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

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Real-Time Queue Length Detection with Roadside LiDAR Data.

Jianqing Wu^1,2, Hao Xu², Yongsheng Zhang²

¹School of Qilu Transportation, Shandong University, Jinan 250061, China.

Sensors (Basel, Switzerland)

|April 25, 2020

Summary

This study introduces a new method using roadside LiDAR data for real-time traffic queue length detection. The novel approach achieves 98% accuracy, improving traffic management systems.

Keywords:

queue length roadside sensor vehicle detection

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Area of Science:

Traffic engineering
Remote sensing technology
Computer vision

Background:

Real-time traffic queue length data is crucial for effective traffic management and applications.
Existing methods may face challenges with accuracy and real-time data acquisition.

Purpose of the Study:

To develop and validate a novel method for real-time traffic queue length detection using roadside LiDAR data.
To address challenges such as occlusion and data loss in queue length estimation.

Main Methods:

Continuous vehicle tracking using LiDAR data processing: background filtering, point clustering, object classification, lane identification, and object association.
Development of a specific algorithm to identify the last vehicle in a queue, accounting for occlusion and package loss.
Field validation using ground-truth data from multiple sites.

Main Results:

The proposed method accurately detects traffic queue lengths in real-time.
Achieved an average accuracy of 98% across six investigated sites.
Detailed diagnosis of errors in queue length detection was performed.

Conclusions:

The novel LiDAR-based method provides a reliable and accurate solution for real-time traffic queue length detection.
This technology can significantly enhance the capabilities of traffic management systems.
The method's robustness in handling occlusion and data loss makes it suitable for practical deployment.