Entropy-Based Dynamic Programming for Efficient Vehicle Parking

TL;DR

This paper introduces a novel entropy-based dynamic programming approach, inspired by statistical mechanics, to optimize parking allocation in multi-story garages, significantly reducing search times and congestion.

Contribution

It develops the Temperature-Informed Parking Policy (TIPP), a new real-time parking management method that predicts and dynamically adjusts parking assignments using an entropy model.

Findings

TIPP outperforms simpler policies in reducing parking search times.

The entropy model accurately predicts parking spot distribution.

TIPP approaches the theoretical optimal solution in simulations.

Abstract

In urban environments, parking has proven to be a significant source of congestion and inefficiency. In this study, we propose a methodology that offers a systematic solution to minimize the time spent by drivers in finding parking spaces. Drawing inspiration from statistical mechanics, we utilize an entropy model to predict the distribution of available parking spots across different levels of a multi-story parking garage, encoded by a single parameter: temperature. Building on this model, we develop a dynamic programming framework that guides vehicles to the optimal floor based on the predicted occupancy distribution. This approach culminates in our Temperature-Informed Parking Policy (TIPP), which not only predicts parking spot availability but also dynamically adjusts parking assignments in real-time to optimize vehicle placement and reduce search times. We compare TIPP with simpler policies and the theoretical optimal solution to demonstrate its effectiveness and gauge how closely it approaches the ideal parking strategy. The results highlight the potential of integrating TIPP in real-world applications, paving the way for smarter, more efficient urban landscapes.