Simulation Of Pedestrian Movement Research Articles

Addressing equifinality in agent-based modeling: a sequential parameter space search method based on sensitivity analysis

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.

Sensor-Based Pedestrian Movement Simulation and Evacuation Strategy in Emergencies

Sensor-Based Pedestrian Movement Simulation and Evacuation Strategy in Emergencies

Simulation of pedestrian movements using a fine grid cellular automata model

Crowd simulation is used in evacuation and crowd safety inspections and in the study of the performance of crowd systems and animations. Cellular automata are extensively utilized in crowd modeling. In regular cellular automata models, each pedestrian occupies a single cell with the size of a pedestrian body. The movements of pedestrians resemble those of chess pieces on a chessboard because the space is divided into relatively large cells. Furthermore, all pedestrians feature the same body size and speed. This study proposes a fine grid cellular automata model that uses small cells and allows pedestrian bodies to occupy several cells. The model allows the use of different body sizes, shapes, and speeds for pedestrians. The model is also used to simulate the movements of pedestrians toward a specific target. A typical walkway scenario is considered to test and evaluate the proposed model. Pedestrian movements are smooth because of the fine grain discretization of movements, and simulation results match the empirical speed–density graphs with good accuracy.

Empirical characterisation of agents’ spatial behaviour in pedestrian movement simulation

Route choice behaviour is a key factor in determining pedestrian movement flows throughout the urban space. Agent-based modelling, a simulation paradigm that allows modelling individual behaviour mechanisms to observe the emergence of macro-level patterns, has not employed empirical data regarding route choice behaviour in cities or accommodated heterogeneity. The aim of this paper is to present an empirically based Agent-Based Model (ABM) that accounts for behavioural heterogeneity in pedestrian route choice strategies, to simulate the movement of pedestrians in cities. We designed a questionnaire to observe to what degree people employ salient urban elements (local and global landmarks, regions, and barriers) and road costs (road distance, cumulative angular change) and to empirically characterise the agent behaviour in our ABM. We hypothesised that a heterogeneous ABM configuration based on the construction of agent typologies from empirical data would portray a more plausible picture of pedestrian movement flows than a homogeneous configuration, based on the same data, or a random configuration. The city of Münster (DE) was used as a case study. From a sample of 301 subjects, we obtained six clusters that differed in relation to the role of global elements (distant landmarks, barriers, and regions) and meaningful local elements along the route. The random configuration directed the agents towards natural elements and the streets of the historical centre. The empirically based configurations resulted in lower pedestrian volumes along roads designed for cars (25% decrease) but higher concentrations along the city Promenade and the lake (40% increase); based on our knowledge, we deem these results more plausible. Minor differences were identified between the heterogeneous and homogeneous configurations. These findings indicate that the inclusion of heterogeneity does not make a difference in terms of global patterns. Yet, we demonstrated that simulation models of pedestrian movement in cities should be at least based on empirical data at the average sample-level to inform urban planners about areas prone to high volumes of pedestrians.

A modified heuristics-based model for simulating realistic pedestrian movement behavior

Pedestrian movement simulation models are used in various areas, such as building evacuation, transportation engineering, and safety management of large events. It also provides effective means to uncover underlying mechanisms of collective behaviors. In this work, a modified heuristics-based model is presented. In this model, the potential collisions in the moving process are explicitly considered. Meanwhile, a series of simulations is conducted in two typical scenarios to demonstrate the influence of critical parameters on model performance. It is found that when facing a wide obstacle in a corridor, the larger the visual radius, the earlier the pedestrian starts to make a detour. In addition, when a pedestrian observes a large crowd walking toward him, he chooses to make a detour and moves in the flow in a uniform direction. Furthermore, the model can reproduce the lane formation pedestrian flow phenomena in relatively high-density situations. With the increase of pedestrian visual radius and the weight of potential collision resistance, more stable pedestrian lanes and fewer moving-through-the-counterflow pedestrians can be observed. In terms of model validation, the density-speed relationship of simulation results accords well with that of the published empirical data. Our results demonstrate that the modified heuristics-based model can overcome the deficiency of the original model, and reproduce more realistic pedestrian movement behavior.

Pedestrian movement simulation for an airport considering social distancing strategy

This study develops a pedestrian microsimulation model for an international airport that implements a social force model to simulate business-as-usual as well as pandemic pedestrian movement scenarios. The study follows an extensive calibration procedure to formulate pedestrian behavior within the microsimulation environment to reflect COVID-19 restrictions. The calibration process includes three components, which are visual observations, sensitivity analysis, and parameter combinations. The calibration process identifies the combination of the parameters that yields a pedestrian behavior for maximizing the distances among pedestrians in the airport. The study tests three scenarios: (1) a base case scenario with no social distancing parameters in effect, (2) a social distancing scenario that implements calibrated social force model parameters, and (3) a social distancing scenario through a combined implementation of the calibrated parameters and an operational improvement strategy. The simulation results for calibration reveal that the identified combination of the parameters yields 3.8% of instances of pedestrian movements that demonstrate a proximity of less than 2 m between pedestrians. The social distancing scenario demonstrates a significant reduction of such instances by 93–94% in comparison to a scenario with no social distancing strategy implemented. Moreover, the results show that social distancing is likely to increase pedestrian’s activity and queue time in the airport. According to the simulation results, it appears that the airport authorities may require queue management processes, infrastructure development, and additional resource deployment to alleviate the negative impacts of social distancing policies on pedestrian movements and operations within the airport.

Agent-based simulation of pedestrian movement: a gradient method with an amplification parameter

Agent-based simulation of pedestrian movement: a gradient method with an amplification parameter

Agent-based simulation of pedestrian movement: a gradient method with an amplification parameter

Pedestrian transportation is becoming increasingly important in modern cities. In this paper, we develop an agent-based model to simulate pedestrian traffic in a city. The real residential area represented by geographic information system (GIS) data was used. This paper aims to show the general model of pedestrian movements that can be applied to both small neighbourhood spaces and whole districts. It can be also used to simulate pedestrian traffic in open as well as closed spaces. A gradient method with an additional parameter related to the distance between the agent and the target was used. What were visualised in the first step were the trajectories of agents' movement and analysed lengths of routes, as well as the time needed to reach the destination by agents. The second step involved sensitivity analysis. Morris screening results show that the impact of all parameters is significant and highly diversified. Nonlinear dependencies between model inputs were detected. The article also shows different walking strategies, e.g., leisure walk or rush to the target. Simulations present how agents move to their destination by navigating and avoiding obstacles they face.

Data-Driven Simulation of Pedestrian Movement with Artificial Neural Network

To predict pedestrian movement is of vital importance in a wide range of applications. Recently, data-driven models are receiving increasing attention in pedestrian dynamics studies, demonstrating a great potential in enhancing simulation performance. This paper presents a pedestrian movement simulation model based on the artificial neural network, in which two submodels are, respectively, used to predict velocity displacement and velocity direction angle at each time step. Destination information, the pedestrian’s historical movement information, neighboring pedestrians, and environmental obstacles within a semicircular-shaped perception area are used as inputs to learn pedestrian movement behavioral rules. In the velocity direction angle submodel, a novel division method on pedestrian’s perception area is adopted. Specifically, perception radius is divided into several bands, and perception angle range is divided into a number of sectors, establishing a weighted spatial matrix to represent varied influences of neighboring pedestrians and obstacles. Experiments on two typical scenarios, the unidirectional flow and bidirectional flow in a long straight corridor, were conducted to obtain pedestrian movement datasets. Then, a series of simulation cases were conducted to investigate the proper values for critical parameters, including perception radius, perception angle division, weights of the spatial matrix, and historical movement adoption. In comparison of pedestrian trajectory between simulation results and real data, the mean trajectory error (MTE) and mean destination error (MDE) are, respectively, 0.114 m and 0.171 m in the unidirectional flow scenario, which are, respectively, 0.204 m and 0.362 m in the bidirectional flow scenario. In addition, the fundamental diagram representing density-velocity and density-flow relationships in simulation results agree well with that in real data. The results demonstrate great capacity and credibility of the presented model in simulating pedestrian movement in real applications.

Modelling the effect of landmarks on pedestrian dynamics in urban environments

Landmarks have been identified as relevant and prominent urban elements, explicitly involved in human navigation processes. Despite the understanding accumulated around their functions, landmarks have not been included in simulation models of pedestrian movement in urban environments. In this paper, we describe an Agent-Based Model (ABM) for pedestrian movement simulation that incorporates the role of on-route and distant landmarks in agents' route choice behaviour. Route choice models with and without landmarks were compared by using four scenarios: road distance minimisation, least cumulative angular change, road distance minimisation and landmarks, least cumulative angular change and landmarks. The city centre of London was used as a case study and a set of GPS trajectories was employed to evaluate the model. The introduction of landmarks led to more heterogeneous patterns that diverge from the minimisation models. Landmark-based navigation brought about high pedestrian volumes along the river (up to 13% of agents) and the boundaries of the parks (around 8% of the agents). Moreover, the model evaluation showed that the results of the landmark-based scenarios were not significantly different from the GPS trajectories in terms of cumulative landmarkness, whereas the other scenarios were. This implies that our proposed landmark-based route choice approach was better able to reproduce human navigation. At the street-segment level, the pedestrian volumes emerging from the scenarios were comparable to the trajectories' volumes in most of the case study area; yet, under- and over-estimation were observed along the banks of the rivers and across green areas (up to +7%, −11% of volumes) in the landmark-based scenarios, and along major roads (up to +11% of volumes) in the least cumulative angular change scenario. While our model could be expanded in relation to the agents' cognitive representation of the environment, e.g. by considering other relevant urban elements and accounting for individual spatial knowledge differences, the inclusion of landmarks in route choice models results in more plausible agents that make use of relevant urban information.

Perception of urban subdivisions in pedestrian movement simulation.

The perception of urban subdivisions, deriving from regionalisation processes and the identification of separating elements (barriers), has proven to dynamically shape peoples' cognitive representations of space and route choice behaviour in cities. However, existing Agent-Based Models (ABMs) for pedestrian simulation have not accounted for these particular cognitive mapping processes. The aim of this paper is to explore the behaviour of pedestrian agents endowed with knowledge about urban subdivisions. Drawing from literature in spatial cognition, we adapted a region-based route choice model, which contemplates a high- and a local planning level, and advanced a barrier-based route choice model, wherein the influence of separating elements is manipulated. Finally, we combined these two approaches in a region-barrier based model. The patterns emerging from the movement of agents employing such approaches were examined in the city centres of London and Paris. The introduction of regions in the routing mechanisms reduced the unbalanced concentration of agents across the street network brought up by the widely employed least cumulative angular change model (-.08 Gini coefficient). The inclusion of barriers further raised the dispersal of the agents through secondary roads, while leading agents to walk along waterfronts and across parks; it also yielded a more regular usage of pedestrian roads. Moreover, the region- and the region-barrier based routes showed deviation ratio values from the road distance shortest path (region-based: 1.18 London, 1.16 Paris, region-barrier based: 1.43 London, 1.33 Paris) consistent with empirical observations from pedestrian behaviour research. A further evaluation of the model with macro-level observational data may enhance the understanding of pedestrian dynamics and help tuning the interplay amongst urban salient elements at the agent level. Yet, we consider the movement flows arising from our current implementation insightful for assessing the distribution of pedestrians and testing possible interventions for the design of legible and walkable spaces.

Network Simulation for Pedestrian Flows with HyDEFS

The reliable simulation of pedestrian movement is an essential tool for the security aware design and analysis of buildings and infrastructure. We developed HyDEFS, an event-driven dynamic flow simulation software which is designed to simulate pedestrian movement depending on varying routing decisions of the individual users and varying constraints. HyDEFS uses given density depending velocities to model congestions and evaluates flow distributions with respect to average and maximum travel time. This is of particular importance when considering evacuation scenarios. We apply HyDEFS on two small networks and cross validate its results by time-discrete and time-continuous calculations.

Modeling and Simulation of Pedestrian Movement Planning Around Corners

Owing to the complexity of behavioral dynamics and mechanisms associated with turning maneuvers, capturing pedestrian movements around corners in a mathematical model is a challenging task. In this study, minimum jerk and one-thirds power law concepts, which have been initially applied in neurosciences and brain research domains, were utilized in combination to model pedestrian movement planning around bends. Simulation outputs explained that the proposed model could realistically represent the behavioral characteristics of pedestrians walking through bends. Comparison of modeled trajectories with empirical data demonstrated that the accuracy of the model could further be improved by using appropriate parameters in the one-thirds power law equation. Sensitivity analysis explained that, although the paths were not sensitive to the boundary conditions, speed and acceleration profiles could be remarkably varied depending on boundary conditions. Further, the applicability of the proposed model to estimate trajectories of pedestrians negotiating bends under different entry, intermediate, and exit conditions was also identified. The proposed model can be applied in microscopic simulation platforms, virtual reality, and driving simulator applications to provide realistic and accurate maneuvers around corners.

Pedestrian movement simulation for stadiums design

It is shown in the paper how pedestrian movement simulation could be applied to check stadium constructions from people comfort and safety point of view. There are number demands which stadiums should be met. They arise from level of event, type of event, safety and comfort criterions and features of people flow as a physic-and-social process, and the price is one of the main issues. Some requirements may compete with each other. The only way to verify decisions on people safety and comfort is to simulate pedestrian flow under different scenarios from normal to emergent.

A data-driven neural network approach to simulate pedestrian movement

As artificial intelligence becomes a research hotspot, more and more researchers are trying to apply it to numerous domains. It is therefore interesting and challenging to apply data-driven neural network technology to pedestrian movement modeling to test its effect against traditional social force model which can be applied to manifold pedestrian scenarios. Recent neural network based pedestrian movement simulation studies often train the network with only one scenario and then test within this scenario with various parameters. To make a more adaptive neural network, we propose a four layer network to learn multiple scenario data by normalization of relative positions among pedestrians, transferring velocity vector to scalar and incorporating more path planning information, and thus to make it more adaptive to various scenarios. Simulation results show that the proposed neural network approach can be applied to several typical pedestrian scenarios including counterflow and evacuation. Moreover, it shows more realistic speed-density curve and generates less trajectory fluctuations compared with social force model. Therefore, the proposed method is capable of generating more realistic pedestrian flow in multiple scenarios.

Modeling Planned and Unplanned Store Visits within a Framework for Pedestrian Movement Simulation

The main contribution of this paper is to introduce the implication of planned and unplanned store visits within a simulation framework of pedestrian movement simulation. In order to implement the activity agenda, pedestrian agents need to successively visit a set of stores, and move over the network. The focus is on the number of planned and unplanned store visits because that determines the activity agenda. The findings from the collected data of the number of planned and unplanned store visits indicate that this number depends of the motivation and some socio-economic characteristics of the visitor in the city center.

A Two-Dimensional Optimal Velocity Model for Unidirectional Pedestrian Flow Based on Pedestrian's Visual Hindrance Field

Modeling and simulation of pedestrian movement is a feasible and effective way to evaluate evacuation facilities and risk. Inspired by the visual field and movement characteristic of pedestrians, we developed a 2-D continuous model that integrates a self-slowing, local direction-changing mechanism, and visual hindrance information. The model allows for the movement in continuous space and time, only controlled by simple kinematic equations and visual hindrance distribution. In order to get the parameters of the kinematic equations, we conducted controlled experiments, collected empirical data, and obtained velocity-changing and direction-changing relations. We then validate the model by simulating three experimental scenarios, i.e., passage, bottleneck, and classroom evacuation. It is found that some typical phenomena such as the stop-and-go waves in the passage and lane formation in the bottleneck can be reproduced. The obtained fundamental diagram and specific flow agree with classic conclusions and experimental measures very well. It is hoped that the idea of this study may be helpful in promoting the modeling and simulation study of pedestrian flow.

Optimal placement of multiple visual sensors considering space coverage and cost constraints

This paper proposes an optimal camera placement method that analyzes static spatial information in various aspects and calculates priorities of spaces using modeling the moving people pattern and simulation of pedestrian movement. To derive characteristics of space and to cover the space efficiently, an agent-based camera placement method has been developed considering the camera performance as well as the space utility extracted from a path finding algorithm. The simulation shows that the method not only determines the optimal number of cameras, but also coordinates the position and orientation of a camera efficiently considering the installation costs. Experimental results show that our approach achieves a great performance enhancement compared to other existing methods.

Modelling crowd–bridge dynamic interaction with a discretely defined crowd

This paper presents a novel method of modelling crowd–bridge interaction using discrete element theory (DET) to model the pedestrian crowd. DET, also known as agent-based modelling, is commonly used in the simulation of pedestrian movement, particularly in cases where building evacuation is critical or potentially problematic. Pedestrians are modelled as individual elements subject to global behavioural rules. In this paper a discrete element crowd model is coupled with a dynamic bridge model in a time-stepping framework. Feedback takes place between both models at each time-step. An additional pedestrian stimulus is introduced that is a function of bridge lateral dynamic behaviour. The pedestrians' relationship with the vibrating bridge as well as the pedestrians around them is thus simulated. The lateral dynamic behaviour of the bridge is modelled as a damped single degree of freedom (SDoF) oscillator. The excitation and mass enhancement of the dynamic system is determined as the sum of individual pedestrian contributions at each time-step. Previous crowd–structure interaction modelling has utilised a continuous hydrodynamic crowd model. Limitations inherent in this modelling approach are identified and results presented that demonstrate the ability of DET to address these limitations. Simulation results demonstrate the model's ability to consider low density traffic flows and inter-subject variability. The emergence of the crowd's velocity–density relationship is also discussed.

A FRAMEWORK FOR PROCESSING AGENT-BASED PEDESTRIAN ACTIVITY SIMULATIONS IN SHOPPING ENVIRONMENTS

Agent-based modeling is a computational methodology that allows the analyst to create, analyze, and experiment with artificial worlds populated by agents. A specific research area is microscale agent-based modeling, which can be used for the simulation of pedestrian movement for low- and high-density scenarios and for the effect of changes in an environment. Such models can also be used for pedestrian dynamics in city centers to show the design effects in the shopping environment. The main contribution of this article is an agent-based model that provides an activity agenda for pedestrian agents that guides their shopping behavior in terms of destination and time spent in shopping areas. This model involves choice mechanisms including where to stop, in what order, and which route to take. The article describes a framework for processing agent-based pedestrian activity simulations within a shopping environment. The main achievement of this research is a validation of the approach leading to a working system. Preliminary findings are reported here.