00935390

Component

https://doi.org/10.3141/1804-23

http://scholar.google.com...Teng&publication_year=2002

The Dempster-Shafer theory for data fusion and mining in support of advanced traffic management is introduced and tested. Dempster-Shafer inference is a statistically based classification technique that can be applied to detect traffic events that affect normal traffic operations. It is useful when data or information sources contribute partial information about a scenario, and no single source provides a high probability of identifying the event responsible for the received information. The technique captures and combines whatever information is available from the data sources. Dempster's rule is applied to determine the most probable event--as that with the largest probability based on the information obtained from all contributing sources. The Dempster-Shafer theory is explained and its implementation described through numerical examples. Field testing of the data fusion technique demonstrated its effectiveness when the probability masses, which quantify the likelihood of the postulated events for the scenario, reflect current traffic and weather conditions.

This paper appears in Transportation Research Record No. 1804, Transportation Data and Information Technology Research.

English

p. 173-178

2002

Transportation Research Record

0309077303

Figures (2) ; References (12) ; Tables (4)

Advanced traffic management systems; Data fusion; Data mining; Decision support systems; Field tests; Probability; Traffic flow theory

Most probable event (Traffic)

Data and Information Technology; Highways; Operations and Traffic Management; I73: Traffic Control

TRIS, TRB, ATRI

Dec 13 2003 12:00AM

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