Document Type
Article
Source Publication Title
Public Works Management & Policy
DOI
https://journals.sagepub.com/doi/full/10.1177/1087724X231217677
Abstract
Recent advancements in autonomous vehicle (AV) technology have the potential to reduce road accidents caused by human error. However, to enhance their safety and performance, it is crucial to understand the patterns of AV collisions. This study examines AV collisions by analyzing their temporal and spatial patterns. Based on reports from the California DMV between 2014 and 2022, the analysis reveals that rear-end collisions are the most common type, while incidents involving pedestrians and overturned vehicles are rare. The majority of collisions involve mid-sized vehicles, and AVs are responsible for a minority of accidents. The study also identifies clusters of incidents in San Francisco, San Jose, Los Angeles, and San Diego, with San Francisco having largest concentration. Specific areas within San Francisco, like Mission District, Japantown, Union Square, and North Beach neighborhoods, show high incident rates. These findings highlight safety concerns, and aid in integration of AVs into transportation infrastructure.
Publication Date
11-29-2023
Language
English
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kermanshachi, Sharareh; Patel, Ronik Ketankumar; Channamallu, Sai Sneha; Khan, Muhammad Arif; and Pamidimukkala, Apurva, "An Exploratory Analysis of Temporal and Spatial Patterns of Autonomous Vehicle Collisions" (2023). SAGE Open Access Agreement Publications-Archive. 40.
https://mavmatrix.uta.edu/utalibraries_sageoapubs/40