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

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.

Fatal Crashes Among Drivers Age 60+ in Connecticut, Massachusetts, New Hampshire, and Rhode Island

Fatal crashes are related with the spatial components of physical environments (e.g. roadways, land use, and political boundaries). This study compares rates of fatal crashes among drivers age 60+ by counties in 4 states of CT, MA, NH, and RI. The GIS application is used to visualize the location of fatal crashes and to identify whether it is clustered as hotspots. This study pooled data related to fatal crashes in CT, MA, NH, and RI from the Fatal Accident Recording System (2008-2018). Sample (n=2,373) inclusion criteria were subjects (driver age 60+) had to have complete data on variables of interest and be involved in a crash with at least one fatality. More than half (n=1,387, 58.5%) of drivers had a fatal injury. Results showed that the county with the highest incidents of fatal crashes was New Haven, CT (n=183 involved, 53% fatality), Worcester, MA (n=179, 61.5% respectively), Hillsborough, NH (n=75, 65.3% respectively), and Providence, RI (n=94, 59.6% respectively). The GIS spatial analysis showed that crashes were clustered along roads with the highest speed limits (interstate highways or multilane state routes) and found that the hotspots of clustered fatal crashes were located in counties with big cities with high population densities (New Haven CT, Hartford CT, Springfield MA, Worcester MA, Boston MA, Concord NH, and Providence RI). Identification of these crash hotspots will be beneficial for drivers and policy makers. The findings may alert drivers to high risk areas and policy makers can implement countermeasures.

Methods for Identifying Truck Crash Hotspots

The goal of this study was to develop a new method for identifying the actual risky spots by using the geographic information system (GIS). For this purpose, in this study, three different methods for detecting hotspots are developed, i.e., (1) the annual average daily traffic (AADT) normalization method, (2) AK crashes (A is the incapacitating crash, and K is the fatal crash) percentage method, and (3) distribution difference method. To evaluate the performances of these three hotspot detection methods along with a baseline method that only considered the frequency of crashes, we applied these three methods to identify the top 20 hotspots for truck crashes in two representative areas in Texas. The results indicated that (1) all three proposed methods produced more reasonable results than the baseline method, and (2) the “distribution difference” method outperformed the other methods.