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Accelerating Discoveries in Traffic Science

Accelerating Discoveries in Traffic Science

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PUBLISHED
01.03.2024
DOI
https://doi.org/10.7307/ptt.v36i1.356
ISSUE
2024 (Vol 36), Issue 1
LICENSE
Copyright (c) 2024 Renan Favero, José Reynaldo Setti
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This work is licensed under a
Creative Commons Attribution-NonCommercial-
NoDerivatives 4.0 International License.

Factors Affecting the Impact of Autonomous Vehicles on Freeway Operations – An Exploratory Analysis Using PCEs

Authors:Renan Favero, José Reynaldo Setti

Abstract

Autonomous vehicles (AVs) and human-driven vehicles (HDVs) will share the roads for a long time, hence the need to study traffic flows mixing AVs and HDVs, especially during the AV introduction period. This paper aims to investigate the roadway and traffic characteristics that affect the impact of AVs on freeway traffic operations, using an adapted version of the HCM6 truck passenger-car equivalent (PCE) methodology. A large number of scenarios comprising different roadway characteristics, AV types and traffic flow compositions were simulated using Vissim to obtain AV PCEs. The results indicated that, for all scenarios considered, an AV has a 20% lower impact on the quality of service and operation than an HDV. A CART decision tree indicated that the most important factors affecting the AVs’ impact on traffic operations are vehicle-to-vehicle connectivity level and the capability of travelling in platoons. Maximum platoon length did not matter, and the increase in the number of traffic lanes reduced the positive impact of AVs on service quality.

Keywords:autonomous vehicles, passenger car equivalent, decision trees, freeways, microsimulation

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