Evaluating the Safety Impact of Connected and Autonomous Vehicles with Lane Management on Freeway Crash Hotspots Using the Surrogate Safety Assessment Model

Abstract

One goal for large-scale deployment of connected and autonomous vehicles is to achieve the traffic safety benefit since connected and autonomous vehicles (CAVs) could reduce the collision risk by enhancing the driver’s situation perception ability. Previous studies have analyzed the safety impact of CAVs involved in traffic, but only few studies examined the safety benefits brought by CAVs when approaching high-collision-risk road segments such as the freeway crash hotspots. This study chooses one freeway crash hotspot in Wuhan, China, as an instance and attempts to estimate the safety benefits for differential penetration rates (PRs) of CAVs using the surrogate safety assessment model (SSAM). First, the freeway crash hotspot is identified with kernel density estimation and simulated by VISSIM. Then, the intelligent driver model (IDM) and Wiedemann 99 (a car-following model) are adopted and calibrated to control the driving behaviors of CAVs and human-driven vehicles (HVs) in this study, respectively. The impact that rather CAVs are constrained with or without managed lanes on traffic safety is also discussed, and the PR of CAVs is set from 10% to 90%. The results of this study show that when the PR of CAVs is lower than 50%, there is no significant improvement on the safety measures such as conflicts, acceleration, and velocity difference, which are extracted from the vehicle trajectory data using SSAM. When the penetration rate is over 70%, the experiment results demonstrate that the traffic flow passing the freeway hotspot is with fewer conflicts, smaller acceleration, and smaller velocity difference in the scenario where CAVs are constrained with managed lane compared with the scenario without managed lane control. The safety benefit that CAVs bring needs to be discussed. The lane management of CAVs will also lead to distinct safety impact.

Investigating the Impact of Automated Vehicles on the Safety and Operation of Low-Speed Urban Networks

“…Tis study has some key features which distinguish it from other studies. First, there are many studies that have investigated the impact of AVs on capacity and MFDs [10,11,22,24,25] or safety [16,20,21,26,29]. However, none of these studies have explored the impact of AVs on MFDs and MSDs simultaneously, and there is no study that has examined the impact of AVs on MSDs.…”

“…Many studies have investigated the impact of AVs on trafc fow and safety in diferent environments and situations [11,16,[20][21][22][23][24][25][26]. Some of these studies have explored the impact of AVs on fundamental diagrams (MFDs) and the capacity of transportation networks [10,27,28].…”

“…Some of these studies have explored the impact of AVs on fundamental diagrams (MFDs) and the capacity of transportation networks [10,27,28]. On the other hand, others have investigated the safety impact of AVs based on safety measures [16,20,21,26,29]. However, to our knowledge, no studies have yet investigated the impact of AVs on MFDs and safety simultaneously.…”

Effect of Driver Response on Efficiency of Vehicular Communication using Penalty Cost Function (EVCPCF)

“…The figures depict upper values for both the AVCS and the cooperative that are capped or limited at 0.5 and 0.33, respectively, with both PCF conditions emanating from and diverging from these values. A MATLAB simulation is run using an increasing number of vehicles (10,20,30,40,50), a frame length of 10, and a communication waiting time of 15 slots in order to validate the provided mathematical model. The simulation examined the limiting situation of equations ( 4) and ( 19), testing data traffic and throughput with efficiency values of 0.5 and 0.33, demonstrating that with high values, both AVCS and cooperative systems will converge to similar values at the limiting values.…”

“…MATLAB simulation was used to verify the mathematical model. The simulation covered two limiting cases of 0.33 and 0.5 and used incrementing numbers of vehicles (10,20,30, 40, 50) to check the impact of increasing vehicle numbers on communication efficiency and examine whether both AVCS and AVCS with cooperative will have close levels and converge at limiting values.…”