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

TL;DR

The paper tackles urban cruising for curbside parking by evaluating a market-driven, auction-based parking assignment implemented via a smartphone app. It introduces an ascending-bid simultaneous independent auction (SIA) with local greedy bidding (LGB) to assign free parking spaces and price them dynamically, integrating driver choice through a cost function that blends price and walking distance. Using Eclipse SUMO microscopic simulations across multiple penetration levels and driver types, the study finds that both information-based guidance and auction-based reservations improve traffic flow as penetration increases, while auctions raise parking expenditures for participants (and more so for non-participants). The results support the practical viability of piloting auction-based parking in cities, highlighting trade-offs for drivers and operators and providing guidance for city-specific calibration and deployment.

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.

Figures (15)

• Figure 1: (a) Parking lot operators might require drivers to mandatory use the intelligent parking lot assignment system. In this case, penetration rate rises from 0% to 100% instantly. (b) If drivers can voluntarily participate in a novel system, it leads to a transition with various penetration levels.
• Figure 2: Overview of the assumed auction-based parking assignment system. Drivers can set their destination, preference of walking distance over parking price, and the maximum parking price they are willing to pay in a smartphone app. This application connects to the simultaneous independent ascending auction service to report their local greedy bids (SIA/LGB). An auction corresponds to each free parking space in the area and has a predefined starting price.
• Figure 3: An example of the SIA auction running. Drivers request parking spaces via a smartphone application. The application bids on simultaneous auctions to reserve a parking lot based on the preference ($\beta$) and the destination of the drivers. When the auctions terminates, drivers will be informed where they can find their reserved parking space.
• Figure 4: Parking simulation in Eclipse SUMO. (a) if there are free spaces in a vehicle's designated parking area, the vehicle occupies the first free space. (b) when its designated parking area is full, a vehicle can be rerouted via a ParkingAreaRerouter to the neighboring parking areas.
• Figure 5: Road network and parking zones in the simulated scenario. Colors correspond to the original parking prices in the two parking zones.