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<p>Uncertainty is a fundamental feature of traffic and transportation systems. It arises from fluctuating travel demand, varied traveler behavior, and gaps or inconsistencies in available data. Individual decisions, such as departure time, route choice, or mode choice, are often based on personal perception and qualitative information rather than precise numerical data. Traditional mathematical methods face difficulties in modeling human perception and decision-making. Fuzzy logic (Zadeh, [388–395]), through fuzzy sets and linguistic variables, provides a mathematical framework for representing imprecision, human perceptions, and approximate reasoning. Pappis and Mamdani [271] were the first to apply fuzzy logic to a traffic engineering problem, developing a fuzzy controller for an isolated intersection. Its success stimulated broader use of fuzzy logic in transportation. Over the past fifty years, fuzzy logic has been applied to traffic flow modeling, network dynamics, signal control, ramp metering, incident detection, public transport operations, vehicle routing and scheduling, air traffic control, airport surface operations, railways, and inland water and maritime transportation. This paper reviews fuzzy logic applications in transportation over five decades, examines approaches across modes, discusses the evolution from rule-based models to hybrid and Type-2 fuzzy systems, and outlines limitations and future research directions.</p>

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Driving Through Uncertainty: Fifty Years of Fuzzy Logic in Traffic and Transportation

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Dušan Teodorović

摘要

Uncertainty is a fundamental feature of traffic and transportation systems. It arises from fluctuating travel demand, varied traveler behavior, and gaps or inconsistencies in available data. Individual decisions, such as departure time, route choice, or mode choice, are often based on personal perception and qualitative information rather than precise numerical data. Traditional mathematical methods face difficulties in modeling human perception and decision-making. Fuzzy logic (Zadeh, [388–395]), through fuzzy sets and linguistic variables, provides a mathematical framework for representing imprecision, human perceptions, and approximate reasoning. Pappis and Mamdani [271] were the first to apply fuzzy logic to a traffic engineering problem, developing a fuzzy controller for an isolated intersection. Its success stimulated broader use of fuzzy logic in transportation. Over the past fifty years, fuzzy logic has been applied to traffic flow modeling, network dynamics, signal control, ramp metering, incident detection, public transport operations, vehicle routing and scheduling, air traffic control, airport surface operations, railways, and inland water and maritime transportation. This paper reviews fuzzy logic applications in transportation over five decades, examines approaches across modes, discusses the evolution from rule-based models to hybrid and Type-2 fuzzy systems, and outlines limitations and future research directions.