Open Collections

Abu Khuzam, Elena

University of British Columbia

Jaywalking behavior represents a major safety concern, especially in traffic environments with intense pedestrian activity. Despite the influence of this behavior on crash risk given that drivers have unexpected interactions with pedestrians and must take additional evasive actions, limited pedestrian models have accounted for jaywalking behavior. This research uses Multiagent Adversarial Inverse Reinforcement Learning (MAAIRL) within a Markov game framework to model road user behavior in jaywalking scenarios at signalized intersections, offering a detailed representation of the dynamic and complex decision-making strategies of pedestrians and drivers in these situations. This approach enables obtaining reward functions that can be used to make inferences about their behaviors and optimal policies that represent the actual sequences of decisions, which can be employed in developing microsimulation models. Results show that jaywalking pedestrians exhibited erratic movements, with higher acceleration rates and unpredictable paths. In contrast, non-jaywalking pedestrians showed more predictable behavior with smaller variations in their paths and greater distances from vehicles while crossing. Additionally, jaywalking scenarios led to smaller time-to-collision (TTC) and post-encroachment time (PET) values, reduced minimum distances, and faster pedestrian movements compared to non-jaywalking scenarios, which shows the increased crash risks associated with jaywalking. Finally, the MAAIRL model was able to adequately learn the behaviors associated with both non-jaywalking and jaywalking pedestrians. This shows the potential of this framework to model complex real-world scenarios. Moreover, these findings underscore the importance of improving pedestrian simulation models to take into account the distinct behavioral patterns associated with jaywalking, and such advancements can facilitate a more comprehensive examination of the safety impacts in busy pedestrian environments.

Text

2025-04-17

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/90688

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/