On the Transition to an Auction-based Intelligent Parking Assignment System

TL;DR

This study evaluates an auction-based parking system through simulations, showing it improves traffic flow and influences parking costs, with higher adoption leading to better performance but increased expenses for users.

Contribution

It provides an empirical analysis of auction-based parking assignment effects on traffic, costs, and user behavior using simulation experiments.

Findings

Traffic flow improves with higher market penetration.

Participants can park closer to preferred lots.

Non-participants face higher parking costs.

Abstract

Finding a free parking space in a city has become a challenging task over the past decades. A recently proposed auction-based parking assignment can alleviate cruising for parking and also set a market-driven, demand-responsive parking price. However, the wide acceptance of such a system is far from certain. To evaluate the merits of auction-based parking assignment, we assume that drivers have access to a smartphone-based reservation system prior to its mandatory introduction and thus have the opportunity to test and experience its merits voluntarily. We set our experiment as Eclipse SUMO simulations with different rates of participants and non-participants to check how different market penetration levels affect the traffic flow, the performance of the auction-based assignment system, and the financial outcomes. The results show that the auction-based system improves traffic flow with increasing penetration rates, allowing participants to park gradually closer to their preferred parking lots. However, it comes with a price; the system also increases parking expenditures for participants. Interestingly, non-participating drivers will face even higher parking prices. Consequently, they will be motivated to use the new system.