Dynamic Price of Parking Service based on Deep Learning

TL;DR

This paper proposes a deep learning-based system to dynamically price parking services in Madrid, aiming to reduce vehicle parking during predicted low air quality episodes, thereby improving urban air quality.

Contribution

It introduces a novel approach combining deep learning and air quality forecasting to optimize parking prices dynamically, which is a new application in urban environmental management.

Findings

Deep learning models effectively forecast air quality episodes.

Dynamic pricing influences parking behavior and air quality.

Economic and quality metrics validate the approach.

Abstract

The improvement of air-quality in urban areas is one of the main concerns of public government bodies. This concern emerges from the evidence between the air quality and the public health. Major efforts from government bodies in this area include monitoring and forecasting systems, banning more pollutant motor vehicles, and traffic limitations during the periods of low-quality air. In this work, a proposal for dynamic prices in regulated parking services is presented. The dynamic prices in parking service must discourage motor vehicles parking when low-quality episodes are predicted. For this purpose, diverse deep learning strategies are evaluated. They have in common the use of collective air-quality measurements for forecasting labels about air quality in the city. The proposal is evaluated by using economic parameters and deep learning quality criteria at Madrid (Spain).