Traffic estimation and real time prediction using adhoc networks

Author

Batool, Fatima ; Khan, Shoab A.

fYear

2005

fDate

18-18 Sept. 2005

Firstpage

264

Lastpage

269

Abstract

This paper presents the process of developing a multilayer feed forward neural network combined with a backpropagation algorithm for forecasting travel time and traffic congestion. Prediction of travel time and traffic congestion based on past and current traffic information is not straightforward due to among others, the high complexity and ill predictability of traffic process, incorrect observations and different data sources. However it appears that neural networks can be exhaustively used to solve these problems. The system is designed on top of a mesh based communication infrastructure for the mobile nodes to communicate. Communication network comprises of multiple networks, i.e. VHF, UHF. The mesh based communication approach enables easy deployment of the system in real world. OLSR routing protocol is used for establishing an ad hoc network for peer-to-peer-communication

Keywords

ad hoc networks; backpropagation; feedforward neural nets; mobile computing; multilayer perceptrons; peer-to-peer computing; routing protocols; telecommunication traffic; ad hoc networks; backpropagation algorithm; mesh based communication infrastructure; mobile nodes; multilayer feedforward neural network; peer-to-peer-communication; real time prediction; routing protocol; traffic congestion; traffic estimation; travel time forecasting; Backpropagation algorithms; Communication networks; Feedforward neural networks; Feeds; Mobile communication; Multi-layer neural network; Neural networks; Peer to peer computing; Routing protocols; Telecommunication traffic;

fLanguage

English

Publisher

ieee

Conference_Titel

Emerging Technologies, 2005. Proceedings of the IEEE Symposium on

Conference_Location

Islamabad

Print_ISBN

0-7803-9247-7

Type

conf

DOI

10.1109/ICET.2005.1558892

Filename

1558892