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 the average search time by 76% in Culture & Transport Hubs, and 72% in Residential & Light Industry, relative to non-users.

About Behafarid Hemmatpour

Behafarid Hemmatpour is a second-year PhD researcher at IMDEA Networks Institute (and UC3M), working with large-scale mobility data to build practical solutions for intelligent transportation and computational epidemiology. Her work spans spatiotemporal analytics and machine learning, including federated learning; the design of data-driven methods for smart street-parking management and urban traffic optimization, and predictive models that improve epidemic forecasting. She evaluates ideas through numerical simulations and real-world datasets, releasing reproducible, open-source code to support community use and extension. Before her PhD, Behafarid earned an MSc in Statistical Physics and Complex Systems at Shiraz University, where she investigated human mobility patterns and modeled cities as complex systems, work that seeded her current focus on scalable, data-driven methods for urban systems and public health.

Este evento se impartirá en inglés