Citation

Alsaleh R, Sayed T. Transportmetrica A: Transp. Sci. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Informa - Taylor and Francis Group)

DOI

10.1080/23249935.2021.1898487

PMID

unavailable

Abstract

This study presents a microsimulation-oriented framework for modeling cyclists' interactions with pedestrians in shared spaces. The objectives of this study are to 1) infer how cyclists in head-on and crossing interactions rationally assess and make guidance decisions of acceleration and yaw rate, and 2) use advanced Artificial Intelligent (AI) techniques to model road-user interactions. The Markov Decision Process modeling framework is used to account for road-user rationality and intelligence. Road user trajectories from three shared spaces in North America are extracted by means of computer-vision algorithms. Inverse Reinforcement Learning (IRL) algorithms are utilized to recover continuous linear and nonlinear Gaussian-Process (GP) reward-functions (RFs). Deep Reinforcement Learning is used to estimate optimal cyclist policies.

RESULTS demonstrated that the GP-RF captures the more complex interaction behaviour and accounts for road-user heterogeneity. The GP-RF led to more consistent inferences of road-users behaviour and accurate predictions of their trajectories compared with the linear RF.

Language: en

Keywords

agent-based model; bicycle; cyclist-pedestrian interaction; reward function; Shared space modeling