An Artificial Intelligence-Based Approach for Simulating Pedestrian Movement

Abstract

This paper proposes a novel approach for simulating pedestrian movement behavior based on artificial intelligence technology. Within this approach, a large volume of microscopic pedestrian movement behavior types were collected and encapsulated into an artificial neural network via network training. The trained network was then fed back into a simulation environment to predict the pedestrian movement. Two simulation experiments were conducted to evaluate the performance of the approach. First, a pedestrian-collision-avoidance test was conducted, and the results showed that virtual pedestrians with learned pedestrian behavior can move reasonably to avoid potential collisions with other pedestrians. In addition, a critical parameter, i.e., defined as "reacting distance" and determined to be 2.5 m, represented the boundary of the collision buffer zone. Second, a pedestrian counterflow in a road-crossing situation was simulated, and the results were compared with the real-life scenario. The comparison revealed that the pedestrian distributions, erratic trajectories, and density-speed fundamental diagram in the simulation are reasonably consistent with the real-life scenario. Furthermore, a quantitative indicator, i.e., the relative distance error, was calculated to evaluate the simulation error of pedestrians' trajectories between the simulation and the real-life scenario, the mean of which was calculated to be 0.322. This revealed that the simulation results were acceptable from an engineering perspective, and they also showed that the approach could reproduce the lane-formation phenomenon. We considered the proposed approach to be capable of simulating human-like microscopic pedestrian flow.Index Terms-Pedestrian flow, artificial intelligence, neural networks, lane formation.

Can we learn where people go?

“…There are first approaches to apply machine learning models in the context of pedestrian dynamics. The applications range from crowd counting and density estimation [3] to the prediction of pedestrian movement [4]. Furthermore, destination prediction aims to predict the destinations of people of a certain audience based on trajectories and prior knowledge of possible destinations [5,6].…”

Artificial neural network based modeling on unidirectional and bidirectional pedestrian flow at straight corridors

“…To better illustrate the reliability of the proposed model, we also make a quantitative comparison of trajectories. The relative distance error (RDE) [10] at time step for pedestrian is defined by…”

“…In order to consider the terrain factors, neural network was embedded into pedestrians ensuring agents motion more intelligent and realistic. Ma et al [10] proposed a novel approach for simulating pedestrian movement behavior based on artificial neural network. The neural network was trained utilizing experimental data collected from a realistic road crossing with bidirectional flow.…”

An Artificial Neural Network Framework for Pedestrian Walking Behavior Modeling and Simulation

“…In the research of [11], a multi-layer perceptron ANN is used to model the operational behavior of pedestrians in continuous space for a bidirectional flow at a crosswalk. The input data comprises the features of the agent's movement and perception.…”

“…For our prototype ANN definition, we followed the approach of [11] and modeled a multi-layer perceptron ANN. We used TensorFlow and its Python API [15].…”