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Title:

How Likely am I to Find Parking? —Stochastic Models of Parking Process and Probabilistic Estimation of Parking Availability

Accession Number:

01628778

Record Type:

Component

Abstract:

Searching for parking has been a problem faced by many drivers, especially in urban areas. The significance of the parking searching problem has led to an increasing public demand for parking information and services, and has drawn attention from research communities. Parking availability information is highly valued by travelers, and is one of the most important inputs to many parking models. This paper proposes a discrete-time Markov model to describe the parking process, as well as a practical framework to predict future parking occupancy from historical occupancy data alone. The proposed discrete-time Markov model is a close approximation to the well-established continuous-time Markov model for parking, and is more computationally efficient. Mathematical properties of the proposed model are derived analytically under specific conditions. The prediction framework consists of two approaches to first estimate the arrival and departure rates offline from historical occupancy observations alone, and a prediction component that can be engaged both offline or online. The first parameter estimation approach makes use of the analytical properties of the occupancy probability distribution and employs curve fitting techniques; and is suitable when the parking facility is under-saturated. When the parking facility is over-saturated, the second approach applies maximum likelihood and least squares estimation directly based on the proposed discrete-time Markov model. The model and the framework are validated using both simulated and real data. The San Francisco case studies demonstrate that the parameters estimated offline are able to lead to accurate predictions of parking facility occupancy both offline and online.

Supplemental Notes:

This paper was sponsored by TRB committee AHB15 Standing Committee on Intelligent Transportation Systems.

Monograph Accession #:

01618707

Report/Paper Numbers:

17-04956

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Xiao, Jun
Lou, Yingyan
Frisby, Joshua

Pagination:

23p

Publication Date:

2017

Conference:

Transportation Research Board 96th Annual Meeting

Location: Washington DC, United States
Date: 2017-1-8 to 2017-1-12
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Geographic Terms:

San Francisco (California)

Subject Areas:

Highways; Operations and Traffic Management; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2017 Paper #17-04956

Files:

TRIS, TRB, ATRI

Created Date:

Dec 8 2016 11:54AM