scholarly journals Unravelling the Impacts of Parameters on Surrogate Safety Measures for a Mixed Platoon

2020 ◽  
Vol 12 (23) ◽  
pp. 9955
Author(s):  
Fan Ding ◽  
Jiwan Jiang ◽  
Yang Zhou ◽  
Ran Yi ◽  
Huachun Tan
Keyword(s):  
Traffic Safety ◽  
Feedforward Control ◽  
Safety Evaluation ◽  
Evaluation Framework ◽  
Active Safety ◽  
Automated Vehicles ◽  
Safety Measures ◽  
Car Following ◽  
Surrogate Safety Measures ◽  
Connected Automated Vehicles

With the precedence of connected automated vehicles (CAVs), car-following control technology is a promising way to enhance traffic safety. Although a variety of research has been conducted to analyze the safety enhancement by CAV technology, the parametric impact on CAV technology has not been systematically explored. Hence, this paper analyzes the parametric impacts on surrogate safety measures (SSMs) for a mixed vehicular platoon via a two-level analysis structure. To construct the active safety evaluation framework, numerical simulations were constructed which can generate trajectories for different kind of vehicles while considering communication and vehicle dynamics characteristics. Based on the trajectories, we analyzed parametric impacts upon active safety on two different levels. On the microscopic level, parameters including controller dynamic characteristics and equilibrium time headway of car-following policies were analyzed, which aimed to capture local and aggregated driving behavior’s impact on the vehicle. On the macroscopic level, parameters incorporating market penetration rate (MPR), vehicle topology, and vehicle-to-vehicle environment were extensively investigated to evaluate their impacts on aggregated platoon level safety caused by inter-drivers’ behavioral differences. As indicated by simulation results, an automated vehicle (AV) suffering from degradation is a potentially unsafe component in platoon, due to the loss of a feedforward control mechanism. Hence, the introduction of connected automated vehicles (CAVs) only start showing benefits to platoon safety from about 20% CAV MPR in this study. Furthermore, the analysis on vehicle platoon topology suggests that arranging all CAVs at the front of a mixed platoon assists in enhancing platoon SSM performances.

Traffic Safety Evaluation of Uncontrolled Intersections using Surrogate Safety Measures under Mixed Traffic Conditions

2015 ◽  
Vol 2512 (1) ◽  
pp. 81-89 ◽  
Author(s):  
P. Vedagiri ◽  
Deepak V. Killi
Keyword(s):  
Traffic Safety ◽  
Safety Evaluation ◽  
Mixed Traffic ◽  
Safety Measures ◽  
Traffic Conditions ◽  
Management Measures ◽  
Large Populations ◽  
Accident Data ◽  
Surrogate Safety Measures ◽  
The Impact

In the developing world, with increases in population, the number of vehicles is increasing tremendously. Traffic safety on roads has become a major concern even with advancements in technology and infrastructure. Traffic safety assessments and accident prediction based on accident data is a reactive approach. There are known drawbacks related to the reliability of accident data, especially in developing countries with large populations, such as India. It is, however, unethical to wait for accidents to occur before drawing statistically accurate conclusions regarding safety impacts. To overcome this impediment, one needs to develop accurate models that rely on surrogate safety measures (SSMs) for effective safety evaluations. The main advantage associated with the use of these models is that they can model crashes more frequently than in the real world and thereby imply an efficient and more statistically reliable proximal measure of traffic safety. The objective of this study is to examine the impact of management measures on traffic safety at a three-arm uncontrolled intersection with the use of microsimulation modeling under mixed traffic conditions. This examination was done by developing a unique methodology of measuring one SSM, postencroachment time (PET). This paper describes improvement in the accuracy of crash predictions by proposing a methodology to calculate PET.

scholarly journals PEDESTRIAN SAFETY EVALUATION OF SIGNALIZED INTERSECTIONS USING SURROGATE SAFETY MEASURES

Transport ◽  
2020 ◽  
Vol 35 (1) ◽  
pp. 48-56
Author(s):  
Sankaran Marisamynathan ◽  
Perumal Vedagiri
Keyword(s):  
Safety Evaluation ◽  
Pedestrian Safety ◽  
Signalized Intersections ◽  
Signalized Intersection ◽  
Mixed Traffic ◽  
Model Framework ◽  
Safety Measures ◽  
Severity Level ◽  
Traffic Conditions ◽  
Surrogate Safety Measures

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the cross-walk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections.

Operational safety of automated and human driving in mixed traffic environments: A perspective of car-following behavior

2021 ◽  
pp. 1748006X2110506
Author(s):  
Tao Li ◽  
Xu Han ◽  
Jiaqi Ma ◽  
Marilia Ramos ◽  
Changju Lee
Keyword(s):  
Traffic Safety ◽  
Risk Mitigation ◽  
Safety Evaluation ◽  
Mixed Traffic ◽  
Trajectory Data ◽  
Road Transportation ◽  
Baseline Model ◽  
Car Following ◽  
On The Road ◽  
The Impact

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new Conditional Variational Autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

scholarly journals The Impact of Truck Proportion on Traffic Safety Using Surrogate Safety Measures in China

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Shengdi Chen ◽  
Shiwen Zhang ◽  
Yingying Xing ◽  
Jian Lu ◽  
Yichuan Peng ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Traffic Flow ◽  
Traffic Safety ◽  
Variation Coefficient ◽  
Support Vector ◽  
Safety Measures ◽  
Speed Variation ◽  
Surrogate Safety Measures ◽  
The Impact ◽  
The Relationship

The purpose of this study is to investigate the impact of the truck proportion on surrogate safety measures to explore the relationship between truck proportion and traffic safety. The relationship between truck proportion and traffic flow parameters was analyzed by correlation and partial correlation analysis, and the value of the 85th percentile speed minus the 15th percentile speed (85%V–15%V) and the speed variation coefficient were selected as surrogate safety measures to explore the impact of truck proportion on traffic status. The k-means algorithm and the support vector machine were employed to evaluate traffic status on a freeway under different truck proportions in different periods. The major results are that the relationship between truck proportion and the value of 85%V–15%V and the speed variation coefficient is consistent in different aggregation periods. With increasing truck proportion, the value of 85%V–15%V, as well as the speed variation coefficient, increases initially and then decreases. In addition, the traffic flow status tends to be dangerous when the truck proportion ranges from 0.4 to 0.6 and when the value of 85%V–15%V and the speed variation coefficient are above 42 km/h and 0.223, respectively. While the truck proportion is from 0.1 to 0.3 and from 0.7 to 0.9, the traffic flow is relatively safe on the condition that the value of 85%V–15%V and the speed variation coefficient were under 42 km/h and 0.223, respectively. Therefore, the relationship between truck proportion and traffic safety could be well revealed by two surrogate safety measures, that is, the value of 85%V–15%V and the speed variation coefficient. In addition, the k-means algorithm and the support vector machine can well reveal the impact of truck proportion on traffic safety in different periods. The findings of this study indicate a need for decreasing the disturbance of mixed traffic and the impact of the truck proportion on traffic safety status.

Safety Impacts of Queue Warning in a Connected Vehicle Environment

2017 ◽  
Vol 2621 (1) ◽  
pp. 31-37 ◽  
Author(s):  
Samaneh Khazraeian ◽  
Mohammed Hadi ◽  
Yan Xiao
Keyword(s):  
Traffic Safety ◽  
Assessment Model ◽  
Connected Vehicle ◽  
Market Penetration ◽  
Warning Systems ◽  
Safety Measures ◽  
Dynamic Message Signs ◽  
Traffic Sensors ◽  
Message Signs ◽  
Surrogate Safety Measures

Queue warning systems (QWSs) have been implemented to increase traffic safety by informing drivers about queued traffic ahead so that they can react in a timely manner to the queue. Existing QWSs rely on fixed traffic sensors to detect the back of a queue. It is expected that if the transmitted messages from connected vehicles (CVs) are used for this purpose, detection can be faster and more accurate. In addition, with CVs, delivery of the messages can be done with onboard units instead of dynamic message signs and provide more flexibility on how far upstream of the queue the messages are delivered. This study investigates the accuracy and benefits of the QWS on the basis of CV data. The study evaluated the safety benefits of the QWS under different market penetrations of CVs in future years. Surrogate safety measures were estimated with simulation modeling combined with the surrogate safety assessment model tool. Results from this study indicate that a relatively low market penetration—about 3% to 6%—for the congested freeway examined in this study was sufficient for an accurate and reliable estimation of the queue length. Even at 3% market penetration, the CV-based estimation of back-of-queue identification was significantly more accurate than that based on detector measurements. The results also found that CV data allowed faster detection of the bottleneck and queue formation. Further, the QWS improved the safety conditions of the network by reducing the number of rear-end conflicts. Safety effects become significant when the compliance percentage with the queue warning messages is more than 15%.

scholarly journals Safety Evaluation for Connected and Autonomous Vehicles’ Exclusive Lanes considering Penetrate Ratios and Impact of Trucks Using Surrogate Safety Measures

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Jian Zhang ◽  
Kunrun Wu ◽  
Min Cheng ◽  
Min Yang ◽  
Yang Cheng ◽  
...  
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Evaluation ◽  
Driver Model ◽  
Microscopic Simulation ◽  
Safety Measures ◽  
Traffic Efficiency ◽  
Safety Improvement ◽  
Demand Scenario ◽  
Surrogate Safety Measures

Plenty of studies on exclusive lanes for Connected and Autonomous Vehicle (CAV) have been conducted recently about traffic efficiency and safety. However, most of the previous research studies neglected comprehensive consideration of the safety impact on different market penetration rates (MPRs) of CAVs, traffic demands, and proportion of trucks in mixture CAVs with human’s driven vehicle environment. On this basis, this study is to (1) identify the safety impact on exclusive lanes for CAVs under different MPRs with different traffic demands and (2) investigate the safety impact of trucks for CAV exclusive lanes on mixture environment. Based on the Intelligent Driver Model (IDM), a CAV platooning control algorithm is proposed for modeling the driving behaviors of CAVs. A calibrated 7-kilometer freeway section microscopic simulation environment is built by VISSIM. Four surrogate safety measures, including both longitudinal and lateral safety risk indexes, are employed to evaluate the overall safety impacts of setting exclusive lanes. Main results indicate that (1) setting one exclusive lane is capable to improve overall safety environment in low demand, and two exclusive lanes are more suitable for high-demand scenario; (2) existence of trucks worsens overall longitudinal safety environment, and improper setting of exclusive lanes in high trucks, low MPR scenario has adverse effect on longitudinal safety; and (3) setting exclusive lanes have better longitudinal and lateral safety improvement in high-truck proportion scenarios. Setting one or two exclusive lanes led to [+42.4% to −52.90%] and [+45.7% to −55.2%] of longitudinal risks while [−1.8% to −87.1%] and [−2.1% to −85.3%] of lateral conflicts compared with the base scenario, respectively. Results of this study provide useful insight for the setting of exclusive lanes for CAVs in a mixture environment.

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