Browsing by Author "Farag, Natalie"

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    LiDAR-Driven Calibration of Microscopic Traffic Simulation for Balancing Operational Efficiency and Prediction of Traffic Conflicts
    (University of Waterloo, 2025-01-21) Farag, Natalie; Bachmann, Christian; Fu, Liping
    Microscopic traffic simulation is a proactive tool for road safety assessment, offering an alternative to traditional crash data analysis. Microsimulation models, such as PTV VISSIM, replicate traffic scenarios and conflicts under various conditions, thereby aiding in the assessment of driving behavior and traffic management strategies. When integrated with tools like the Surrogate Safety Assessment Model (SSAM), these models estimate potential conflicts. Research often focuses on calibrating these models based on traffic operation metrics, such as speed and travel time, while neglecting safety performance parameters. This thesis investigates the effects of calibrating microsimulation models for both operational metrics including travel time and speed, and safety metrics including traffic conflicts and Post Encroachment Time (PET) distribution, using LiDAR sensor data. The calibration process involves three phases: performance calibration, performance and safety calibration, and only safety calibration. The results show that incorporating safety-focused parameters enhances the model's ability to replicate observed conflict patterns. The study highlights the trade-offs between operational efficiency and safety, with adjustments to parameters like standstill distance improving safety outcomes without significantly compromising operational metrics. Furthermore, there is a substantial difference in the calibrated minimum distance headway for the safety model, highlighting the trade-off between operational efficiency and safety. While the operational calibration focuses on optimizing flow, the safety calibration prioritizes realistic conflict simulation, even at the cost of reduced flow efficiency. The research emphasizes the importance of accurately simulating real-world driver behavior through adjustments to parameters like the probability and duration of temporary lack of attention.