00715620

Component

http://onlinepubs.trb.org...trr/1995/1510/1510-002.pdf

https://scholar.google.co...wara&publication_year=1995

The relationships between traffic flow variables play important roles in traffic engineering. They are used not only in basic traffic flow analyses but also in some macroscopic traffic flow simulation models. For many decades, various mathematical formulations that describe the relationships among density, flow, and speed have been proposed, including multiregime models. Previously, the best mathematical curve was determined by trying several different formulas and applying regression analysis. In these processes, one must specify in advance which mathematical formula should be adopted and where it should be shifted to another in a multiregime model. Neural network models have some promising abilities to represent nonlinear behaviors accurately and to self-organize automatically. A procedure for describing the macroscopic relationships among traffic flow variables using some neural network models is presented. First, a Kohonen feature map model was introduced to convert original observed data points into fewer, more uniformly distributed ones. This conversion improved regression precision and computational efficiency. Next, a multilayer neural network model was introduced to describe the two-dimensional relationships. The model was effective in describing the nonlinear and discontinuous characteristics among traffic flow variables. It was unnecessary to specify the regression curves and the transition points in advance. The multiple correlation coefficients resulting from the model were better than those resulting from a conventional nonlinear equation.

This paper appears in Transportation Research Record No. 1510, Traffic Flow Theory and Characteristics with Applications for Intelligent Transportation System Technologies. Distribution, posting, or copying of this PDF is strictly prohibited without written permission of the Transportation Research Board of the National Academy of Sciences. Unless otherwise indicated, all materials in this PDF are copyrighted by the National Academy of Sciences. Copyright © National Academy of Sciences. All rights reserved

01399815

English

p. 11-18

1995

Transportation Research Record

0309062039

Figures (10) ; References (10) ; Tables (3)

Coefficients; Correlation analysis; Neural networks; Nonlinear systems; Traffic density; Traffic flow; Traffic speed; Variables

Discontinuity; Nonlinearity

Data and Information Technology; Highways; Operations and Traffic Management; I71: Traffic Theory

TRIS, TRB

Jan 19 1996 12:00AM

• ANOTHER LOOK AT A PRIORI RELATIONSHIPS AMONG TRAFFIC FLOW CHARACTERISTICS (WITH DISCUSSION AND CLOSURE)
• ARTERIAL INCIDENT DETECTION INTEGRATING DATA FROM MULTIPLE SOURCES
• DRIVER DECELERATION BEHAVIOR ON A FREEWAY IN NEW ZEALAND
• ESTIMATING INTERSECTION TURNING MOVEMENT PROPORTIONS FROM LESS-THAN-COMPLETE SETS OF TRAFFIC COUNTS
• EVENT-BASED SHORT-TERM TRAFFIC FLOW PREDICTION MODEL
• MICROSCOPIC MODELING OF TRAFFIC WITHIN FREEWAY LANES
• STATISTICAL ANALYSIS AND VALIDATION OF MULTIPOPULATION TRAFFIC SIMULATION EXPERIMENTS
• STATISTICAL ANALYSIS OF DAY-TO-DAY VARIATIONS IN REAL-TIME TRAFFIC FLOW DATA