Exploring cruising using agent-based and analytical models of parking

Abstract

This article proposes two models to analyse parking search: an analytical model called PARKANALYST and a geosimulation model, termed PARKAGENT, which explicitly accounts for street network and drivers' parking-related decisions. We employ both models to analyse the impact of occupancy rate and demand-to-supply ratio on cruising for parking and to compare the models' outcomes. We estimate the main characteristics of parking dynamics, and find that the spatial effects influence system dynamics starting from an occupancy rate of 85% while become really important for analysing parking when the occupancy rate is above 92-93%.

Downtown curbside parking capacity

“…The treatment of downtown as isotropic simpli…es the analysis considerably since at any point in time tra¢ c ‡ow is the same throughout the downtown area; the analysis then entails solving ordinary rather than partial di¤erential equations. Arnott and Inci further simpli…ed the problem by making some special assumptions 17 that render the di¤erential equation system autonomous (time does not enter the analysis explicitly), which permits phase-plane/state-space analysis. The arrows give the direction of motion, under the assumption that drivers decide whether to travel based on myopic expectations (more precisely, the entry rate at time t is assumed to depend on the perceived full price of a trip, which depends only on tra¢ c conditions at time t).…”

“…In words, when R is positive, which corresponds to saturated curbside parking, it equals the 17 They assume that trip lengths are negative exponentially distributed, which implies that the exit rate from the in-transit pool at time t depends only on the stock of cars in transit and cruising for parking at that point in time, and not on the history of congestion. They also assume that visit durations are negative exponentially distributed, which implies that the exit rate from curbside parking depends only on the amount of curbside parking.…”

“…De…ne the long-run second-best best optimum, conditional on demand in-tensity, to be whichever of the solutions to (17), (18), and (19) yields the highest social surplus.…”

Agent-based day-to-day traffic network model with information percolation

“…The authors point out the close relation between ABS and social science models, behaviour models, route choice models, land use models and activity-based travel demand models. In addition, ABS is employed to explore the parking search and decisions (Levy et al, 2013), and container transport business (Sinha-Ray et al, 2003). Day-to-Day (DTD) Evolutionary Dynamics are used in this study to describe and predict the daily variation of traffic flows and drivers' route adjustment processes.…”

More Effective Use of Urban Space by Autonomous Double Parking

“…According to Mingardo et al [9], parking policy trends have evolved from predict and provide (e.g., creating restricted parking spaces), to command and control (e.g., pricing parking), and, more recently, to managing demand (e.g., differentiated fees, promotion of remote park and go facilities, and massive use of IT to guide people and save cruising time; see initiatives listed in [4,8]). Academia has also studied the problem of parking search and parking economics, contributing with analytical or simulation models [1][2][3][10][11][12][13] and optimizing parking efficiency in scenarios such as curbside [10,14], campus [15], freight traffic [13], or off-street parking [1,16]. Private initiatives offer websites and smartphone apps to find parking in advance (e.g.…”