Year: 2024
Type: article
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 article 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 evaluation 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.
Source: IEEE Internet of Things Journal
Authors: Wenjun Zheng, Zhan Shi, Qianyu Ou, Ruizhi Liao
Language: English
Subfield: Building and Construction
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Awards: G4301455898, G4644238951
OpenAlex ID: W4405429608