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Trajectory Data Analyses for Pedestrian Space-time Activity Study
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Vehicle and Pedestrian Traffic Signal Performance Measures Using LiDAR-Derived Trajectory Data.

Enrique D Saldivar-Carranza1, Jairaj Desai1, Andrew Thompson1

  • 1Joint Transportation Research Program, Lyles School of Civil and Construction Engineering, Purdue University, West Lafayette, IN 47907, USA.

Sensors (Basel, Switzerland)
|October 16, 2024
PubMed
Summary
This summary is machine-generated.

LiDAR sensors precisely track vehicles and pedestrians at intersections, enabling real-time traffic signal performance analysis. This technology offers new insights into pedestrian movement, potentially improving traffic signal timing.

Keywords:
LiDARpedestrianperformancetraffic signalvehicle

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Area of Science:

  • Transportation Engineering
  • Traffic Flow Analysis
  • Sensor Technology

Background:

  • Signalized intersections are critical nodes in transportation networks.
  • Traditional traffic data collection methods have limitations in accuracy and scope.
  • Light Detection and Ranging (LiDAR) offers high-resolution object tracking capabilities.

Purpose of the Study:

  • To develop and validate methodologies for estimating traffic signal performance measures using LiDAR data.
  • To analyze vehicle and pedestrian movement patterns at signalized intersections.
  • To assess the potential of LiDAR for real-time traffic monitoring and pedestrian behavior analysis.

Main Methods:

  • Utilized LiDAR trajectory data from an intersection over a 24-hour period, capturing millions of vehicle and pedestrian waypoints.
  • Generated Purdue Probe Diagrams (PPDs) from linear-referenced trajectories for both vehicles and pedestrians.
  • Applied vehicle-based PPDs to estimate performance measures like queue lengths, arrivals on green, and level of service; pedestrian-based PPDs for wait times and multi-crosswalk traversal.

Main Results:

  • Successfully estimated key traffic signal performance measures (e.g., 85th percentile queue lengths, Highway Capacity Manual Level of Service) from LiDAR data.
  • Quantified pedestrian movement, including wait times and the proportion of individuals crossing multiple paths.
  • Determined pedestrian walking speeds (e.g., 15th percentile at 3.9 ft/s), enabling data-driven adjustments to signal timing.

Conclusions:

  • LiDAR technology provides a robust method for detailed, real-time assessment of traffic signal performance for both vehicles and pedestrians.
  • The ability to measure pedestrian speeds directly allows for more informed and potentially safer traffic signal timing strategies compared to standard guidelines.
  • LiDAR data enhances traffic management capabilities by offering granular insights into intersection dynamics.