Investigation of Automated Vehicle Effects on Driver's Behavior and Traffic Performance

Aria, Erfan; Olstam, Johan; Schwietering, Christoph · 2016 · OpenAlex-citations

DOI: 10.1016/j.trpro.2016.06.063

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Summary

This thesis investigates the impact of automated vehicles (AVs) on driver behavior and overall traffic performance, addressing the emerging reality of mixed traffic environments where automated and conventional vehicles coexist. The research is motivated by the potential of AVs to reduce congestion through shorter time headways and improved safety margins, while also addressing concerns regarding driver situation awareness, overreliance on automation, and the complexities of interactions in scenarios such as merging and overtaking. The study aims to examine how AVs influence conventional driver behavior, evaluate their effect on traffic performance measures, and assess the capability of microscopic simulation models to represent these dynamics. The methodology combines a broad literature review with a microscopic traffic simulation case study. The literature review focused on driving simulator studies and psychological research to identify behavioral parameters, such as gap acceptance and attention levels, which served as setup values for the simulation. The simulation was conducted using VISSIM software on a 2.9-kilometer three-lane autobahn segment featuring an off-ramp, on-ramp, and a roundabout. Three scenarios were modeled: 0% AV penetration (conventional vehicles only), 100% AV penetration, and 50% mixed penetration. Specific driving behavior parameters, including look-ahead distance and deceleration limits, were adjusted to reflect high-level automation for AVs. The study assumed that conventional drivers in the mixed scenario would adapt to the shorter headways maintained by AVs. The findings indicate that the positive effects of AVs on traffic performance are most pronounced during crowded conditions, such as peak hours. In the scenario with automated vehicles, the average density on the autobahn segment decreased by 8.09% during p.m. peak hours compared to the conventional vehicle scenario. The simulation also revealed smoother traffic flow and reduced queue lengths in weaving segments. The 50% penetration scenario demonstrated feasible interaction between conventional and automated vehicles, validating the hypothesis that conventional drivers adapt to AV behaviors. Additionally, the literature review highlighted that conventional vehicles driving near AV platoons tend to reduce their own time headways, potentially increasing risk, while highly automated driving was noted to reduce situation awareness and increase drowsiness in light traffic. The significance of this work lies in demonstrating that VISSIM can effectively simulate the presence and share of automated vehicles in traffic networks, providing a tool for evaluating future road infrastructure demands. The results suggest that AVs can enhance network capacity and reduce congestion, particularly in high-demand scenarios. However, the study notes that the validity of these outputs requires further research on urban and rural roads under varying traffic conditions. The thesis underscores the importance of understanding behavioral adaptations in mixed traffic to ensure safety and efficiency as automation technologies become more prevalent.

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Topics

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• traffic density
• situational awareness