01152542

Component

https://doi.org/10.3141/2183-13

http://scholar.google.com...riou&publication_year=2010

Current advances in artificial intelligence are providing new opportunities for utilizing the enormous amount of data available in contemporary urban road surveillance systems. Several approaches, methodologies, and techniques have been presented for analyzing and forecasting traffic counts because such information has been identified as vital for the deployment of advanced transportation management and information systems. In this paper, a meta-analysis framework is presented for improving forecasted information of traffic counts, based on an adaptive data processing scheme. In particular, a framework for combining traffic count forecasts within a Mamdani-type fuzzy adaptive optimal control scheme is presented and analyzed. The proposed methodology treats the uncertainty pertaining to such circumstances by augmenting qualitative information of future traffic flow states (and values) with a knowledge base and a heuristic optimization routine that provides dynamic training capabilities, resulting in an efficient real-time forecasting mechanism. Results from the application of the proposed framework on data acquired from realistic signalized urban network data (of Athens, Greece) and for a diversity of locations exhibit its potential.

01324884

10-0330

English

pp 120-128

2010

Transportation Research Record: Journal of the Transportation Research Board

9780309160544

Print

Figures (7) ; References (30) ; Tables (1)

Adaptive control; Fuzzy logic; Heuristic methods; Meta-analysis; Real time data processing; Traffic counts; Traffic forecasting

Advanced transportation management and information systems

Athens (Greece)

Data and Information Technology; Highways; Planning and Forecasting; I72: Traffic and Transport Planning

TRIS, TRB, ATRI

Jan 25 2010 10:12AM

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