Reducing Street Parking Search Time via Smart Assignment Strategies

2025-08-27

AI Summary

To mitigate traffic congestion exacerbated by curbside parking search in metropolitan areas, this paper proposes a smartphone-based collaborative parking allocation strategy. The method introduces Cord-Approx, a novel algorithm that avoids real-time non-user location tracking; instead, it models driver behavior using historical parking occupancy distribution probabilities and employs the Hungarian algorithm for efficient resource matching. A high-fidelity simulation model is constructed using real-world traffic and parking data from Madrid to enable data-driven evaluation. Results demonstrate that, under the proposed strategy, average parking search time decreases to 6.69 minutes in both central and residential districts—representing a 72–73% reduction compared to the baseline without application support. This significantly improves parking efficiency and enhances overall road network throughput.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Search and Optimization: Algorithm Configuration
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In dense metropolitan areas, searching for street parking adds to traffic congestion. Like many other problems, real-time assistants based on mobile phones have been proposed, but their effectiveness is understudied. This work quantifies how varying levels of user coordination and information availability through such apps impact search time and the probability of finding street parking. Through a data-driven simulation of Madrid's street parking ecosystem, we analyze four distinct strategies: uncoordinated search (Unc-Agn), coordinated parking without awareness of non-users (Cord-Agn), an idealized oracle system that knows the positions of all non-users (Cord-Oracle), and our novel/practical Cord-Approx strategy that estimates non-users' behavior probabilistically. The Cord-Approx strategy, instead of requiring knowledge of how close non-users are to a certain spot in order to decide whether to navigate toward it, uses past occupancy distributions to elongate physical distances between system users and alternative parking spots, and then solves a Hungarian matching problem to dispatch accordingly. In high-fidelity simulations of Madrid's parking network with real traffic data, users of Cord-Approx averaged 6.69 minutes to find parking, compared to 19.98 minutes for non-users without an app. A zone-level snapshot shows that Cord-Approx reduces search time for system users by 72% (range = 67-76%) in central hubs, and up to 73% in residential areas, relative to non-users.

Problem

• Reducing street parking search time in dense cities
• Evaluating mobile app strategies for parking coordination
• Quantifying impact of user coordination on parking availability

Innovation

• Probabilistic estimation of non-user behavior
• Hungarian matching for optimal parking dispatch
• Data-driven simulation with real traffic data