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Driver behavior analysis at alternative intersection corridors through driving simulator.

Guangchuan Yang1, R Thomas Chase2, Yunmei Liu3

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Summary
This summary is machine-generated.

Driver behavior in alternative intersection (AI) corridors was evaluated using a driving simulator. While AI designs show promise, specific movements like the Median U-Turn (MUT) side street left-turn pose higher risks for failure movements.

Keywords:
Alternative intersectionsCorridor designDriver behaviorDriving simulatorFailure movement

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Area of Science:

  • Traffic Engineering
  • Human Factors in Transportation
  • Road Safety

Background:

  • Alternative intersection (AI) designs offer potential safety and operational benefits over conventional intersections (CI).
  • AI designs involve unconventional traffic maneuvers, necessitating an understanding of their impact on driver behavior, especially in successive corridor implementations.
  • Evaluating driver performance in AI corridors is crucial for effective deployment in transportation projects.

Purpose of the Study:

  • To assess driver performance navigating corridors with various alternative intersection designs.
  • To identify key metrics influencing driver behavior, including failure movements (FMs), approach speed (AS), hard-braking events (HBEs), and approach lane changes (ALCs).
  • To compare driver responses across different corridor treatments: conventional intersection (CI), Reduced Conflict Intersection (RCI), and varied AI designs.

Main Methods:

  • A driving simulator experiment was conducted with 48 participants navigating AI corridors.
  • Three corridor treatments were simulated: CI, RCI, and a mix of AI designs.
  • Data collected included FMs, AS, HBEs, and ALCs across 12 intersection pairs, with varying preceding and test intersection configurations.

Main Results:

  • Trial number, preceding intersection configuration, test intersection movement, and intersection pair significantly affected driver behavior.
  • The Median U-Turn (MUT) side street left-turn showed the highest risk for failure movements.
  • Hard-braking events were most frequent at Quadrant Roadway Intersection (QRI), MUT, and RCI configurations. Approach speeds increased over trials.

Conclusions:

  • Driver behavior is significantly influenced by intersection configuration and trial progression, not demographics.
  • Specific AI designs and movements require careful consideration due to observed behavioral impacts.
  • Clear signage and pavement markings are vital for informing drivers about unconventional AI traffic patterns.