertunnel weaving sections

Wang

et al. 2022

PLoS ONE

Existing lane-changing models generally neglect the detailed modeling of lane-changing actions and model lane-changing only as an instantaneous event. In this study, an intertunnel weaving section was taken as the background, the lane-changing duration and distance in the lane-changing process were taken as the main research objects. The detailed modeling of a lane-changing action was emphasized. Aerial videos of intertunnel weaving sections were collected, and accurate vehicle trajectory data were extracted. Basic data analysis shows that the lane-changing duration has a lognormal distribution and the lane-changing distance has a normal distribution. To analyze the difference of the lane-changing behavior characteristics in different lane-changing environments, based on the lead spacing and lag spacing in the target lane, a hierarchical clustering algorithm was applied to classify the lane-changing environment into six different types. Then, a deep neural network regression model was applied to model the lane-changing process for each environment type. The results show that the horizontal distribution, vertical distribution and statistical characteristics of the lane changing points under different lane-changing environments are significantly different. The prediction accuracy of the lane-changing distance after classification is improved by at least 61%, and the prediction accuracy of the lane-changing duration after classification is improved by at least 57%. It is also found that lane-changing behavior characteristics with large or small lag spacing are easier to predict, while in the other cases, the randomness of the lane-changing behavior characteristics is more obvious. The research results can be incorporated into lane-changing decision assistance systems and micro traffic simulation models to make the assistance system safer and more effective, and the simulation outputs should be more realistic and accurate.