Review on Parking Demand Estimation Based on Crowdsourced Data

Abstract

With the increase in urbanization and the growth of the economy, car ownership increases significantly, and at the same time demand for parking increases causing several issues like traffic congestion and resource wastage. There’s a critical significance of accurate parking demand estimation in the context of urban planning, addressing those issues. It emphasizes the emerging role of crowdsourced data as a novel solution for more efficient and cost-effective parking demand estimation. This review discusses the benefits of crowdsourcing, including real-time data and minimal infrastructure requirements, while acknowledging challenges like data accuracy, user privacy, and potential biases. Furthermore, it provides a comprehensive overview of the subject matter and it suggests future directions for improvement, proposing the integration of advanced technologies like Google APIs and IoT to augment parking demand estimation models and address the limitations associated with crowdsourced data.