Pedestrian Simulation Research Articles

Capacity Analysis of a Passenger Rail Hub Using Integrated Railway and Pedestrian Simulation

As the level of passenger demand in rail transit systems increases, major railway stations in urban centres face serious capacity issues. Both analytical and simulation methods have been used to analyse complex station areas; however, prior efforts have only focused on either train or pedestrian movements with over-simplified assumptions that do not properly capture the impact of their interaction on capacity. This study applies an integrated crowd and transit simulation platform “Nexus” to simultaneously study the impact of pedestrian and train movements on the system performance of a complex railway station. Unlike other methods such as sequential simulation methods, the integrated simulation platform permits linkage between commercial-grade simulators. Instead of treating each simulator separately, this integrated method enables detailed modelling of how the train and crowd dynamic interact at station platforms. Such integration aims to explore the interactive effect on both types of movement and enable performance analysis possible only through this combination. To validate the model, a case study is performed on Toronto’s Union Station. Extensive data were collected, processed and input into railway and pedestrian models constructed using OpenTrack and MassMotion, respectively, and integrated via Nexus. Examining scenarios of increased levels of train and passenger volumes, a 9% drop in on-time performance of train operation is observed, while the level of service experienced by passengers on the platform deteriorates significantly due to crowding. Both length and variation in dwell time due to pedestrian movement are recognized as the main factors of performance deterioration, especially when the system approaches capacity limit. The simulation model produces estimates of the practical track-side capacity of the station and associated platform crowding levels, and helps identify locations where passengers experience severe overcrowding, which are not easily obtainable from mathematical models.

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.

Cognitive prediction of obstacle's movement for reinforcement learning pedestrian interacting model

Abstract Recent studies in pedestrian simulation have been able to construct a highly realistic navigation behaviour in many circumstances. However, when replicating the close interactions between pedestrians, the replicated behaviour is often unnatural and lacks human likeness. One of the possible reasons is that the current models often ignore the cognitive factors in the human thinking process. Another reason is that many models try to approach the problem by optimising certain objectives. On the other hand, in real life, humans do not always take the most optimised decisions, particularly when interacting with other people. To improve the navigation behaviour in this circumstance, we proposed a pedestrian interacting model using reinforcement learning. Additionally, a novel cognitive prediction model, inspired by the predictive system of human cognition, is also incorporated. This helps the pedestrian agent in our model to learn to interact and predict the movement in a similar practice as humans. In our experimental results, when compared to other models, the path taken by our model’s agent is not the most optimised in certain aspects like path lengths, time taken and collisions. However, our model is able to demonstrate a more natural and human-like navigation behaviour, particularly in complex interaction settings.

A comparison of daytime and nighttime pedestrian road-crossing behavior using an immersive virtual environment

Objective Reduced visibility for both drivers and pedestrians is a key factor underlying the higher risk of vehicle-pedestrian collisions in dark conditions. This study investigated the extent to which pedestrians adjust for the higher risk of road crossing at night by comparing daytime and nighttime pedestrian road crossing using an immersive virtual environment. Method Participants physically crossed a single lane of continuous traffic in an immersive pedestrian simulator. Participants were randomly assigned to either the daytime or the nighttime lighting condition. The primary measures were the size of the gap selected for crossing and the timing of crossing motions relative to the gap. Results The results showed that there were no significant differences in gap selection or movement timing in daytime vs. nighttime lighting conditions. However, there was a marginal increase in the time to spare after crossing the road when crossing in the dark, likely due to an accumulation of small differences in gap choices and movement timing. Conclusion This study suggests that pedestrians do not adjust their road crossing to account for greater risk at night. As such, this study adds to our understanding of the potential risk factors for pedestrian injuries and fatalities in nighttime conditions.

Pedestrian movement modelling for a commercial street considering COVID-19 social distancing strategies

This research attempts to understand the impacts of social distancing on dense urban pedestrian environments through pedestrian movement simulations. It develops a pedestrian microsimulation modelling framework that evaluates three scenarios for a commercial street in the Halifax Regional Municipality (HRM). The Business-as-Usual scenario mimics pre-COVID conditions with no social distancing protocols. Pandemic Scenario# 1 represents social distancing without any changes in the pedestrian infrastructure. The HRM has adopted a mobility response plan for COVID-19, this generates Pandemic Scenario# 2 depicting the widened sidewalks within the pedestrian microsimulation model. The results reveal that the social distancing strategy in the pandemic scenarios significantly improved pedestrian flow in terms of the reduction in contact violations. These violations are described as instances in which a pedestrian violates the 2 m social distancing rule. The simulation of the first pandemic scenario (no sidewalk enhancement) showed a significant reduction of 43% in the number of contact violations during the one-hour pedestrian simulation of the street. The second pandemic scenario showed a 68% decrease in violations. The conclusions derived from this research support the actions of the municipality as the simulation results indicate that an increase in sidewalk width can influence contact rates and time travelled. When comparing the two pandemic scenarios, the scenario that incorporated wider sidewalks showed a decrease in total travel time and contact rates.

Identification of measures to contain the outbreaks on passenger ships using pedestrian simulations

The SARS-CoV-2 pandemic, since the beginning of 2020, has had a strong effect on many industry sectors including maritime transport. In this context, the passenger transport industry was the most affected and it is still in a very critical situation. Starting from the “No Sail Order” issued in March 2020, cruise companies stopped their operations. Besides the international regulatory bodies issued several guidelines for the prevention and management of pandemics onboard in order to safely resume cruises. The present work addresses this topic, aiming to discuss procedures and best practices to reduce the risk of uncontrolled spreading of SARS-CoV-2 infection on large cruise vessels. Starting from the lessons learned from the representative case of Diamond Princess, here the tools developed in the framework of Industry 4.0 have been used to highlight and handle the criticalities risen on the internal layout of passenger vessels, opening new opportunities to operate existing vessels and improve the design new buildings for outbreaks prevention and control.

A Data-Driven Model for Pedestrian Behavior Classification and Trajectory Prediction

Pedestrian modeling remains a formidable challenge in transportation science due to the complicated nature of pedestrian behavior and the irregular movement patterns. To this extent, accurate and reliable positioning technologies and techniques play a significant role in the pedestrian simulation studies. The objective of this research is to predict pedestrian movement in various perspectives utilizing historical trajectory data. The study features considered in this research are pedestrian class, speed and position. The ensemble of these features provides a thorough description of pedestrian movement prediction, whilst contributes to the context of pedestrian modeling and Intelligent Transportation Systems. More specifically, pedestrian movement is grouped into different classes considering gender, walking pace and distraction by employing random forest algorithms. Then, position and speed prediction is computed employing suitable data-driven methods, in particular, the locally weighted regression (LOESS method), taking into account the individual pedestrian.s profile. An LSTM-based (Long Short-Term Memory) model is also applied for comparison. The methodology is applied on pedestrian trajectory data that were collected in a controlled experiment undertaken at the Campus of the National Technical University of Athens (NTUA), Greece. Prediction of pedestrian.s movement is achieved, yielding satisfactory results.

Three-Layers Model of Pedestrian in 2D Complex Space

Walking is the basic way of human movement. How to model pedestrian in complex space is a challenge. Various models such as social force and cellular automaton have been proposed. These models can reproduce some macroscopic characteristics of pedestrian, but are not very effective in complex space. This paper proposes a pedestrian model framework, including mental-planning-moving three layers. The mental layer depicts the mental state of pedestrians, selects the movement mode, destination, and speed. The planning layer performs route planning and determines the sequence of temporary target points. The moving layer could embed social force model to realize physical movement. The framework can introduce incomplete information and uncertainty, which makes the pedestrian’s decision-making probabilistic, and the trajectory has a certain degree of randomness. A pedestrian simulation software has been developed, and multiple simulation experiments have been performed in a complex two-dimensional space. The results show that the model can describe the pedestrian in a complex space realistically.

Rule-Based Pedestrian Simulation

Rule-Based Pedestrian Simulation

Large-Scale Evacuation Shelter Selection Method Through Iterations of Pedestrian Simulations With Dynamic Congestion Reproduction

It is necessary to optimize evacuation guidance to shelters in short evacuation time. The state-of-the-art method based on an idea of combinatorial optimization problems related to evacuees’ locations and the capacities of nearby shelters has been developed, while it cannot mitigate the effect of congestion on roads/streets after evacuation starts. In this study, to cover this problem, we develop a new method that utilizes simulations for estimating the effect of congestion on roads/streets during evacuation and reassigning shelters to evacuees based on the simulation results. By iterating this step, our method derives the congestion-aware solutions for shelter selection that can realize more smooth evacuation. To evaluate our method, we conducted multi-agent simulations assuming a disaster situation in a sightseeing spot. Specifically, we examined a hypothetical case scenario involving the evacuation of 30,000 visitors from the Gion Festival. We compared the proposed method with conventional methods, such as the nearest shelter selection method and our previous method. We found that our proposed method reduced average and total evacuation time and congestion on roads compared to the conventional methods including the nearest shelter selection method and our previous method that only employs combinatorial optimization without estimating congestion. From this result, our idea of simulation-based congestion estimation has an impact of easing congestion during evacuation and preventing overcapacity of shelters at the same time. It shows the possibilities of help in developing congestion-aware evacuation strategies in emergency situations of crowded areas like huge cities or sightseeing spots.

Learning to interpret novel eHMI: The effect of vehicle kinematics and eHMI familiarity on pedestrian’ crossing behavior

Introduction: In current urban traffic, pedestrians attempting to cross the road at un-signalized locations are thought to mostly use implicit communication, such as deceleration cues, to interpret a vehicle’s intention to yield. There is less reliance on explicit driver- or vehicle-based messages, such as hand/head movements, or flashing lights/beeping horns. With the impending deployment of Automated Vehicles (AV), especially those at SAE Level 4 and 5, where the driver is no longer in control of the vehicle, there has been a surge in interest in the value of new forms of communication for AVs, for example, via different types of external Human Machine Interfaces (eHMIs). However, there is still much to be understood about how quickly a novel eHMI affects pedestrian crossing decisions, and whether it provides any additional aid, above and beyond implicit/kinematic information from the vehicle. The aim of this between-participant study, funded by the H2020 interACT project, was to investigate how the combination of kinematic information from a vehicle (e.g., Speed and Deceleration), and eHMI designs, play a role in assisting the crossing decision of pedestrians in a cave-based pedestrian simulator. Method: Using an existing, well-recognized, message for yielding (Flashing Headlights - FH) as a benchmark, this study also investigated how quickly a novel eHMI (Slow Pulsing Light Band – SPLB) was learned. To investigate the effect of eHMI visibility on crossing decisions, the distance at which each eHMI was perceivable was also measured. Results: Results showed that, compared to SPLB, the FH led to earlier crossings during vehicle deceleration, especially at lower approaching speeds, and smaller time gaps. However, although FH was visible earlier than SPLB, this visibility does not appear to be the only reason for earlier crossings, with message familiarity thought to play a role. Participants were found to learn the meaning conveyed by FH relatively quickly, crossing around 1 second earlier in its presence (compared to the no eHMI condition), across the three blocks of trials. On the other hand, it took participants at least one block of 12 trials for the new SPLB signal to affect crossing, which only accelerated crossing initiations by around 200 ms, compared to the no eHMI condition. The role of comprehension, long-term exposure, and familiarity of novel messages in this context is therefore important, if AVs are to provide safe, trustworthy communication messages, which will enhance traffic flow and efficiency.

Velocity-repulsion model for pedestrian dynamics based on a multi-state modelling framework

In this work, we proposed a multi-state modelling framework that transforms the pedestrian simulation problem into the design of five modules, namely pedestrian state, environmental information, movement action, action function, and state transition rules. Based on the framework, a velocity-repulsion model is constructed with the feature that the faster a pedestrian walks, the greater distance he will keep from others. The model sets several states with different velocity and repulsion parameter couples for pedestrians who take action based on the current state and adjust the velocity and repulsion distance by updating the state. Simulation works are performed for validation. The simulated velocity-density and flow-width relations fit well with experimental data. Two characteristic forms of velocity-headway relations for acceleration and deceleration are revealed in the simulation and confirmed in the experimental data. Self-organisation phenomena of the bottleneck scenarios are reproduced.

VR Pedestrian Simulator Studies at Home: Comparing Google Cardboards to Simulators in the Lab and Reality

Virtual Reality is commonly applied as a tool for analyzing pedestrian behavior in a safe and controllable environment. Most such studies use high-end hardware such as Cave Automatic Virtual Environments (CAVEs), although, more recently, consumer-grade head-mounted displays have also been used to present these virtual environments. The aim of this study is first of all to evaluate the suitability of a Google Cardboard as low-cost alternative, and then to test subjects in their home environment. Testing in a remote setting would ultimately allow more diverse subject samples to be recruited, while also facilitating experiments in different regions, for example, investigations of cultural differences. A total of 60 subjects (30 female and 30 male) were provided with a Google Cardboard. Half of the sample performed the experiment in a laboratory at the university, the other half at home without an experimenter present. The participants were instructed to install a mobile application to their smartphones, which guided them through the experiment, contained all the necessary questionnaires, and presented the virtual environment in conjunction with the Cardboard. In the virtual environment, the participants stood at the edge of a straight road, on which two vehicles approached with gaps of 1–5 s and at speeds of either 30 or 50 km/h. Participants were asked to press a button to indicate whether they considered the gap large enough to be able to cross safely. Gap acceptance and the time between the first vehicle passing and the button being pressed were recorded and compared with data taken from other simulators and from a real-world setting on a test track. A Bayesian approach was used to analyze the data. Overall, the results were similar to those obtained with the other simulators. The differences between the two Cardboard test conditions were marginal, but equivalence could not be demonstrated with the evaluation method used. It is worth mentioning, however, that in the home setting with no experimenter present, significantly more data points had to be treated or excluded from the analysis.

A Review of Virtual Reality Studies on Autonomous Vehicle–Pedestrian Interaction

An increasing number of studies employ virtual reality (VR) to evaluate interactions between autonomous vehicles (AVs) and pedestrians. VR simulators are valued for their cost-effectiveness, flexibility in developing various traffic scenarios, safe conduct of user studies, and acceptable ecological validity. Reviewing the literature between 2010 and 2020, we found 31 empirical studies using VR as a testing apparatus for both implicit and explicit communication. By performing a systematic analysis, we identified current coverage of critical use cases, obtained a comprehensive account of factors influencing pedestrian behavior in simulated traffic scenarios, and assessed evaluation measures. Based on the findings, we present a set of recommendations for implementing VR pedestrian simulators and propose directions for future research.

Lane-design for mixed pedestrian flow in T-shaped passage

T-shaped passage is one of the most important facilities in urban rail transit station, and the crowded phenomena always appear during peak hours. Mixed pedestrian flow in T-shaped passage may generate some conflicts, which causes a lot of delays for passengers. For reducing conflicts and improving efficiency in T-shaped passage, we designed the walking lanes for mixed pedestrian flow with different origins (Os) and destinations (Ds), and the preference strength was proposed to perform lane-design rule. A cellular automata (CA) based pedestrian simulation model with the lane-design was built, and we simulated the mixed pedestrian flow with the effect of lane-design on the platform of MATLAB software. Simulation results indicate that pedestrian conflicts were reduced and the efficiency of passing was improved. Reasonable lane-design was useful for mixed pedestrian flow, and it could provide technical support for the planning and design of pedestrian facilities.

Crowd Evacuation Simulation Research Based on Improved Reciprocal Velocity Obstacles (RVO) Model with Path Planning and Emotion Contagion

Crowd evacuation simulation is an important research topic for designing reasonable building layout and effective evacuation routes. The reciprocal velocity obstacles (RVO) model is a pedestrian motion model which is used, but it does not work when complex and multiple obstacles are present in the scene. This paper proposes an improved RVO model with path planning and emotion contagion for crowd evacuation simulation. The model uses the vertices of the obstacles to construct pedestrian path nodes for planning pedestrian evacuation paths. To make the pedestrian evacuation paths simulation results more reasonable, the safety and congestion of the path nodes are considered, to plan the shortest evacuation path. Finally, a contagious disease model is introduced to study the impact of emotion contagion on the evacuation process. A crowd evacuation simulation system is developed, and simulations have been carried out in a variety of scenarios. Experiments show that the model can effectively simulate crowd evacuation, providing a powerful reference for building and layout design.

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models.

Flood–pedestrian simulator for modelling human response dynamics during flood-induced evacuation: Hillsborough stadium case study

Abstract. The flood–pedestrian simulator uses a parallel approach to couple a hydrodynamic model to a pedestrian model in a single agent-based modelling (ABM) framework on graphics processing units (GPU), allowing dynamic exchange and processing of multiple-agent information across the two models. The simulator is enhanced with more realistic human body characteristics and in-model behavioural rules. The new features are implemented in the pedestrian model to factor in age- and gender-related walking speeds for the pedestrians in dry zones around the floodwater and to include a maximum excitement condition. It is also adapted to use age-related moving speeds for pedestrians inside the floodwater, with either a walking condition or a running condition. The walking and running conditions are applicable without and with an existing two-way interaction condition that considers the effects of pedestrian congestion on the floodwater spreading. A new autonomous change of direction condition is proposed to make pedestrian agents autonomous in wayfinding decisions driven by their individual perceptions of the flood risk or the dominant choice made by the others. The relevance of the newly added characteristics and rules is demonstrated by applying the augmented simulator to reproduce a synthetic test case of a flood evacuation in a shopping centre, to then contrast its outcomes against the version of the simulator that does not consider age and gender in the agent characteristics. The enhanced simulator is demonstrated for a real-world case study of a mass evacuation from the Hillsborough football stadium, showing usefulness for flood emergency evacuation planning in outdoor spaces where destination choice and individual risk perception have great influence on the simulation outcomes.

How Do Child ADHD Symptoms and Oppositionality Impact Parent-Child Interactions When Crossing Virtual Roads?

This study examined how parents and children interact when crossing virtual roads together. We examined (1) whether children's inattention/hyperactivity and oppositionality and children's failure to jointly perform the task interfered with parents' efforts to scaffold children's road-crossing skill and (2) whether experience with the joint road-crossing task impacted children's subsequent performance in a solo road-crossing task. Fifty-five 8- to 10-year-old children with and without attention-deficit/hyperactivity disorder and their parents first jointly crossed a lane of traffic in an immersive pedestrian simulator. Children then completed the same road-crossing task alone. Parents completed questionnaires about children's symptoms of inattention/hyperactivity and oppositionality. Analyses of the joint road-crossing task showed that when parents and children crossed different gaps, parents suggested and opposed more gaps and were less likely to use a prospective gap communication strategy (i.e., communicating about a crossable gap prior to its arrival). Crossing different gaps was also associated with increased expressions of negative affect among parents and children and an increase in collisions among children. Children's level of parent-reported oppositionality also predicted an increase in child defiance and parental redirection of child behavior. Analyses of children's subsequent crossing performance indicated that parents' use of a prospective gap communication strategy during the joint road-crossing task predicted selection of larger gaps during the solo crossing task. Not crossing through the same gap and increased levels of child oppositionality interfered with the scaffolding process, potentially informing future parent-based intervention efforts for increasing children's road-crossing safety.

Pedestrian simulation and PLOS analysis in the subway station

Pedestrian simulation and PLOS analysis in the subway station