An Evaluation of Information Sharing Parking Guidance Policies Using a Bayesian Approach

TL;DR

This paper evaluates how different parking guidance policies and the market penetration of probe cars affect the accuracy of real-time parking occupancy data, using a Bayesian simulation approach to optimize information gathering.

Contribution

It introduces a Bayesian simulation framework to assess and optimize parking guidance policies, including a near-optimal strategy for maximizing information gain.

Findings

Efficient policies can offset low probe car market penetration.

Trade-offs exist between exploration for information and exploitation for accuracy.

Simulation results guide smart parking system design.

Abstract

Real-time parking occupancy information is critical for a parking management system to facilitate drivers to park more efficiently. Recent advances in connected and automated vehicle technologies enable sensor-equipped cars (probe cars) to detect and broadcast available parking spaces when driving through parking lots. In this paper, we evaluate the impact of market penetration of probe cars on the system performance, and investigate different parking guidance policies to improve the data acquisition process. We adopt a simulation-based approach to impose four policies on an off- street parking lot influencing the behavior of probe cars to park in assigned parking spaces. This in turn effects the scanning route and the parking space occupancy estimations. The last policy we propose is a near-optimal guidance strategy that maximizes the information gain of posteriors. The results suggest that an efficient information gathering policy can compensate for low penetration of connected and automated vehicles. We also highlight the policy trade-off that occur while attempting to maximize information gain through explorations and improve assignment accuracy through exploitations. Our results can assist urban policy makers in designing and managing smart parking systems.