Modeling of Microscopic Cyclist Behavior with Path-Planning Algorithms
H. Chu (TU Delft - Civil Engineering & Geosciences)
W Daamen – Mentor (TU Delft - Traffic Systems Engineering)
Alexandra Gavriilidou – Graduation committee member (TU Delft - Traffic Systems Engineering)
Frederik Schulte – Graduation committee member (TU Delft - Transport Engineering and Logistics)
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Abstract
In this thesis, an exploration into the potential use of path-planning algorithms in modeling cyclist behavior is made, and a novel model utilizing such algorithms, incorporating the commonly found overtaking behavior on bike paths, is developed and assessed.
The investigation started with a literature review on the existing behavioral interpretations and findings of bicycle riding, where the cyclists' interaction with the environment and other cyclists is found to be at different task levels of the riding process. Based on the findings, further analysis into how existing techniques replicate the different layers of behavior is made and assessed. During the assessment, this research determined that path-planning algorithms could best be used to replicate the physical steering behavior of cyclists. An investigation into the inner workings of path-planning algorithms is also done, resulting in the final four-layer conceptual framework based on the two-layer operational framework of bicycle riding, adapting the process into mental (perception, goal orientation) and physical (path planning, movement) layers.
With the adapted modeling framework, a model is developed, verified, and assessed with face-validation against real-world trajectory data. The development and verification step provides insights into the inner workings of the model, showcasing how the four layers of the framework are realized, and how the changing of used parameters would affect the intermediate output between the model layers. The face validation consists of two scenarios: a physical steering and pedaling-focused scenario of chicanes, which is a series of bottlenecks, and an overtaking scenario that focuses on the mental process of overtaking decisions. The results showcased that the developed model can create plausible steering and pedaling behaviors in the chicane scenario. However, the model showed lower accuracy and consistency in predicting the mental overtaking maneuvers.
The assessment result of the developed model showcased the strength of path-planning algorithms in augmenting the existing model with the physical steering capability of the cyclists. The limited accuracy in the overtaking scenario highlights the importance of capturing the mental process of bicycle riding. Future work could further refine the mental layers of the framework, specifically the goal orientation process. The adapted modeling framework also provides a new direction and foundation for further work on the path-planning additions and improvement of bicycle behavioral modeling.