Searching for Parking in a Busy Downtown District An Agent-based Computational and Analytical Model
Résumé
Finding a place to park one's car is a serious issue in contemporary urban mobility. Despite the importance of the topic (30% of cars might be cruising for parking in many large cities) and the central role given to parking policies, surprisingly little is known about the basic laws governing the search time. We present a novel agent-based approach combining numerical simulations and theoretical considerations to model cars cruising for on-street parking in busy down-town districts. The approach is premised on the idea that, rather than parking at the first vacant spot that they encounter, drivers may be more or less prone to parking on a given spot, depending on their perceptions of its characteristics (notably its distance to their destination and its cost). This spot-specific parking probability is quantified by means of a scalar variable, the 'attractiveness'. On this premise, we show that this problem can be solved using an exact formula for the stationary state and depends on the topology of the streets. This is demonstrated by comparing our theoretical results with a stochastic in-silico model and the method is illustrated with the case of the city centre of Lyon. Finally, the relationship between the search time and the spatial modulation of the attractiveness of parking spots is explored. We find that such a modulation, which could in practice be enforced by targeted parking policies at the level of individual streets, dramatically affects the parking search time, which paves the way for a more efficient control over occupancies and cruising times in on-street parking networks.
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