Crowdsense Roadside Parking Spaces with Dynamic Gap Reduction Algorithm

TL;DR

This paper presents a Dynamic Gap Reduction Algorithm (DGRA) that improves the accuracy of mobile sensing for on-street parking detection in smart cities, validated through real tests and simulations.

Contribution

The paper introduces DGRA, a novel crowdsensing algorithm, and a DSTBM model to enhance parking detection accuracy and evaluate its effectiveness in urban environments.

Findings

DGRA significantly reduces sensing accuracy gaps.

Real drive tests confirm the algorithm's effectiveness.

Simulations support the potential for improved urban parking management.

Abstract

In the context of smart city development, mobile sensing emerges as a cost-effective alternative to fixed sensing for on-street parking detection. However, its practicality is often challenged by the inherent accuracy limitations arising from detection intervals. This paper introduces a novel Dynamic Gap Reduction Algorithm (DGRA), which is a crowdsensing-based approach aimed at addressing this question through parking detection data collected by sensors on moving vehicles. The algorithm's efficacy is validated through real drive tests and simulations. We also present a Driver-Side and Traffic-Based Model (DSTBM), which incorporates drivers' parking decisions and traffic conditions to evaluate DGRA's performance. Results highlight DGRA's significant potential in reducing the mobile sensing accuracy gap, marking a step forward in efficient urban parking management.