Optimising Pedestrian Flow Around Large Stadiums

This study proposes a method that combines the cellular automaton model and the differential evolution algorithm for optimising pedestrian flow around large stadiums. A miniature version of a large stadium and its surrounding areas is constructed via the cellular automaton model. Special mechanisms are applied to influence the behaviour of an agent that leaves from a certain stadium gate. The agent may be attracted to a nearby business facility and/or guided to uncongested areas. The differential evolution algorithm is then used to determine the optimal probabilities of the influencing agents for each stadium gate. The main goal is to reduce the evacuation time, and other goals such as reducing the costs for the influencing agents’ behaviours and the individual evacuation time are also considered. We found that, although they worked differently in different scenarios, the attraction and guidance of agents significantly reduced the evacuation time. The optimal evacuation time was achieved with moderate attraction to the business facilities and strong guidance to the detouring route. The results demonstrate that the proposed method can provide a goal-dependent, exit-specific strategy that is otherwise hard to acquire for optimising pedestrian flow.

Social Distancing with the Optimal Steps Model

With the Covid-19 pandemic, an urgent need has arisen to simulate social distancing. The Optimal Steps Model (OSM) is a pedestrian locomotion model that operationalizes an individual's need for personal space. We present new parameter values for personal space in the OSM to simulate social distancing in the pedestrian dynamics simulator Vadere. Our approach is pragmatic. We consider two use cases: in the first, we demand that a set social distance must never be violated. In the second the social distance can be violated temporarily for less than 10s. For each use case we conduct simulation studies in a typical bottleneck scenario and measure contact times, that is, violations of the social distance rule.We conduct regression analysis to assess how the parameter choice depends on the desired social distance and the corridor width. We find that evacuation time increases linearly with the width of the repulsion potential, which is an analogy to physics modeling the strength of the need for personal space. The evacuation time decreases linearly with larger corridor width. The influence of the corridor width on the evacuation time is smaller than the influence of the range of the repulsion, that is, the need for personal space. If the repulsion is too strong, we observe clogging effects. Our regression formulas enable Vadere users to conduct their own studies without understanding the intricacies of the OSM implementation and without extensive parameter adjustment.

Understanding the Characteristics of Pedestrians when Passing Obstacles of Different Sizes: An Experimental Study

The aim of this study is to understand the collective movements of individuals and to observe how individuals interact within a physical environment in a crowd dynamic, which has drawn the attention of many researchers. We conducted an experimental study to observe interactions in the collective motions of people and to identify characteristics of pedestrians when passing obstacles of different sizes (bar-shaped, 1.2 m, 2.4 m, 3.6 m and 4.8 m), going through one narrow exit and employing three different flow rates in walking and running conditions. According to the results of our study, there were no differences in collision-avoidance behaviour of pedestrians when walking or running. The pedestrians reacted early to the obstacles and changed the direction in which they were walking by quickly turning to the left or to the right. In terms of the speed of the pedestrians, the average velocity was significantly affected while performing these tasks, decreasing as the size of the obstacle increased; therefore, the size of obstacles will affect flow and speed levels. Travel time was shorter when participants were in the medium-flow rate experiments. In terms of the distance of each individual’s travel, our data showed that there was no significant difference in all the flow rate experiments for both speed levels. Our results also show that when the pedestrians crossed an obstacle, the lateral distance averaged from 0.3 m to 0.7 m, depending on the flow rate and speed level. We then explored how the body sways behaved while avoiding obstacles. It is observed that the average sway of the body was less in the high-speed conditions compared to the low-speed conditions – except for the HF & 4.8 m experiment. These results are expected to provide an insight into the characteristics of the behaviour of pedestrians when avoiding objects, and this could help enhance agent-based models.

Glossary to Support Applied International Research on Decision Making for High Conflict Urban Marches and Parades

This article was created in context of OPMoPS (Organized Pedestrian Movement in Public Spaces), a French-German interdisciplinary collaboration on high conflict urban marches and parades. As OPMoPS aims to support decision making for authorities of public order, both a French and a German police institution are members of the consortium. Communication with target group was insofar challenging, as their experts' language is close to everyday terms. Thus the authors are proposing the following glossary to support applied international research in this field. Both authors are not skilled language experts but pragmatic members of OPMoPS's police institutions. All terms can be found in English, German and French, with a focus on police and on German police procedure. It is firstly classed in thematic order, and secondly in alphabetical order.

Minimization of Mean-CVaR Evacuation Time of a Crowd using Rescue Guides: a Scenario-based Approach

In case of a threat in a public space, the crowd in it should be moved to a shelter or evacuated without delays. Risk management and evacuation planning in public spaces should also take into account uncertainties in the traffic patterns of crowd flow. One way to account for the uncertainties is to make use of safety staff, or guides, that lead the crowd out of the building according to an evacuation plan. Nevertheless, solving the minimum time evacuation plan is a computationally demanding problem. In this paper, we model the evacuating crowd and guides as a multi-agent system with the social force model. To represent uncertainty, we construct probabilistic scenarios. The evacuation plan should work well both on average and also for the worst-performing scenarios. Thus, we formulate the problem as a bi-objective scenario optimization problem, where the mean and conditional value-at-risk (CVaR) of the evacuation time are objectives. A solution procedure combining numerical simulation and genetic algorithm is presented. We apply it to the evacuation of a fictional passenger terminal. In the mean-optimal solution, guides are assigned to lead the crowd to the nearest exits, whereas in the CVaR-optimal solution the focus is on solving the physical congestion occurring in the worst-case scenario. With one guide positioned behind each agent group near each exit, a plan that minimizes both objectives is obtained.

Experimental Study of Collective Pedestrian Dynamics

We report on two series of experiments, conducted in the frame of two different collaborations designed to study how pedestrians adapt their trajectories and velocities in groups or crowds. Strong emphasis is put on the motivations for the chosen protocols and the experimental implementation. The first series deals with pattern formation, interactions between pedestrians, and decision-making in pedestrian groups at low to medium densities. In particular, we show how pedestrians adapt their headways in single-file motion depending on the (prescribed) leader’s velocity. The second series of experiments focuses on static crowds at higher densities, a situation that can be critical in real life and in which the pedestrians’ choices of motion are strongly constrained sterically. More precisely, we study the crowd’s response to its crossing by a pedestrian or a cylindrical obstacle of 74cm in diameter. In the latter case, for a moderately dense crowd, we observe displacements that quickly decay with the minimal distance to the obstacle, over a lengthscale of the order of the meter.

Agent Based Modelling and Simulation of Pedestrian Crowds in Panic Situations

The increasing occurrence of panic stampedes during mass events has motivated studying the impact of panic on crowd dynamics. Understanding the collective behaviors of panic stampedes is essential to reducing the risk of deadly crowd disasters. In this work, we use an agent-based formulation to model the collective human behavior in such crowd dynamics. We investigate the impact of panic behavior on crowd dynamics, as a specific form of collective behavior, by introducing a contagious panic parameter. The proposed model describes the intensity and spread of panic through the crowd. The corresponding panic parameter impacts each individual to represent a different variety of behaviors that can be associated with panic situations such as escaping danger, clustering, and pushing. Simulation results show contagious panic and pushing behavior, resulting in a more realistic crowd dynamics model.

A new framework for high-resolution pedestrian data processing using rule-based algorithms and real-time alarm systems

Pedestrian flows and densities have increased in recent years within transport-related public facilities such as train stations, as well as in private buildings such as shopping centers, event halls or convention centers. Increasing flows and high densities often raise comfort, safety, operational and delay issues; and therefore, require pedestrian flow optimization, intervention or even revised regulation. Recent technological advances enhanced pedestrian sensing; however, they disregard adaptive data capture, processing, and strategic communication within reasonable time, or real-time, such as tactic occupancy or density alarms trigger rules. Content of this research is twofold. First, new data capturing and processing advances of recent technological developments are combined in an integral software and hardware-based framework. Second, applied methods highlight projects and experiences on both pedestrian research and on existing and operating pedestrian facilities. Based on the described, two-sided approach, proposed framework is able to fulfil high safety and comfort standards of facilities such as train stations, retail facilities or event halls. In this research, past semi-automatic video analysis processing of pedestrian behavioral studies is replaced with combined sensor and data processing system within proposed framework. In train stations of major operators, real-time pedestrian observation increases safety levels on station platforms. Tactic algorithms and alarm trigger schemes enable on-time surveillance, e.g. at overcrowded floor levels in shopping centers for escalator or door closure. Sensor data is used to train models for underpass pedestrian flow regarding path choice and fundamental diagram. In retail, queue length, trajectory analysis and floor occupancy are determined for economic, comfort as well as safety evaluation. Using trajectory classification, movement and dwell time is analyzed for staff and visitors separately (see Figure 1).

Decentralized Control for Self-driving Cars That can Freely Move on Two-dimensional Plane

In the current traffic rules, cars have to move along lanes and to stop at red traffic lights. However, in the future when all cars become completely driverless, these traffic rules may vanish and cars may be allowed to move freely on two-dimensional plane by avoiding others like pedestrian flow. This innovation could greatly reduce traffic jams. In this study, we propose a decentralized control scheme for future self-driving cars that can freely move on two-dimensional plane, based on the social force model widely used as the model of pedestrian flow. The performance of the proposed scheme is validated via simulation. Although this study is still conceptual and does not consider realistic details, we believe that it paves the way to developing novel traffic systems.

Vulnerable People in Microscopic Evacuation Modelling

Computational evacuation modelling as a part of approval procedures or design processes is sometimes concerned with vulnerable people requiring special attention. This vulnerability can be based on external circumstances or on individual characteristics. Microscopic methods are well suited to deal with such specific determinants by their ability to model individual movement and certain behavioural aspects. By reference to case studies the possibilities of up-to-date individual evacuation models to cover egress scenarios including vulnerable people are discussed. The selected examples demonstrate that the evacuation of vulnerable people often depends more on the modelling of individual behaviour rather than on a very detailed description of individual characteristics. Group formation and the guidance or assistance of other people will have a strong impact on the evacuation process and thus require special modelling techniques and respective calibration and validation efforts guided by empirical studies.