Direction-Decision Learning Based Pedestrian Flow Behavior Investigation

Abstract

To investigate the pedestrian flow behavior in corridors, a microscopic simulation model of pedestrian flow is proposed in this paper based on the desired-direction-decision learning and social force model. The proposed model is composed of two parts: direction-decision and walking behavior decision. First, the decision tree model is proposed to predict the walking direction of pedestrians by comparing the prediction and simulation performance of three different models. Then, to avoid collisions between pedestrians and obstacles, the acceleration model and the collision avoidance model are proposed to compute the walking speed. Finally, an computational experiment is conducted to simulate crowd movement in corridors. The experimental results show that the proposed model can suggest the shortest overtaking route for individual pedestrians among four models, and the speed-density relationship fits the experimental data well. The sensitive analysis shows that the lanes in bidirectional pedestrian flow can be formed much more easily if the pedestrians have higher direction changing frequency, and there is an optimal visibility field (2.8) to realize the highest traffic efficiency.

Efficient Prediction of Human Motion for Real-Time Robotics Applications With Physics-Inspired Neural Networks

“…Physics and Neural Network mixed approaches. Past attempts to use machine learning techniques in combination with physical models have applied gradient descend methods to learn the interaction forces [21], used linear regression and NN to predict the direction of motion [22], used evolutionary algorithm to optimise the SFM parameters from video segments [23]. The approach taken in this paper is rather new and is inspired to the simulation of vehicle dynamics by [24].…”

Modeling and Simulation of Departure Passenger’s Behavior Based on an Improved Social Force Approach: A Case Study on an Airport Terminal in China

“…Two major factors affect the route choice: (a) environmental and (b) personal factors. e most influential environmental factors are distances from destination [24], congestion degree [25], speed difference [26], and exit's width [27,28]. Apart from environmental factors, personal factors can also affect exit choice.…”

Review of Pedestrian Trajectory Prediction Methods: Comparing Deep Learning and Knowledge-based Approaches

“…Such a mechanism allows notably to explain clogging effects at bottlenecks. Recent decision-based models are based on cognitive effect [124], [125] or learning process [37].…”

“…Examples of the hybrid approach are given in applications like the discovering of novel climate patterns [248], [249], the finding of novel compounds in material science [250], [251], the designing density functionals in quantum chemistry [252], or the improving imaging technologies in bio-medical science [253], [254]. Recently, the first applications for pedestrian trajectory predictions can be found in [255], [34], [36].…”

“…Furthermore, another possibility is to use DL methods to improve the accuracy of the KB models. This can be done by estimating the parameters based on the results of an DL algorithm or to implement a neural network in the KB simulations [37], [38]. These combinations are called the hybrid approach and they benefit from the strengths of both approaches and avoid their shortcomings.…”