Cyclists and other types of road users who do not adhere to lane discipline pose a challenge in microscopic traffic simulation. In most software, the models are adapted to increase the lateral flexibility of road users, either through introducing sub-lanes (SUMO) or introducing a continuous lateral axis (PTV Vissim). These solutions enable the simulation of some behaviors, such as passing within the same driving lane. However, other behaviors exhibited by these flexible road users, including switching between cycling infrastructure, the roadways, and the sidewalk, riding against the given direction of travel, and selecting unexpected pathways to cross at intersections, remain difficult to simulate. This paper presents a modeling approach for cyclists, users of micro-mobility modes, and other non-lane-based road users. This method uses the concept of guidelines, or desire lines, that represent the intended path of non-lane-based road users. Guidelines are the same in form as the center line of road (sub-)lanes. Instead of following these lines precisely, the guideline is used to determine the desired direction for the road user in the next time step, which is used as input into an adapted social force type model. The movement and interaction model is formulated based on the NOMAD model for pedestrian dynamics. The single acceleration vector is divided into a speed component and a direction component that are calibrated and validated using trajectory data from cyclists at four signalized intersections in Munich, Germany. Maximum Likelihood Estimation (MLE) is used to estimate the model parameters and k-fold cross-validation is used to evaluate the modeling approach. The results are discussed and an outlook for future research is presented.


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    Title :

    A movement and interaction model for cyclists and other non-lane-based road users


    Contributors:


    Publication date :

    2023




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    Unknown




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