Dynamic route guidance using maximum flow theory and its MapReduce implementation

Author

Ye, Peijun ; Chen, Cheng ; Zhu, Fenghua

Author_Institution

State Key Lab. for Intell. Control & Manage. of Complex Syst., Inst. of Autom., Beijing, China

fYear

2011

fDate

5-7 Oct. 2011

Firstpage

180

Lastpage

185

Abstract

Road traffic load balancing can avoid network congestion and improve traffic efficiency. This paper proposes a method of dynamic route guidance based on Maximum Flow Theory to balance traffic load of road network. A modified Ford-Fulkerson algorithm is used for searching the optimal route. In addition, the algorithm is implemented by using MapReduce primitives, which introduces Cloud Computing Platform for large-scale traffic network guidance. Computational experimental environment is built by integrating Artificial Transportation Systems (ATS) and Hadoop. Results in ATS and performances on Hadoop show that the method proposed can improve the traffic situation effectively.

Keywords

cloud computing; road traffic; traffic engineering computing; Ford-Fulkerson algorithm; Hadoop; MapReduce; artificial transportation systems; cloud computing platform; dynamic route guidance; maximum flow theory; road traffic load balancing; Cloud computing; Computers; Heuristic algorithms; Roads; Vehicle dynamics; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Transportation Systems (ITSC), 2011 14th International IEEE Conference on

Conference_Location

Washington, DC

ISSN

2153-0009

Print_ISBN

978-1-4577-2198-4

Type

conf

DOI

10.1109/ITSC.2011.6082927

Filename

6082927