During epidemics, decision-making regarding intervention measures faces complex trade-offs. Interventions targeting indoor venues can mitigate disease spread, since they are associated with higher infection risk for respiratory pathogens. However, as experienced during the COVID-19 pandemic, these measures can lead to economic losses, especially in the hospitality sector. In this study, we propose a hybrid modeling and simulation framework to provide decision support for reducing the infection risk in indoor venues while maintaining viable economic activity. Our framework integrates (i) a microscopic pedestrian model for human movement, (ii) a hybrid simulation model for virus spread and transmission, and (iii) a multi-criteria decision-making approach to identify the best service options. The framework is demonstrated for the SARS-CoV-2 infection risk. The restaurant case study results illustrate that maximizing the distance between seating groups can have a limited effect on the infection risk. Service duration and service capacity are key determinants of expected economic activity, but they constitute significant trade-offs: the former has a substantial impact on the infection risk, and the latter drives the probability of infectious introductions. Our analysis demonstrates the need for multi-criteria approaches during an outbreak and consideration of the epidemiological context for operational decision-making, even at an individual venue.

This chapter explains the processes of verification, calibration, and validation in pedestrian modelling. These are essential processes in the design and use of pedestrian models that together ensure accurate simulations of pedestrian behavior. Verification confirms that the model's implementation aligns with its conceptual design, calibration adjusts model parameters to improve accuracy, and validation assesses how well the model represents real-world pedestrian movements. Verification involves a structured process of testing whether the implemented model accurately reflects the conceptual model. This is done through a series of verification test cases, which compare the simulated outcomes to what is expected from the conceptual model. Calibration and validation are interrelated but serve different purposes. Calibration is an iterative process that fine-tunes model parameters to minimize errors between simulation results and reference data. Validation, on the other hand, assesses how accurate a pedestrian model replicates pedestrian behavior and dynamics. The state-of-the-art approach involves multi-objective calibration and validation, where multiple scenarios and metrics (i.e. objectives) are used to calibrate and validate the model. The choice of objectives has a major impact on the calibration and validation results. Key is that the scenarios and metrics are chosen such that they cover and capture all the relevant behaviors and dynamics. Which behaviors and dynamics are relevant depends on the intended use of the model and the type of modelled behavior. As most pedestrian models are stochastic or use stochastic parameters it is essential that during calibration and validation replications, repeating the simulation multiple time using the same inputs, are run to deal with this. Lastly, a sensitivity analysis of the model is also important to determine which parameters the model is most sensitive to. This guides the calibration process and can ensure that the calibration is as efficient as possible. All these processes are explained in detail in this chapter. This includes descriptions of how to apply them in the context of pedestrian behavior modelling and what are important factors to consider. This chapter therefore provides guidance for both model developers in creating valid models and model users is assessing the quality of their model for the intended application.

SARS-CoV-2 transmission in indoor spaces, where most infection events occur, depends on the types and duration of human interactions, among others. Understanding how these human behaviours interface with virus characteristics to drive pathogen transmission and dictate the outcomes of non-pharmaceutical interventions is important for the informed and safe use of indoor spaces. To better understand these complex interactions, we developed the Pedestrian Dynamics—Virus Spread model (PeDViS): an individual-based model that combines pedestrian behaviour models with virus spread models that incorporate direct and indirect transmission routes. We explored the relationships between virus exposure and the duration, distance, respiratory behaviour, and environment in which interactions between infected and uninfected individuals took place and compared this to benchmark ‘at risk’ interactions (1.5 metres for 15 minutes). When considering aerosol transmission, individuals adhering to distancing measures may be at risk due to build-up of airborne virus in the environment when infected individuals spend prolonged time indoors. In our restaurant case, guests seated at tables near infected individuals were at limited risk of infection but could, particularly in poorly ventilated places, experience risks that surpass that of benchmark interactions. Combining interventions that target different transmission routes can aid in accumulating impact, for instance by combining ventilation with face masks. The impact of such combined interventions depends on the relative importance of transmission routes, which is hard to disentangle and highly context dependent.

The Covid-19 pandemic has had a large impact on the world. The virus spreads especially easily among people in indoor spaces such as restaurants. Hence, tools that can assess how different restaurant settings can impact the potential spread of an airborne virus and that can assess the effectiveness of mitigation policies are of high value. Microscopic pedestrian models provide the tools necessary to assess the detailed movements of people in a restaurant and with that the risk of virus transmission. This paper presents the application of a microscopic pedestrian model, including a novel activity choice and scheduling model, to assess virus transmission risks in restaurants. Simulation experiments identify that different factors impact virus transmission risks in a restaurant. Contacts between restaurant staff and customers are the driving factor for virus transmission in a restaurant whereby especially staff presents a big risk. Hence, mitigation policies focussing on these interactions and on preventing staff from transmitting the virus can be highly effective. The results also show that different restaurant layouts and setups lead to distinctly different transmission risks. Therefore, insights obtained from simulating one restaurant cannot be just transferred to any other restaurant. Together, these results show the added value of including pedestrian models in disease transmission risk modelling exercises to mitigate the impact of a pandemic caused by an airborne virus. However, the research also shows that, to better utilize the potential of pedestrian models for disease transmission risk modelling, future research of pedestrian activity scheduling behaviour in indoor spaces is necessary.

Most works featuring the capacity of pedestrian infrastructures have focussed on unidirectional movement base cases, studied relatively low-density situations and instructed their participants to behave ‘normal’. However, during large crowd movements at train stations and events often far higher densities are encountered, the flow situations are more complex, and pedestrians do not always behave ‘normal’. The objective of this study is to determine the impact of heterogeneity in walking speed and the flow situation on the global and local dynamics of the crowd. A large pedestrian experiment was performed by Delft University of Technology, coined CrowdLimits, to derive the answer to this question. A preliminary analysis of the participants’ movements illustrates that especially the introduction of differences in walking speed and distribution between flows influence the shape fundamental diagram.

Rotating ones body is a strategy pedestrians commonly use to avoid collisions. Even though this behaviour impacts capacity heavily, this rotation behaviour is seldomly studied. This research aims to increase insight into rotation behaviour of pedestrians in high density bidirectional and crossing flows. Based on data from the CrowdLimits experiments, the effect of density, movement base case, flow ratio and disturbances on the rotation behaviour of pedestrians are studied. The main findings are that all these four factors impact the number of rotations in a flow. Yet, further research is necessary to better identify to what extent and when these factors impact the rotation behaviour most.

Ideally, a multitude of steps has to be taken before a commercial implementation of a pedestrian model is used in practice. Calibration, the main goal of which is to increase the accuracy of the predictions by determining the set of values for the model parameters that allows for the best replication of reality, has an important role in this process. Yet, up to recently, calibration has received relatively little attention within the field of pedestrian modelling. Most studies focus only on one specific movement base case and/or use a single metric. It is questionable how generally applicable a pedestrian simulation model is that has been calibrated using a limited set of movement base cases and one metric. The objective of this research is twofold, namely, to (1) determine the effect of the choice of movement base cases, metrics, and density levels on the calibration results and (2) to develop a multiple-objective calibration approach to determine the aforementioned effects. In this paper a multiple-objective calibration scheme is presented for pedestrian simulation models, in which multiple normalized metrics (i.e., flow, spatial distribution, effort, and travel time) are combined by means of weighted sum method that accounts for the stochastic nature of the model. Based on the analysis of the calibration results, it can be concluded that (1) it is necessary to use multiple movement base cases when calibrating a model to capture all relevant behaviours, (2) the level of density influences the calibration results, and (3) the choice of metric or combinations of metrics influence the results severely.