scholarly journals Rear-End Crash Risk Analysis considering Drivers’ Visual Perception and Traffic Flow Uncertainty: A Hierarchical Hybrid Bayesian Network Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Naikan Ding ◽  
Linsheng Lu ◽  
Nisha Jiao
Keyword(s):  
Visual Perception ◽  
Traffic Flow ◽  
Crash Risk ◽  
Perceptual Information ◽  
Specific Flow ◽  
Car Following ◽  
Driving Behaviors ◽  
Level Data ◽  
Spatial Transferability ◽  
Model Approach

Rear-end crashes or crash risk is widely recognized as safety-critical state of vehicles under comprehensive conditions. This study investigated the association between traffic flow uncertainty, drivers’ visual perception, car-following behavior, roadway and vehicular characteristics, and rear-end crash risk variation and compared the crash risk variation prediction with and without specific flow-level data. Two datasets comprising 5055 individual vehicles in car-following state were collected through on-road experiments on two freeways in China. A hierarchical hybrid BN model approach was proposed to capture the association between drivers’ visual perception, traffic flow uncertainty, and rear-end crash risk variation. Results show that (1) the BN model with flow-level data outperformed the BN model without flow-level data and could predict 85.3% of the cases of crash risk decrease, with a false alarm rate of 21.4%; (2) the hierarchical hybrid BN models showed plausible spatial transferability in predicting crash risk variation; and (3) the incorporation of specific flow-level variables and data greatly benefited the successful identification of rear-end crash risk variations. The findings of this study suggest that rear-end crash risk is inherently associated with both individual driving behaviors and traffic flow uncertainty, and appropriate visual perceptual information could compensate for crash risk and improve safety.

The impact of aggressive driving behaviors on multi-lane highway traffic flow

2018 ◽  
Vol 29 (07) ◽  
pp. 1850056 ◽  
Author(s):  
H. B. Zhu ◽  
G. Y. Chen ◽  
H. Lin ◽  
Y. J. Zhou
Keyword(s):  
Traffic Flow ◽  
High Speed ◽  
Aggressive Driving ◽  
Highway Traffic ◽  
Lane Changing ◽  
Car Following ◽  
Driving Behaviors ◽  
Evolution Pattern ◽  
Proposed Model ◽  
The Impact

A modified cellular automata traffic model is proposed to simulate four-lane traffic flow, in which drivers are classified into aggressive drivers and cautious drivers and the anticipative velocity of the adjacent vehicles is considered. Analysis from the vehicles’ evolution pattern indicates that vehicles driven by the aggressive drivers are more powerful in behaviors of lane-changing and car-following. The model is refined by using the small cell of one meter long in order to simulate the traffic flow meticulously and realistically. The results indicate that the lane-changing maneuver exhibits different property as the density varies, and it does have a significant impact on the characteristics of the surrounding traffic flow due to their interfering effects on the following vehicles. Furthermore, the phenomenon of high-speed car-following is exhibited, and the results coincide with the empirical data very well. It is shown that the proposed model is reasonable and can partially reflect the real traffic.

Autonomous Vehicles’ Car-Following Drivability Evaluation Based on Driving Behavior Spectrum Reference Model

2021 ◽  
pp. 036119812199485
Author(s):  
Xiao Qi ◽  
Ying Ni ◽  
Yiming Xu ◽  
Ye Tian ◽  
Junhua Wang ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Reference Model ◽  
Numerical Test ◽  
Safety Evaluation ◽  
Driving Behavior ◽  
Car Following ◽  
Driving Behaviors ◽  
Human Driver ◽  
Normal Human ◽  
Naturalistic Driving

A large portion of the accidents involving autonomous vehicles (AVs) are not caused by the functionality of AV, but rather because of human intervention, since AVs’ driving behavior was not properly understood by human drivers. Such misunderstanding leads to dangerous situations during interaction between AV and human-driven vehicle (HV). However, few researches considered HV-AV interaction safety in AV safety evaluation processes. One of the solutions is to let AV mimic a normal HV’s driving behavior so as to avoid misunderstanding to the most extent. Therefore, to evaluate the differences of driving behaviors between existing AV and HV is necessary. DRIVABILITY is defined in this study to characterize the similarity between AV’s driving behaviors and expected behaviors by human drivers. A driving behavior spectrum reference model built based on human drivers’ behaviors is proposed to evaluate AVs’ car-following drivability. The indicator of the desired reaction time (DRT) is proposed to characterize the car-following drivability. Relative entropy between the DRT distribution of AV and that of the entire human driver population are used to quantify the differences between driving behaviors. A human driver behavior spectrum was configured based on naturalistic driving data by human drivers collected in Shanghai, China. It is observed in the numerical test that amongst all three types of preset AVs in the well-received simulation package VTD, the brisk AV emulates a normal human driver to the most extent (ranking at 55th percentile), while the default AV and the comfortable AV rank at 35th and 8th percentile, respectively.

scholarly journals A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

Smart Cities ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 314-335
Author(s):  
Hafiz Usman Ahmed ◽  
Ying Huang ◽  
Pan Lu
Keyword(s):  
Autonomous Vehicles ◽  
Adaptive Cruise Control ◽  
Cruise Control ◽  
Modeling Tools ◽  
Microscopic Traffic Simulation ◽  
Simulation Tools ◽  
Car Following ◽  
Driving Behaviors ◽  
Micro Simulation ◽  
Models And Modeling

The platform of a microscopic traffic simulation provides an opportunity to study the driving behavior of vehicles on a roadway system. Compared to traditional conventional cars with human drivers, the car-following behaviors of autonomous vehicles (AVs) and connected autonomous vehicles (CAVs) would be quite different and hence require additional modeling efforts. This paper presents a thorough review of the literature on the car-following models used in prevalent micro-simulation tools for vehicles with both human and robot drivers. Specifically, the car-following logics such as the Wiedemann model and adaptive cruise control technology were reviewed based on the vehicle’s dynamic behavior and driving environments. In addition, some of the more recent “AV-ready (autonomous vehicles ready) tools” in micro-simulation platforms are also discussed in this paper.

Surrounding Vehicles’ Contribution to Car-Following Models: Deep-Learning-Based Analysis

2021 ◽  
pp. 036119812110186
Author(s):  
Saeed Vasebi ◽  
Yeganeh M. Hayeri ◽  
Peter J. Jin
Keyword(s):  
Deep Learning ◽  
Traffic Flow ◽  
Short Term Memory ◽  
Data Availability ◽  
Car Following ◽  
Preceding Vehicle ◽  
Long Short Term Memory ◽  
The Right ◽  
Mean Square Errors ◽  
Deep Learning Model

Relatively recent increased computational power and extensive traffic data availability have provided a unique opportunity to re-investigate drivers’ car-following (CF) behavior. Classic CF models assume drivers’ behavior is only influenced by their preceding vehicle. Recent studies have indicated that considering surrounding vehicles’ information (e.g., multiple preceding vehicles) could affect CF models’ performance. An in-depth investigation of surrounding vehicles’ contribution to CF modeling performance has not been reported in the literature. This study uses a deep-learning model with long short-term memory (LSTM) to investigate to what extent considering surrounding vehicles could improve CF models’ performance. This investigation helps to select the right inputs for traffic flow modeling. Five CF models are compared in this study (i.e., classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models). Performance of the CF models is compared in relation to accuracy, stability, and smoothness of traffic flow. The CF models are trained, validated, and tested by a large publicly available dataset. The average mean square errors (MSEs) for the classic, multi-anticipative, adjacent-lanes, following-vehicle, and all-surrounding-vehicles CF models are 1.58 × 10−3, 1.54 × 10−3, 1.56 × 10−3, 1.61 × 10−3, and 1.73 × 10−3, respectively. However, the results show insignificant performance differences between the classic CF model and multi-anticipative model or adjacent-lanes model in relation to accuracy, stability, or smoothness. The following-vehicle CF model shows similar performance to the multi-anticipative model. The all-surrounding-vehicles CF model has underperformed all the other models.

Influence of Driving Behaviors on the Stability in Car Following

2019 ◽  
Vol 20 (3) ◽  
pp. 1081-1098 ◽  
Author(s):  
Yunpeng Wang ◽  
Junjie Zhang ◽  
Guangquan Lu
Keyword(s):  
Car Following ◽  
Driving Behaviors ◽  
The Stability

Car-following theory of steady-state traffic flow using time-to-collision

2011 ◽  
Vol 12 (8) ◽  
pp. 645-654 ◽  
Author(s):  
Sheng Jin ◽  
Zhi-yi Huang ◽  
Peng-fei Tao ◽  
Dian-hai Wang
Keyword(s):  
Steady State ◽  
Traffic Flow ◽  
Time To Collision ◽  
Car Following

The effects of drivers’ aggressive characteristics on traffic stability from a new car-following model

2016 ◽  
Vol 30 (18) ◽  
pp. 1650243 ◽  
Author(s):  
Guanghan Peng ◽  
Li Qing
Keyword(s):  
Stability Analysis ◽  
Nonlinear Analysis ◽  
Traffic Flow ◽  
Linear Stability Analysis ◽  
Stable Condition ◽  
Mkdv Equation ◽  
Stable Region ◽  
Car Following ◽  
Car Following Model ◽  
The Stability

In this paper, a new car-following model is proposed by considering the drivers’ aggressive characteristics. The stable condition and the modified Korteweg-de Vries (mKdV) equation are obtained by the linear stability analysis and nonlinear analysis, which show that the drivers’ aggressive characteristics can improve the stability of traffic flow. Furthermore, the numerical results show that the drivers’ aggressive characteristics increase the stable region of traffic flow and can reproduce the evolution and propagation of small perturbation.

scholarly journals Enhancing Freeway Safety through Intervening in Traffic Flow Dynamics Based on Variable Speed Limit Control

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Jinming You ◽  
Shouen Fang ◽  
Lanfang Zhang ◽  
John Taplin ◽  
Jingqiu Guo
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
New Technologies ◽  
Flow Dynamics ◽  
Crash Risk ◽  
Speed Limit ◽  
Variable Speed ◽  
Variable Speed Limit ◽  
Traffic Conditions ◽  
Safety Research

New technologies and traffic data sources provide great potential to extend advanced strategies in freeway safety research. The High Definition Monitoring System (HDMS) data contribute comprehensive and precise individual vehicle information. This paper proposes an innovative Variable Speed Limit (VSL) based approach to manage crash risks by intervening in traffic flow dynamics on freeways using HDMS data. We first conducted an empirical analysis on real-time crash risk estimation using a binary logistic regression model. Then, intensive microscopic simulations based on AIMSUN were carried out to explore the effects of various intervention strategies with respect to a 3-lane freeway stretch in China. Different speed limits with distinct compliance rates under specified traffic conditions have been simulated. By taking into account the trade-off between safety benefits and delay in travel time, the speed limit strategies were optimized under various traffic conditions and the model with gradient feedback produces more satisfactory performance in controlling real-time crash risks. Last, the results were integrated into lane management strategies. This research can provide new ideas and methods to reveal the freeway crash risk evolution and active traffic management.

scholarly journals A New Car-Following Model with Consideration of Dynamic Safety Distance

2018 ◽  
Vol 2018 ◽  
pp. 1-5
Author(s):  
Tao Wang ◽  
Jing Zhang ◽  
Guangyao Li ◽  
Keyu Xu ◽  
Shubin Li
Keyword(s):  
Traffic Flow ◽  
Velocity Model ◽  
Safe Distance ◽  
Optimal Velocity ◽  
New Model ◽  
Car Following ◽  
Optimal Velocity Model ◽  
Safety Distance ◽  
Car Following Model ◽  
The Impact

In the traditional optimal velocity model, safe distance is usually a constant, which, however, is not representative of actual traffic conditions. This paper attempts to study the impact of dynamic safety distance on vehicular stream through a car-following model. Firstly, a new car-following model is proposed, in which the traditional safety distance is replaced by a dynamic term. Then, the phase diagram in the headway, speed, and sensitivity spaces is given to illustrate the impact of a variable safe distance on traffic flow. Finally, numerical methods are conducted to examine the performance of the proposed model with regard to two aspects: compared with the optimal velocity model, the new model can suppress traffic congestion effectively and, for different safety distances, the dynamic safety distance can improve the stability of vehicular stream. Simulation results suggest that the new model is able to enhance traffic flow stability.

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