Improved social force model considering conflict avoidance

The Parameter Calibration of Social Force Model for Pedestrian Flow Simulation Based on YOLOv5

With the increasing importance of subways in urban public transportation systems, pedestrian flow simulation for supporting station management and risk analysis becomes more necessary. There is a need to calibrate the simulation model parameters with real-world pedestrian flow data to achieve a simulation closer to the real situation. This study presents a calibration approach based on YOLOv5 for calibrating the simulation model parameters in the social force model inserted in Anylogic. This study compared the simulation results after model calibration with real data. The results show that (1) the parameters calibrated in this paper can reproduce the characteristics of pedestrian flow in the station; (2) the calibration model not only decreases global errors but also overcomes the common phenomenon of large differences between simulation and reality.

Experimental and modelling studies of collision avoidance strategy choices and behavioural characteristics in interweaving pedestrian flow

The mechanisms of collision avoidance (CA) behaviours in interweaving pedestrian flow movements are important for pedestrian space planning and emergency management but not well understood yet. In this paper, a series of controlled interweaving pedestrian flow experiments with different densities are carried out to investigate the CA behaviours, especially CA strategy choices. Four types of CA strategies are manually identified in these experiments. Nine characteristic parameters based on the trajectory data are defined to explore the characteristics of CA behaviours. The experimental results reveal that (i) the CA behaviours change with density levels; (ii) heterogeneities can be found for individual pedestrians; (iii) the defined characteristic parameters show different statistical features for different types of CA strategies, and correlations exist between most of the parameter pairs; (iv) it usually takes 0.5-2.5 s to complete a CA process with a trajectory length of 0.5-3.5 m. A multi-nomial logit (MNL) model and a long-short-term-memory (LSTM) model are established respectively for predicting pedestrians' choices of CA strategies using the selected characteristic parameters as inputs. The modelling results prove the importance of using time-series data for pedestrian behaviour modelling, and the LSTM models show advantages over the MNL model at this point.

Effects of the direction and speed strategies on pedestrian dynamics

In this work, we propose a velocity-based model for pedestrian movements containing two sub-models to study how the direction and speed selection strategies affect pedestrian dynamics. Affected by others in the view, pedestrians deviate their moving direction from the desired one to resolve space and velocity conflicts, the sensitivity to which is adjusted by two proportional parameters. After determining the moving direction, they choose a reasonable speed to avoid immediate collisions. The tolerance of personal-space violations is considered given that people accept physical contacts in some contexts and may squeeze despite the lack of space instead of matching the speed of the one in front. Simulation results demonstrate that the direction and speed selection strategies affect pedestrian dynamics in several aspects, including the time and metabolic energy cost, density-velocity relations, and crowd stability. Intriguingly, the discrepant empirical fundamental diagrams of pedestrians can be reproduced and explained in a natural way.

An integration of enhanced social force and crowd control models for high-density crowd simulation

Social force model is one of the well-known approaches that can successfully simulate pedestrians’ movements realistically. However, it is not suitable to simulate high-density crowd movement realistically due to the model having only three basic crowd characteristics which are goal, attraction, and repulsion. Therefore, it does not satisfy the high-density crowd condition which is complex yet unique, due to its capacity, density, and various demographic backgrounds of the agents. Thus, this research proposes a model that improves the social force model by introducing four new characteristics which are gender, walking speed, intention outlook, and grouping to make simulations more realistic. Besides, the high-density crowd introduces irregular behaviours in the crowd flow, which is stopping motion within the crowd. To handle these scenarios, another model has been proposed that controls each agent with two different states: walking and stopping. Furthermore, the stopping behaviour was categorized into a slow stop and sudden stop. Both of these proposed models were integrated to form a high-density crowd simulation framework. The framework has been validated by using the comparison method and fundamental diagram method. Based on the simulation of 45,000 agents, it shows that the proposed framework has a more accurate average walking speed (0.36 m/s) compared to the conventional social force model (0.61 m/s). Both of these results are compared to the real-world data which is 0.3267 m/s. The findings of this research will contribute to the simulation activities of pedestrians in a highly dense population.