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Title:

A Continuum Approximation Approach for Flexible Parking Reservation System Management

Accession Number:

01697869

Record Type:

Component

Abstract:

Reservation-based parking system has the merit of eliminating vehicles cruising for parking. While the authors have seen many long-period (e.g., daily) parking reservation services in the market, the short-period (e.g.,hourly) parking reservation remains a huge challenge due to the high uncertainty of customer arrival and departure. To mitigate the service failure caused by random late departures of customers, the authors propose a new flexible reservation mechanism where the reservation is no longer binding to a specific location at a specific time, but with a certain tolerance of predetermined spatiotemporal flexibility instead. With a well-designed pricing instrument for such parking flexibility, customers can coordinate to significantly reduce the reservation failure rate, resulting in an optimal system equilibrium benefiting the entire society. Due to the complex nature of such system, a continuum approximation framework is used to provide tractable analysis for a large scale urban parking system. The authors can successfully provide accurate system management decision support with bounded optimality gap and analytical insights.

Supplemental Notes:

This paper was sponsored by TRB committee ABE50 Standing Committee on Transportation Demand Management.

Report/Paper Numbers:

19-03922

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Wang, Xiaotian
Wang, Xin

Pagination:

7p

Publication Date:

2019

Conference:

Transportation Research Board 98th Annual Meeting

Location: Washington DC, United States
Date: 2019-1-13 to 2019-1-17
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

Figures; References; Tables

Subject Areas:

Highways; Operations and Traffic Management; Vehicles and Equipment

Source Data:

Transportation Research Board Annual Meeting 2019 Paper #19-03922

Files:

TRIS, TRB, ATRI

Created Date:

Dec 7 2018 9:40AM