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.

This chapter summarizes the presentations of speakers addressing such issues during the Automated Vehicles Symposium 2020 (AVS20) held virtually on July 27–30, 2020. These speakers participated in the break-out session titled “Artificial Intelligence for Automated Vehicle Control and Traffic Operations: Challenges and Opportunities”. The corresponding discussion and recommendations are presented in terms of the lessons learned and the future research directions to be adopted to benefit from AI in order to develop safer and more efficient connected and automated vehicles (CAV). This session was organized by the Transportation Research Board (TRB) Committee on Traffic Flow Theory and Characteristics (ACP50) and the TRB Committee on Artificial Intelligence and Advanced Computing Applications (AED50).

This paper presents an integrated modelling framework to capture pedestrian walking behaviour in congested and uncongested conditions. The framework is built using a combination of concepts from the Social Force model (basic one-to-one interaction), behavioural heuristics (physiological and cognitive constraints), and materials science (multi-body potential concept). The approach is ultimately designed to capture pedestrian interactions in transit stations. Due to the lack of available trajectory data of pedestrians within transit stations, the model is calibrated using pedestrian trajectory data from narrow bottleneck and bidirectional flow experiments provided by the Delft University of Technology. These two scenarios were chosen due to the frequency with which they occur in transit stations. The integrated modelling framework reproduced similar trajectory patterns observed in the experiments which encouraged a transit station simulation in an environment similar to that at the Foggy-Bottom METRO station in Washington, D.C.