Title: Safety evaluation of connected and automated vehicles in mixed traffic with conventional vehicles at intersections

Abstract

Connected and Automated Vehicles (CAVs) can potentially improve the performance of the transportation system by reducing human errors. This paper investigates the safety impact of CAVs in a mixed traffic with conventional vehicles at intersections. Analyzing real-world AV crashes in California revealed that rear-end crashes at intersections are the dominant crash type. Therefore, to enhance our understanding of the future interactions between human-driven vehicles with CAVs at intersections, a simulation framework was developed to model the mixed traffic environment of Automated Vehicles (AV), cooperative AVs, and conventional human-driven vehicles. In order to model AVs driving behavior, Adaptive Cruise Control (ACC) and cooperative ACC (CACC) models are utilized. Particularly, this study explores system improvements due to automation and connectivity across varying CAV market penetration scenarios. ACC and CACC car following models are used to mimic the behavior of AVs and cooperative AVs. Real-world connected vehicle data are utilized to modify and tune the acceleration/deceleration regimes of the Wiedemann model. Next, the driving volatility concept capturing variability in vehicle speeds was utilized to calibrate the simulation to represent the safety performance of a real-world environment. Two surrogate safety measures are used to evaluate the safety performance of a representative intersection under differentmore » market penetration rate of CAVs: the number of longitudinal conflicts and driving volatility. At low levels of ACC market penetration, the safety improvements were found to be marginal, but safety improved substantially with more than 40% ACC penetration. Additional safety improvements can be achieved more quickly through the addition of cooperation and connectivity through CACC. Furthermore, ACC/CACC vehicles were found to improve mobility performance in terms of average speed and travel time at intersections.« less

Authors:

Arvin, Ramin  ^[1];

• Search DOE PAGES for author "Arvin, Ramin"
• Search DOE PAGES for ORCID "0000-0001-7805-3362"

Khattak, Asad J.  ^[1];

• Search DOE PAGES for author "Khattak, Asad J."
• Search DOE PAGES for ORCID "0000-0002-0790-7794"

Kamrani, Mohsen  ^[1];

• Search DOE PAGES for author "Kamrani, Mohsen"
• Search DOE PAGES for ORCID "0000-0003-2073-8582"

Rio-Torres, Jackeline  ^[2]

• Search DOE PAGES for author "Rio-Torres, Jackeline"
• Search DOE PAGES for ORCID "0000-0001-6750-9408"

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+ Show Author Affiliations

1. Univ. of Tennessee, Knoxville, TN (United States). Dept. of Civil and Environmental Engineering
2. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Energy & Transportation Science Division

Publication Date:

Wed Oct 28 00:00:00 EDT 2020

Research Org.:

Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1785644

Grant/Contract Number:  

AC05-00OR22725

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Intelligent Transportation Systems

Additional Journal Information:

Journal Volume: 25; Journal Issue: 2; Journal ID: ISSN 1547-2450

Publisher:

Taylor & Francis

Country of Publication:

United States

Language:

English

Subject:

33 ADVANCED PROPULSION SYSTEMS; 29 ENERGY PLANNING, POLICY, AND ECONOMY; Adaptive cruise control; connected and automated vehicles; cooperative adaptive cruise control; driving volatility; intersection safety; mobility; simulation; time to collision