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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Evaluation of Roadside LiDAR-Based and Vision-Based Multi-Model All-Traffic Trajectory Data.

Fei Guan¹, Hao Xu¹, Yuan Tian²

¹Department of Civil & Environmental Engineering, University of Nevada, 1664 N. Virginia St., Reno, NV 89557, USA.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

LiDAR sensors offer superior detection range and nighttime accuracy for traffic trajectory data compared to computer vision systems. Both methods accurately measure vehicle speeds, but LiDAR excels in pedestrian data consistency.

Keywords:

roadside LiDAR roadway sensor traffic trajectory data

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Last Updated: Jul 24, 2025

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Area of Science:

Transportation Engineering
Sensor Technology
Data Science

Background:

Trajectory data is crucial for understanding spatiotemporal traffic patterns.
Multi-model all-traffic data enhances accuracy and frequency for microscopic analysis.
Roadside sensors like LiDAR and cameras are key for data collection.

Purpose of the Study:

To compare and evaluate LiDAR-based and computer vision-based trajectory data.
To assess sensor performance under various conditions, including lighting and user types.
To provide guidance for selecting appropriate sensors for traffic analysis.

Main Methods:

Collected trajectory data from LiDAR and camera sensors at the same intersection.
Analyzed data for detection range, accuracy in different lighting, and speed measurement consistency.
Applied smoothing techniques to assess speed data.

Main Results:

LiDAR demonstrated a broader detection range and better performance in low light than computer vision.
Both sensors performed well for vehicle volume counting during the day.
LiDAR provided more consistent pedestrian counting accuracy at night, and both accurately measured vehicle speeds after smoothing.

Conclusions:

LiDAR offers advantages in detection range and nighttime/pedestrian data accuracy.
Computer vision data showed more fluctuations in pedestrian speed measurements.
The study provides valuable insights for selecting sensors based on specific traffic analysis needs.