00822755

Component

https://doi.org/10.3141/1774-04

http://scholar.google.com...ovic&publication_year=2001

Fuzzy inference has become a popular approach to modeling systems in which uncertainties associated with human perception and decision are present. Its use for the modeling framework of travel demand prediction is promising. The possibility of applying fuzzy inference to the problem of predicting bus ridership at individual stops is examined. The required properties for such a model are identified, and the mathematics of fuzzy inference is examined. The factors that may cause transit use at individual stops are identified, and their relationships to ridership are modeled using the hierarchically structured fuzzy rule basis. This fuzzy rule-based model is similar to that of the cross-classification approach, with the boundaries of the classes being fuzzy. The artificial neural network and regression methods are used to model the same problem, and the results are compared with those of the fuzzy inference method. The data used for calibration are obtained from a study of actual bus stops in Delaware. Although no conclusion is drawn as to which approach is superior, fuzzy inference provides an alternative to the traditional regression approach in which the phenomenon's causality is complicated yet the behavior of the output must be consistent with commonsense knowledge. In particular, it is suited for problems in which the actual volume to be predicted fluctuates widely. Such problems require strength of logical explanatory power and understanding of the pattern rather than accuracy in fitting the data.

This paper appears in Transportation Research Record No. 1774, Artificial Intelligence and Intelligent Transportation Systems.

English

p. 25-35

2001

Transportation Research Record

0309072352

Figures (5) ; References (11)

Alternatives analysis; Bus stops; Bus transit; Case studies; Forecasting; Fuzzy logic; Neural networks; Regression analysis; Ridership; Travel behavior; Travel demand

Fuzzy inference

Delaware

Data and Information Technology; Highways; Planning and Forecasting; Public Transportation

TRIS, TRB, ATRI

Jan 30 2002 12:00AM

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