@article{ref1,
title="Microscopic modeling of cyclists interactions with pedestrians in shared spaces: a Gaussian process inverse reinforcement learning approach",
journal="Transportmetrica A: transport science",
year="2021",
author="Alsaleh, Rushdi and Sayed, Tarek",
volume="ePub",
number="ePub",
pages="ePub-ePub",
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. <br><br>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.<p /> <p>Language: en</p>",
language="en",
issn="2324-9935",
doi="10.1080/23249935.2021.1898487",
url="http://dx.doi.org/10.1080/23249935.2021.1898487"
}