Abstract
The short-term interactions between on-street and garage parking policies and the associated parking pricing can be highly influential to the searching-for-parking traffic and the overall traffic performance in the network. In this paper, we develop a macroscopic on-street and garage parking decision model and integrate it into a traffic system with an on-street and garage parking search model over time. We formulate an on-street and garage parking-state-based matrix that describes the system dynamics of urban traffic based on different parking-related states and the number of vehicles that transition through each state in a time slice. This macroscopic modeling approach is based on aggregated data at the network level over time. This leads to data collection savings and a reduction in computational costs compared to most of the existing parking/traffic models. This easy to implement methodology can be solved with a simple numerical solver. All parking searchers face the decision to drive to a parking garage or to search for an on-street parking space in the network. This decision is affected by several parameters including the on-street and garage parking fees. Our model provides a preliminary idea for city councils regarding the short-term impacts of on-street and garage parking policies (e.g., converting on-street parking to garage parking spaces, availability of garage usage information to all drivers) and parking pricing policies on: searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental impact), as well as the revenue created for the city by the hourly on-street and garage parking fee rates. This model can be used to analyze how on-street and garage parking policies can affect traffic performance; and how traffic performance can affect the decision to use on-street or garage parking. The proposed methodology is illustrated with a case study of an area within the city of Zurich, Switzerland.
Highlights
As the population in urban areas is increasing, more and more cars need to nd parking spaces in city centers. ese vehicles normally have the choice between on-street and garage parking
O -street parking is referred to as garage parking. e macroscopic model is built on a tra c system with a parking search model over time
What is the ideal ratio between on-street and garage parking fees to attract drivers such that they avoid cruising for on-street parking? We study the impacts of a limited on-street and garage capacity in combination with di erent on-street and garage parking pricing parameters, i.e., due to the limited number of garage parking spaces and di erent related pricing schemes, congestion might occur and a ect the tra c performance in the network
Summary
As the population in urban areas is increasing, more and more cars need to nd parking spaces in city centers. ese vehicles normally have the choice between on-street and garage parking. Benenson et al [19] develop an agent-based parking model for a city by simulating the behavior of each driver in comparison to our macroscopic framework based on aggregated data Further studies use this agent-based parking model to analyze different parking policies [20], estimate city parking patterns [21], explore cruising-for-parking [22, 23] and evaluate parking planning projects for large parking garages [24]. Liu and Geroliminis [30] use an MFD approach to investigate how cruising-for-onstreet-parking influences the commuters’ morning peak and develop a dynamic parking pricing model to reduce total social cost They do not consider garage parking in its framework. With limited data collection efforts, our macroscopic on-street and garage parking decision model shows the influence of different on-street and garage parking pricing ratios on the average searching time/distance.
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