Title page for ETD etd-1112102-151040


Type of Document Dissertation
Author Hatipkarasulu, Yilmaz
URN etd-1112102-151040
Title A Variable Response Time Lag Module for Car Following Models Using Fuzzy Set Theory
Degree Doctor of Philosophy (Ph.D.)
Department Civil and Environmental Engineering
Advisory Committee
Advisor Name Title
P. Brian Wolshon Committee Chair
Chester G. Wilmot Committee Member
Fereydoun Aghazadeh Committee Member
John B. Metcalf Committee Member
Jerome W. Hoffman Dean's Representative
Keywords
  • driver behavior
  • driver response
  • artificial intelligence
  • fuzzy set theory
  • driver time lag
  • traffic flow
  • car following
Date of Defense 2002-09-11
Availability unrestricted
Abstract
Since the 1950s, car following phenomena have been studied and analyzed, resulting in various models and algorithms. In general, the car following process has been defined as a stimulus-response relationship in which the driver of the following vehicle reacts to the actions of the lead vehicle after a time lag. One of the fundamental assumptions that underlie car following theory is that the driver response time lag is always a constant value for the driver at all times, regardless of level of detail of the model. Assumption of a constant time lag value introduces a number of broad assumptions however, which do not concur with human nature. The definition of driver time lag embodies a high level of imprecision and ambiguity that is difficult to describe using standard mathematical formulations and coefficients. Recently, the fuzzy set theory has been proposed as a potential approach to describe such dynamic phenomenon using a natural language and approximate reasoning.

To address the problems associated with the constant time lag; this study presents a fuzzy response time lag module that can be used in any car following model or algorithm without changing its fundamental mathematical structure. In this dissertation, the development and structure of the module is described including the fuzzy definitions of driving states, fuzzy rule extraction, and fuzzy time lag assignment. Statistical and graphical evaluations of the module performance are also included by integrating the module into two commonly used car following models. In the graphical evaluations, the module improved the model performance significantly by providing more precise timing for the driver response. Both Kolmogorov-Smirnov and Root Mean Squared Error Tests confirmed that the module improves the car following model performance.

The fuzzy T module is the first research effort in defining and modeling the concept of variable response time lag. Accurate description of driver behavior has important impacts in traffic flow analysis that leads to safety and operation of the existing and design of the future transportation systems. This study is a complete example of an artificial intelligence application, providing innovative methodologies and examples of fuzzy system development, training and execution.

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