Intelligent microscopic models for traffic flow characterization
Date
2025
Authors
Ali, Faryal
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Abstract
In this dissertation, microscopic models for traffic flow characterization are studied. Based on the traffic flow evolution characteristics and aiming to characterize the traffic behavior accurately and realistically, this research focuses on developing realistic traffic flow models to improve traffic safety, efficiency, and pollution control. A traffic model based on driver response is introduced considering both driver reaction and sensitivity. Driver sensitivity includes typical, sluggish, or aggressive drivers. Then the pavement condition is investigated using the pavement condition index (PCI). The impact of fog on visibility is a major factor affecting traffic congestion and safety. Thus, the traffic behavior based on visibility during foggy weather is also investigated. In addition, the recent introduction of connected autonomous vehicles (CAVs) has had a significant impact on road networks. Therefore, a spring-mass based traffic model to evaluate human-driven vehicle (HV), autonomous vehicle (AV), and CAV behavior on a horizontal curve is proposed. Further, CAV behavior at bottlenecks considering cyberattacks is investigated. This dissertation also provides an energy consumption model considering driver energy-saving awareness. The performance of traffic models is presented and compared with the intelligent driver (ID) model, and traffic stability is analyzed. The results demonstrate the advantages of the proposed approach.