This researchextracts trajectories of non-directional micromobility traffic (pedestrians,cyclists, and mopeds) in a shared right-of-way urban space, applying aVoronoi-based area-weighted framework to construct Network Fundamental Diagrams(NFDs). Using an aggregation technique that weights by link length, we exploretwo Voronoi-based approaches for generating NFDs from microscopic data: astandard mixed-mode approach and a novel mode-isolated approach. Resultsdemonstrate that both methods accurately compute macroscopic traffic measures,and mode-isolated approach, in particular, reveals unique contribution of eachmode to the NFD. Cyclists and mopeds drive performance dominance, shaping theNFD even when pedestrians dominate traffic composition (mode-share dominance).This study empirically validates existence of mixed two-dimensional traffic NFDincluding critical capacities and jam densities and highlights the aggregateimpact of individual modes. The findings underscore the potential ofarea-weighted aggregation to account for heterogeneity in urban mixed traffic,offering insights into capacity and efficiency evaluations for non-motorizedtransport systems.

Pedestrians are among the travelers most vulnerable to collisions that are associated with high fatality and injury rates. The increasing rate of urbanization and mixed land-use construction make walking (along with other non-motorized travel) a predominant transportation mode with a wide variety of behaviors expected. Because of the inherent safety concerns seen in pedestrian transportation infrastructures, especially those with conflicting multimodal movements expected (crosswalks, transit platforms, etc.), it is important that pedestrian behavior is modeled as a risk-taking stochastic behavior that may lead to errors and thus collision formation. In previous work, the complexity and cost associated with building pedestrian models in a cognitive-based environment weighted down the construction of simulation tools that can capture pedestrian-involved collisions, including those seen in shared space environments. In this paper, a tool that will help evaluate the safety of pedestrian traffic is initiated: an extended modeling framework of pedestrian walking behavior is adopted while incorporating different physiological, physical, and decision-making elements. The focus is on operational decisions (i.e., path choices defined by longitudinal and lateral trajectories) with a pre-specified set of origins and destinations. The model relies on the prospect theory paradigm where pedestrians evaluate their acceleration and directional alternatives while considering the possibility of colliding with other ‘‘particles.’’ Using a genetic algorithm method, the new model is calibrated using detailed trajectory data. This model can be extended to model the interactions between a variety of different modes that are present in different mixed land-use environments.