Dynamic O-D travel time estimation using an artificial neural network

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

Although the minimum Q-D travel time path in a dynamic traffic network can be calculated using standard minimum path algorithms there are a number of transportation applications which require a quick estimate of this information. These applications include such areas as real time vehicle dispatching systems where potential routes between a large number of origins and destinations have to be continually updated for a variety of vehicles throughout the day. The objective of this paper is to demonstrate the feasibility of using an artificial neural network (ANN) to estimate the O-D travel time in a dynamic traffic network. Three feed forward neural networks were developed to model the travel time behavior during different time periods of the day: AM peak, PM peak and off peak. These ANN models were subsequently trained and tested using a network from the City of Edmonton, Alberta. A comparison of the ANN model with a traditional statistical model is then presented. Lastly, the computational efficiency of the proposed ANN model compared to two shortest path algorithms is demonstrated. The statistical results show that the ANN models are appropriate for estimating dynamic O-D travel times and are significantly faster than the exact minimum path algorithms.

Original languageEnglish (US)
Pages236-242
Number of pages7
StatePublished - 1995
EventProceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference - Seattle, WA, USA
Duration: Jul 30 1995Aug 2 1995

Other

OtherProceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference
CitySeattle, WA, USA
Period7/30/958/2/95

ASJC Scopus subject areas

  • Engineering(all)

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    Fu, L., & Rilett, L. R. (1995). Dynamic O-D travel time estimation using an artificial neural network. 236-242. Paper presented at Proceedings of the 6th 1995 Vehicle Navigation and Information Systems Conference, Seattle, WA, USA, .