Modeling pedestrian-cyclist interactions in shared space using inverse reinforcement learning. (April 2020)

• Record Type: Journal Article
• Authors: Alsaleh, Rushdi; Sayed, Tarek
• Is Part Of: Transportation research. Volume 70(2020)
• Page Start: 37
• Page End: 57
• Publication Date: 2020-04
• Subjects: Shared space modeling -- Overtaking behavior -- Following behavior -- Simulation -- Cyclist and pedestrian -- Reward function; Automobile drivers -- Psychology -- Periodicals; Automobile driving -- Psychological aspects -- Periodicals; Transportation -- Psychological aspects -- Periodicals; 629.283019
• DOI: 10.1016/j.trf.2020.02.007
• Languages: English
• ISSNs: 1369-8478

Abstract

Highlights: Models are developed to predict following and overtaking behavior in cyclist-pedestrian interactions in shared space. Road users are modeled as utility-based intelligent rational agents. Inverse reinforcement learning algorithms are used to estimate reward functions. Reward functions are formulated as linear combinations of state features. The models are implemented in agent-based microsimulation of shared space. Abstract: The objective of this study is to model the microscopic behaviour of mixed traffic (cyclist-pedestrian) interactions in non-motorized shared spaces. Video data were collected at two locations of Robson Square non-motorized shared space in downtown Vancouver, British Columbia. Trajectories of cyclists and pedestrians involved in interactions were extracted using computer vision algorithms. The extracted trajectories were used to obtain several variables that describe elements of road users' behaviour including longitudinal and lateral distances, speed and speed differences, interaction angle, and cyclist acceleration and yaw rate. The road users behaviour was modeled as utility-based intelligent rational agents using the finite-state Markov Decision Process (MDP) framework with unknown reward functions. The study implemented Inverse Reinforcement Learning (IRL) using two algorithms: the Maximum Entropy (ME) algorithm, and the Feature Matching (FM) algorithm to recover/estimate the reward function weights of cyclists in two types of interactions with pedestrians: following and overtaking interactions. Reward function weights infer cyclist preferences during their interactions with pedestrians in non-motorized shared spaces, and can form the key component in developing agent based microsimulation model for road users. Furthermore, the estimated reward functions were used to estimate cyclists' optimal policy for such interactions. A simulation platform was developed using the estimated reward functions and the cyclist optimal policies to simulate cyclist trajectories for the validation dataset. Results show that the Maximum Entropy (ME) IRL algorithm outperformed the Feature Matching (FM) IRL algorithm, and generally provided reasonable results for modeling such interactions in non-motorized shared spaces, considering the high degrees of freedom in movement and the more-complex road users interactions in such facilities. This research is considered an important step toward developing a full Agent-Based Model (ABM) for road users in shared space facilities to evaluate the safety and efficiency of such facilities.