TRB Pubsindex
Text Size:

Title:

A Linear Program for System Optimal Parking Reservation Assignment

Accession Number:

01698221

Record Type:

Component

Abstract:

Most studies on traffic assignment ignore the substantial effects of searching for parking on travel times and congestion. Recent work on parking reservation systems provide an alternative to searching for parking by allocating parking spaces while travelers are en-route, or before they depart. The necessary technologies are well-established and generally available, but the network effects of parking reservation are not yet well understood. Using parking reservation systems, system optimal traffic assignment can be combined with parking space allocation to minimize the combination of in-vehicle travel time and time spent walking from parking to the destination. This paper modifies the linear program for system optimal dynamic traffic assignment of round-trip travel to include parking assignment. The parking location, and time spent at the destination, affect the departure time and origin for the return trip. The model is further extended to trip chains for travelers visiting multiple zones before returning to their residence. Numerical results quantify increases in walking time and in-vehicle travel time when primary parking is unavailable.

Supplemental Notes:

This paper was sponsored by TRB committee ADB30 Standing Committee on Transportation Network Modeling.

Report/Paper Numbers:

19-00591

Language:

English

Corporate Authors:

Transportation Research Board

Authors:

Levin, Michael W

Pagination:

6p

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:

References

Subject Areas:

Highways; Operations and Traffic Management; Planning and Forecasting

Source Data:

Transportation Research Board Annual Meeting 2019 Paper #19-00591

Files:

TRIS, TRB, ATRI

Created Date:

Dec 7 2018 9:49AM