An Optimal Adaptive Routing Algorithm for Large-Scale Stochastic Time-Dependent Networks

Gao, Song; University Transportation Centers Program (U.S.); University of Massachusetts at Amherst

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An Optimal Adaptive Routing Algorithm for Large-Scale Stochastic Time-Dependent Networks

2015-12-30
By Gao, Song

[PDF-231.81 KB]

English

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Details:

Creators:

Gao, Song
Corporate Creators:

University Transportation Centers Program (U.S.) ; University of Massachusetts at Amherst

University Transportation Centers Program (U.S.) ; University of Massachusetts at Amherst Less -
Corporate Contributors:

New England Transportation Consortium ; United States. Dept. of Transportation. Research and Innovative Technology Administration

New England Transportation Consortium ; United States. Dept. of Transportation. Research and Innovative Technology Administration Less -
Subject/TRT Terms:

[+]

Algorithms Highways I72: Traffic And Transport Planning Planning And Forecasting Real Time Information Route Choice Routes And Routing Stochastic Processes Time Dependence
Resource Type:

Tech Report
Geographical Coverage:

United States
TRIS Online Accession Number:

01590495
Edition:

Final Report
Corporate Publisher:

New England University Transportation Center
Abstract:

The objective of this project is to develop an efficient algorithm and its computer implementation for the optimal adaptive routing problem that is practical in large-scale real-life networks, where a traveler could revise the route choice based upon en route traffic information. Existing adaptive routing algorithms are for explorative purpose and can only be applied to hypothetical and simplified networks. In this project, important changes will be made to make the algorithm practical in real-life networks, in three major areas: memory, running time, and realistic features. The algorithm will be implemented and tested on both randomly generated networks and a number of real-life large networks, including those from the Pioneer Valley Planning Commission (PVPC) of Massachusetts, and Stockholm, Sweden. The developed algorithm is an important building block for the route choice module of an advanced traffic prediction model, and is also the intelligent core of a route guidance system.
Format:

PDF
Funding:

UMAR24-19A
Collection(s):

University Transportation Centers US Transportation Collection
Main Document Checksum:

[+]

urn:sha256:e2a3585a0673aec6326fbe655b04a76fc033d270ce8d4a964f1a73d87357e59c
Download URL:

https://rosap.ntl.bts.gov/view/dot/38992/dot_38992_DS1.pdf
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An Optimal Adaptive Routing Algorithm for Large-Scale Stochastic Time-Dependent Networks

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