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Entropy-Based Dynamic Programming for Efficient Vehicle Parking

Jean-Luc Lupien, Abdullah Alhadlaq, Yuhan Tang, Jiayu Joyce Chen, Yutan Long

TL;DR

The Temperature-Informed Parking Policy (TIPP) is proposed, which not only predicts parking spot availability but also dynamically adjusts parking assignments in real-time to optimize vehicle placement and reduce search times.

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.

Entropy-Based Dynamic Programming for Efficient Vehicle Parking

TL;DR

The Temperature-Informed Parking Policy (TIPP) is proposed, which not only predicts parking spot availability but also dynamically adjusts parking assignments in real-time to optimize vehicle placement and reduce search times.

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.

Paper Structure

This paper contains 6 sections, 6 equations, 6 figures.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Prediction - Entropy Model
  4. Control - Dynamic Programming
  5. Experiments
  6. Conclusion

Figures (6)

  • Figure 1: The framework of this research
  • Figure 2: Predicted and Measured Fill Rate as a Function of Distance
  • Figure 3: MSE as a Function of Observations in Sample
  • Figure 4: Depiction of the parking garage utilized in the experimentation phase. This is a 10-level parking garage, each level featuring 30 parking spots. Red spots indicate occupied spaces, and white spots represent empty ones. The occupancy distribution is determined using a temperature of 0.5 to simulate the fill rate as per the Entropy Model
  • Figure 5: Comparison of the time taken to park for 30 sequential cars under four different parking policies in a garage simulated at a temperature of 0.5.
  • ...and 1 more figures