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ORIGINAL RESEARCH article

Front. Phys., 20 August 2025

Sec. Social Physics

Volume 13 - 2025 | https://doi.org/10.3389/fphy.2025.1644470

This article is part of the Research TopicInnovative Approaches to Pedestrian Dynamics: Experiments and Mathematical ModelsView all 4 articles

Coupling microscopic and mesoscopic models for crowd dynamics with emotional contagion

  • Department of Mathematics, University of Houston, Houston, TX, United States

We are interested in modeling and simulating the dynamics of human crowds, where the spreading of an emotion (specifically fear) influences the pedestrians’ behavior. Our focus is on crowd dynamics in venues where dense aggregations might occur within a rarefied crowd (e.g., an airport terminal) and emotional states evolve in space and time as the result of a threat (e.g., a gunshot). In the parts of the venue where crowd density is low, we consider a microscopic, individual-based model inspired by Newtonian mechanics. In this model, the fear level of each pedestrian influences their walking speed and is affected by the fear levels of the people in their vicinity. The mesoscopic model is derived from the microscopic model via a mean-field limit approach. This ensures that the two types of models are based on the same principles and analogous parameters. The mesoscopic model is adopted in the parts of the venue where crowd density is higher, i.e., we use the crowd density as a regime indicator. We propose interface conditions to be imposed at the boundary between the regions of the domain where microscopic and mesoscopic models are used. We note that we do not consider dangerously high-density crowd scenarios, for which a macroscopic (continuum) model would be more appropriate. We test our microscopic-to-mesoscopic model on problems involving a crowd walking through a corridor or evacuating from a square.

1 Introduction

Crowds have the ability to act, think, and adapt their own movement strategies in response to environmental cues. This feature, possessed by living systems in general, sets them apart from inert matter, which follows deterministic laws of physics and behaves predictably under given conditions. It also makes the mathematical modeling of crowd dynamics particularly challenging. In fact, to be accurate even in situations when people alter their typical walking strategy in response to an external cue (e.g., a gunshot or a propagating fire), a model of crowd dynamics needs to go beyond the principles of classical mechanics. Specifically, it needs to incorporate mechanisms to account for heterogeneous behavior of individual entities and their interactions, together with the effect of an emotional state (e.g., fear) on both individuals and interactions. See, e.g., [17]. Such a model could be instrumental in improving crowd management in venues like airport terminals or concert arenas.

Microscopic, such as agent-based models, effectively capture individual variability since they allow each agent to adopt a unique walking strategy. See, e.g., [812]. For a comprehensive overview of agent-based models that implement emotional contagion in crowd simulations, we refer the reader to [13]. Unfortunately, microscopic models struggle with scalability. Specifically, they cannot accurately represent complex, non-local, and nonlinearly additive interactions as crowd size increases. Mesoscopic or kinetic models (see, e.g. [57, 1418]) offer an alternative that can reproduce complex interactions in crowds. Inspired by the kinetic theory of gases, these models consider individuals as active particles, instead of passive particles, i.e., particles that interact in a conservative and reversible manner as in traditional gas dynamics. The consequence is that interactions in kinetic models for crowd dynamics are irreversible, non-conservative and, in some cases, nonlocal and nonlinearly additive [6, 7, 15]. Therefore, kinetic models offer a powerful tool to overcome the limitations of agent-based models in capturing complex interactions. However, these models, which are Boltzmann-type evolution equations for the statistical distribution of pedestrian positions and velocities, become computationally expensive when the size of the computational domain is large.

This paper proposes a hybrid approach to the study of crowds under the influence of a spreading emotion aiming to combine the advantages of microscopic and kinetic models, while lessening the respective limitations. The starting point for our hybrid model is a microscopic model from [19] in which the emotional state (i.e., the fear level) of each person influences their walking speed and is affected by the emotional state of the people in their vicinity. Note that the emotional state is introduced as a variable that, in response to interactions with other people, can change in space and time and, in turn, alter the walking strategy, as it happens in real-life situations [4]. Through a mean-field limit approach, from the microscopic model we derived a (Bhatnagar–Gross–Krook-like) kinetic model. This operation in 1D is presented in [19], while here we extend it to 2D and propose how the kinetic and microscopic models can be interfaced, so that each model can be used where its use is most appropriate. Specifically, the microscopic model is adopted in the parts of the domain where the crowd density is lower than a prescribed value since in this case such model is computationally cheap and accurate. Where the density is above the prescribed value and the microscopic model loses accuracy and computational efficiency, we adopt the kinetic model. Since we limit the size of the domain where the kinetic model holds, our hybrid microscopic-to-mesoscopic approach remains computationally efficient. Figure 1 shows a picture of a real crowd with mixed density: the vertical black line separates the higher density crowd (left), which would be simulated with the kinetic model, from the lower density crowd (right), which would be simulated with the microscopic model.

Figure 1
Crowd of people gathered in various patterns on a cobblestone square, viewed from above. The left side shows dense clusters, while the right side displays people more evenly spaced, walking in organized lines.

Figure 1. Example of a mixed density crowd, with dense aggregations (left of the black line) near low-density regions (right of the black line).

In addition to the advantages discussed so far, our hybrid model stems from a multiscale vision, i.e., the dynamics at the microscopic scale define the conceptual basis toward the derivation of the models at a higher scale (mesoscopic). The reader interested in the further derivation of a macroscopic model from the kinetic model is referred to [19], where results are limited to one space dimension. At the macroscopic level, the crowd is seen as a continuum and thus macroscopic models are suited for densely packed crowds. It is shown in [19] that the kinetic description provides better resolution than the macroscopic model whose viscosity solution becomes incorrect when the characteristics at the particle level cross. Because of this, we do not consider the macroscopic model and how it interfaces to the models at the lower scales.

Following [19], for simplicity we assume that the walking direction is prescribed. This means that our model cannot reproduce the tendency of an individual in a stressful situation to follow the stream of people, also referred to as herding in the literature [20]. The reader interested in kinetic models that allow for a change in walking direction as a results of interactions with other people and the environment (i.e., walls and other kinds of obstacle) is referred to [1618, 2125]. We remark that only in [23] the emotional state is treated as a variable, while it is parameterized as a constant in space and time in [16, 18, 21, 22, 25] and as a function of space and time in [17, 24].

We assess our hybrid method through test cases in one and two dimensions. The tests in 1D represent one-directional motion through a corridor, where half of the people have a high fear level and the remaining half have no fear at all. This situation leads to a localized high crowd density as the panicked people walk faster than the non-panicked. The 2D tests also create a localized high-density region, resulting from a scenario similar to a group of panicked people trying to evacuate a square.

The rest of the paper is organized as follows. Section 2 briefly recalls the derivation of the 1D mesoscopic model from the 1D microscopic model from [19], extends the derivation to 2D and then presents the discretization of the 2D microscopic and mesoscopic models. The interface conditions that allow to use microscopic and mesoscopic models in different parts of a given domain are discussed in Section 3. Section 4 presents the numerical results and conclusions are drawn in Section 5.

2 From microscopic to mesoscopic

2.1 In one dimension

We consider a system of N agents that are described by their positions xi(t),i=1,,N in spatial domain ΩR and fear levels qi(t),i=1,,N for time t belonging to a time interval of interest (0,T]. Each agent moves with speed equal to the agent’s fear level and the fear level of an agent tends to the local average of fear levels of nearby agents. The microscopic model, also called agent-based, is described by the following system of ordinary differential equations:

dxidt=qi,dqidt=γqi*qi,(1)

for i=1,,N, where qi*, the average fear level for agent i, defined as:

qi*=j=1Nκi,jqjj=1Nκi,j,(2)

κi,j=κ(|xixj|) being an interaction kernel. One of the commonly used formulas for this kernel is

κr=Rr2+R2π,r0,(3)

see for example, [19]. Parameter R>0 in Equation 3 characterizes the radius of interactions. Parameter γ in Equation 1 describes the interaction strength. For simplicity, we assume that the interaction strengths of all agents are equal, although for one test in Section 4.1 we will make its value variable from one agent to the other.

We note that Equation 1 is simply Newton’s law, assuming that fear is the only driver for motion. In this simple model, every agent has the same mass, which then gets normalized to 1.

The kinetic formulation is derived from Equations 1, 2 via a mean-field limit approach. For this, we denote the empirical distribution density as:

fNx,q,t=1Ni=1Nδxxitδqqit,(4)

where (xi(t),qi(t)) is as in Equation 1, and δ represents the Dirac delta function. To obtain the kinetic model, let ψC01(R2) be a test function, multiply Equation 4 by ψ, integrate with respect to space and fear level over the respective domain and differentiate in time:

ddtfNx,q,tψx,qdxdq=1Ni=1Nddtδxxitδqqitψx,qdxdq.

By manipulating the above equation as explained in [19], one gets the following integral equation:

ψfNx,q,tt+fNx,q,tqx+γκmfNx,tκρfNx,tqfNx,q,tqdxdq=0,(5)

where

mfNx,t=1Nj=1Nδxxjqj=1Nj=1Nδxxjδqqjqdxdq=fNx,q,tqdq,

and

ρfNx,t=1Ni=1Nδxxi=fNx,q,tdq.

Since ψ is an arbitrary test function, Equation 5 leads to the following equation:

ftN+fNqx+γκmfNx,tκρfNx,tqfNq=0,

which can be rewritten as:

ftN+qfNx+γq*qfNq=0,

where the local average fear level q*(t,x) is given by:

q*x,t=κ|xy|fNy,q,tqdqdyκ|xy|fNy,q,tdqdy.

Under appropriate compactness assumptions, the empirical distribution fN converges weakly to a limiting distribution f(x,q,t) as N, resulting in the following kinetic equation:

ft+qfx+γq*qfq=0.

2.2 In two dimensions

The N agents are now described by their positions xi(t)=(xi(t),yi(t)) within domain ΩR2 and fear levels qi(t),i=1,,N. The time interval of interest is (0,T]. Each agent moves with speed equal to their fear level and with a prescribed direction θi. This could be, e.g., the direction to the nearest exit if we are considering an evacuation scenario. So, we obtain the following microscopic model:

dxidt=qicosθi,sinθi,dqidt=γqi*qi,(6)

for i=1,,N, where qi*, the average fear level for agent i, defined as:

qi*=j=1Nκi,jqjj=1Nκi,j,

κi,j=κ(|xixj)| being an interaction kernel. We consider again the kernel in Equation 3.

For the derivation of the kinetic model, we follow the same steps used in 1D. We denote the empirical distribution density as:

fNx,q,t=1Ni=1Nδxxitδqqit,(7)

where (xi(t),qi(t)) is as in Equation 6 and δ is the Dirac delta function. We multiply Equation 7 by a test function ψC01(R3), integrate with respect to space and fear level and differentiate in time:

ddtfNx,q,tψx,qdxdq=1Ni=1Nddtδxxitδqqitψx,qdxdq.

Since δ(xxi(t))δ(qqi(t))ψ(x,q)dxdq=ψ(xi(t),qi(t)), then

ddtfNx,q,tψx,qdxdq=1Ni=1Nddtψxit,qit=1Ni=1Nψxxi,qi,ψyxi,qidxidt+ψqxi,qidqidt.

By plugging Equation 6 into the above equation, we obtain:

ddtfNx,q,tψx,qdxdq=1Ni=1Nψxxi,qi,ψyxi,qiqicosθi,sinθi+ψqxi,qiγqi*qi=1Ni=1Nδxxiδqqiψxx,qcosθ+ψyx,qsinθqdxdq+γNi=1Nψqxi,qij=1Nκi,jqjj=1Nκi,jqi.

Using Equation 7, we get

ddtfNx,q,tψx,qdxdq=fNx,q,tψxx,qcosθ+ψyx,qsinθqdxdq+γNi=1Nψqxi,qij=1Nκi,jqjj=1Nκi,jqi.(8)

We have:

1Nj=1Nκi,jqj=1Nj=1Nκ|xixj|qj=κ|xix|1Nj=1Nδxxjqjdx=κ|xix|mfNx,tdx=κmfNxi,t,(9)

where

mfNx,y,t=1Nj=1Nδxxjqj=1Nj=1Nδxxjδqqjqdxdq=fNx,q,tqdq.

Similarly, we have:

1Nj=1Nκij=κ|xix|1Nj=1Nδxxjdx=κ|xix|ρfNx,tdx=κρfNxi,t,(10)

where

ρfNx,t=1Ni=1Nδxxi=fNx,q,tdq.

Using Equations 9, 10, the second term at the right-hand side of Equation 8 can be written as:

γNi=1Nψqxi,qiκmfNxi,tκρfNxi,tqi=γψqx,qκmfNx,tκρfNx,tq1Ni=1Nδxxiδqqidxdq=γψqx,qκmfNx,tκρfNx,tqfNx,q,tdxdq.

Thus, Equation 8 becomes

ddtfNx,q,tψx,qdxdq=fNx,q,tψxx,qcosθ+ψyx,qsinθqdxdq+γψqx,qκmfNx,tκρfNx,tqfNx,q,tdxdq

Integrating the terms on the right-hand side of this equation by parts, assuming that ψ vanishes at the boundaries, and rearranging the terms, we get:

ψfNx,q,tt+qcosθfNx,q,tx+qsinθfNx,q,ty+γκmfNx,tκρfNx,tqfNx,q,tqdxdq=0.

Since ψ is an arbitrary test function, this leads to:

ftN+qcosθfNx+qsinθfNy+γκmfNκρfNqfNq=0,

which can be rewritten as:

ftN+qcosθfNx+qsinθfNy+γq*qfNq=0,

where the local average fear level q* is given by:

q*x,t=κ|xx|fNx,q,tqdxdqκ|xx|fNx,q,tdxdq.

Under appropriate compactness assumptions, the empirical distribution fN converges weakly to a limiting distribution f(x,q,t) as N, resulting in the following kinetic equation:

ft+qcosθfx+qsinθfy+γq*qfq=0.(11)

2.3 Discretization of the 2D models

We use the explicit Euler scheme for the system of ordinary differential Equation 8. Let Δt be a time step and tn=nΔt, n=0,,TΔt. Denote by (xin,qin) the values of the position and the fear level of agent i at time tn. The values at time tn+1 are given by

xin+1=xin+qincosθin,sinθinΔt,qin+1=qin+γqi*,nqinΔt,(12)

with

qi*,n=j=1Nκ|xinxjn|qjnj=1Nκ|xinxjn|.

Next, we consider the discretization of Equation 11 in space, time, and fear level using a finite difference method. We assume that xΩ=[D,D]×[D,D], for given D. We partition Ω using mesh size Δx along the x axis and Δy along the y axis, so to obtain cells:

Ωi,j=xi12,xi+12×yj12,yj+12,xi+12=xi+Δx2,yj+12=yj+Δy2,

where the centers of the cells (xi,yj) are given by (iΔxD,jΔyD), for i=0,1,,Nx and j=0,1,,Ny, where Nx=2D/Δx, and Ny=2D/Δy. The fear level varies in interval [0,1], with q=0 indicating no stress and q=1 indicating the highest stress level. Interval [0,1] is partitioned into intervals of equal length Δq:

qj12,qj+12,qj+12=qj+Δq2,

where the centers of the cells qj are given by qj=jΔq, for j=0,1,,Nq, with Nq=1/Δq. These partitions of Ω and [0,1] induce a partition of the 3D domain Ω×[0,1] into 3D cells.

Let (0,T] be the time interval of interest and set the time step Δt to

Δt=12minΔxmaxjqj,Δymaxjqj,Δq2γmaxjqj.(13)

to satisfy the Courant–Friedrichs–Lewy (CFL) condition (see, e.g. [26]). Further, we denote tn=nΔt, n=0,1,,T/Δt, and for any given quantity g, its approximation at a specific time point tn is denoted with gn.

The finite difference method produces an approximation of the average over the cell

fj,k,ln1|Ωj,k|ΔqΩj,kql12ql+12fx,q,tndxdq.

To write the finite difference discretization of Equation 11, we introduce

qj,k*,n=lj̄k̄κj,j̄,k,k̄fj̄,k̄,lnqlΔq|Ωj̄,k̄|lj̄k̄κj,j̄,k,k̄fj̄,k̄,lnΔq|Ωj̄,k̄|,κj,j̄,k,k̄=Rxjxj̄2+ykyk̄2+R2π.(14)

Additionally, let ϕ be slope limiter function. For the numerical results in Section 4, we will consider and compare two slope limiters: the van Leer limiter

ϕθ=|θ|+θ1+|θ|,

and the minmod slope limiter

ϕθ=0   if θ0,θ   if 0θ1,1   if θ1.

See, e.g., [26] for more details.

A finite difference scheme for Equation 11 is as follows: At time step tn+1, find fj,k,ln+1 for j=1,,Nx1, k=1,,Nx1 and l=1,,Nq1, such that:

fj,k,ln+1fj,k,lnΔt+ηj+12,k,lnηj12,k,lnΔx+ζj,k+12,lnζj,k12,lnΔy+γξj,k,l+12nξj,k,l12nΔq+γCj,k,l+12nCj,k,l12nΔq=0,

where the flux in x variable is

ηj+12,k,ln=ηj+12,k,ln,++ηj+12,k,ln,,

with

ηj+12,k,ln,+=ηj,k,ln,++Δx2σj,k,ln,+,ηj+12,k,ln,=ηj+1,k,ln,Δx2σj+1,k,ln,,ηj,k,ln,+=|qlcosθj,k|+qlcosθj,k2fj,k,ln,ηj,k,ln,=qlcosθj,k|qlcosθj,k|2fj,k,ln,σj,k,ln,+=ηj+1,k,ln,+ηj,k,ln,+Δxϕηj,k,ln,+ηj1,k,ln,+ηj+1,k,ln,+ηj,k,ln,+,σj,k,ln,=ηj,k,ln,ηj1,k,ln,Δxϕηj+1,k,ln,ηj,k,ln,ηj,k,ln,ηj1,k,ln,,

and the flux in y variable is

ζj,k+12,ln=ζj,k+12,ln,++ζj,k+12,ln,,

with

ζj,k+12,ln,+=ζj,k,ln,++Δx2ωj,k,ln,+,ζj+12,k,ln,=ζj,k+1,ln,Δx2ωj,k+1,ln,,ζj,k,ln,+=|qlsinθj,k|+qlsinθj,k2fj,k,ln,ζj,k,ln,=qlsinθj,k|qlsinθj,k|2fj,k,ln,ωj,k,ln,+=ζj,k+1,ln,+ζj,k,ln,+Δyϕζj,k,ln,+ζj,k1,ln,+ζj,k+1,ln,+ζj,k,ln,+,ωj,k,ln,=ζj,k,ln,ζj,k1,ln,Δyϕζj,k+1,ln,ζj,k,ln,ζj,k,ln,ζj,k1,ln,.

The flux and flux limiter in q is

ξj,k,l+12n=|qj,k*,nql+12n|+qj,k*,nql+12n2fj,k,ln+|qj,k*,nql+12n|qj,k*,nql+12n2fj,k,l+1n,Cj,k,l+12n=12|sj,k,l+12n|1ΔtΔqsj,k,l+12nWj,k,l+12nϕWj,k,b+12nWj,k,l+12n,(15)

with

sj,k,l+12n=qj,k*,nql+12nandWj,k,l+12n=fj,k,l+1nfj,k,ln.

The subscript b in Equation 15 is l1 if sj,k,l+12>0 and l+1 if sj,k,l+12<0.

Scheme Equation 14 is second-order in the fear level variable q when the solution is smooth. If one sets ϕ(θ)=0, for all θ in Equation 14, a first-order upwind numerical scheme [26] is obtained.

Finally, we remark that by setting to 0 all terms in y one can easily write down the 1D counterparts of Equations 12, 14.

3 Hybrid microscopic-to-mesoscopic approach

The main idea of the hybrid scheme is to use different models depending on the local crowd density. In regions of the domain where the density is lower than a critical threshold ρc, we will use the agent-based formulation and in the regions where the density is larger than a certain threshold, we will use the mesoscopic or kinetic formulation. For clarity, we present the hybrid scheme in one dimension first, and then extend it to two dimensions.

3.1 In one dimension

For simplicity of description, we assume that the kinetic domain, i.e., the part of the computational domain where the kinetic model holds, is an interval Kn=[xIn,xIn+], where xIn and xIn+ belong to the spatial mesh and can change from one time step to another. In other words, the locations of the interfaces between the agent-based and the kinetic regimes change over time. Because of this, a cell that belongs to the kinetic domain at time tn might be located in the agent-based domain at the next time tn+1. This happens if xIn<xIn+1 or xIn+1+<xIn+. Additionally, the agents’ trajectories can cross the interface boundaries of the kinetic domain. Thus, our algorithm must describe how to transfer information from the kinetic formulation to the agent-based formulation, and vice versa. In general, the cells that belong to the kinetic domain do not have to be adjacent, i.e., we can have multiple microscopic-kinetic interfaces. So, in general, Kn would denote the collection of cells in the kinetic regime.

In order to conserve the total “mass” of people, i.e., total number of people in the system when both probabilistic and deterministic models are used, we introduce a generalized microscopic model in which agents can have different, and in general, non-integer masses. To describe how it works, let An be a set of integers representing the labels of agents present in the model at time step tn and let (xin,min,qin) denote the position, mass, and fear level of agent iAn. Position and fear level come from the discrete model, i.e., the 1D counterpart of Equation 12, and initially the masses of all agents are equal to 1, as mentioned in Section 2.1. Let {fi,kn} be the discrete probability density function at time tn computed with the 1D counterpart of Equation 14. We assume that fi,kn is defined at all mesh points xi regardless of whether they belong to Kn or not, with fi,kn=0 for xiKn. Then, initially the total density of the system at the mesh points xi is given by

ρin=jAnExixjn+kfi,knΔq,Ex=1πrex2r2(16)

where E(x) is an approximation of the Dirac delta function δ(x). The values of ρin are used to determine if mesh point xi belong to the kinetic domain at next time step tn+1, i.e., Kn+1. Specifically, if ρinρc, then xi will belong to Kn+1. Let xIn+1 and xIn+1+ be the position of these interfaces at time tn+1.

When agents cross an interface and pass from the microscopic domain to the kinetic domain, we have an inflow of mass into the kinetic domain. We handle this as follows: if agent i crosses the microscopic-to-kinetic interface, this agent is added to the probability distribution function fi,jn:

fi,jnfi,jn+ER0xixinER0qjqinmin,(17)

where ER0 is a piece-wise approximation of the delta function:

ER0x=12R0,|x|R0,0,|x|>R0.(18)

Note that in this case we choose a simpler approximation for the Dirac delta to have a more direct control on the support. The updated fi,jn in Equation 17 is plugged into Equation 14 to find fi,jn+1 and index i is removed from set An, i.e., An+1=An\{i}.

Next, we consider crossing the interface in the opposite direction, i.e., from the kinetic domain to the microscopic domain. Suppose xIn+1>xIn, i.e., the microscopic-to-kinetic interface moves to the right from time tn to tn+1. Let I be the set of indexes j such that xj[xIn,xIn+1]. Then, the mass of people that occupy a position in [xIn,xIn+1] at time tn is given by:

jIkfj,knΔqΔx.(19)

Since at time tn+1, interval [xIn,xIn+1] belongs to the microscopic domain (and hence a deterministic model holds there), we create a new agent i that occupies the midpoint of [xIn,xIn+1]. For conservation of mass, agent i has mass equal to the mass in [xIn,xIn+1], and fear level equal to the average in [xIn,xIn+1]. This means:

xin=12xInn+xIn+1n,min=jIkfj,knΔqΔx,qin=jIkqkfj,knΔqΔxjIkfj,knΔqΔx.

Then, index i gets added to An to form An+1=An{i}. This procedure is repeated at the other (kinetic-to-microscopic) interface and, in general, at all other interfaces.

To avoid loss or creation of mass (i.e., people), if Equation 19 is smaller than 1, a new agent is not created and the position of the interface is not changed. However, over a certain number of time steps the mass leaving the kinetic domain for the microscopic domain will amount to 1 or more. We note that in our simplified model, all pedestrians have non-negative fear levels and move with the assigned walking direction, which is aligned with the positive x-axis. Thus, in general, an out-flux of mass happens at the interface positioned at xIn+. Since the walking speed equals the fear level, the mass of agents with fear level qj per unit of time flowing out of the kinetic region through the interface at xIn+ equals

qjfxI+,jnΔqΔt(20)

Note that, since qj is speed, qjΔt is length and thus Equation 20 is indeed a mass. Over the time interval [tm,tn], the number of agents with fear level qj that leave the kinetic domain equals

k=mnqjfIk+,jkΔqΔt,

and the total number of agents leaving the kinetic domain equals

jk=mnqjfIk+,jkΔqΔt.

So, when this mass is greater than 1, we create a new agent j with parameters

xjn=xIn+n,qjn=jk=mnqj2fIk+,jkΔqΔtjk=mnqjfIk+,jkΔqΔt,mjn=jk=mnqjfIk+,jkΔqΔt.

In conclusion, at time tn+1 for all agents iAn+1 in the microscopic domain, position and fear level are given by the 1D counterpart of Equation 12, and the mass is computed as explained above. For the cells that belong to the kinetic domain Kn+1, the values of the probability distribution function fi,kn+1 are determined using the 1D counterpart of Equation 14. For both models, the average fear level qi*,n is computed by

qi*,n=jAnκ|xjnx|mjnqjn+jIkκ|xjnx|qkfj,knΔxΔqjAnκ|xjnx|mjn+jIkκ|xjnx|fj,knΔxΔq.(21)

where x is either the position of agent i, i.e., xin, or mesh point xi. Note that Equation 21 computes the average regarding of whether a neighborhood of agent i belongs entirely to the microscopic domain, the kinetic domain, or is across the two domains.

3.2 In two dimensions

For simplicity of description, we assume that the kinetic domain, i.e., the part of the computational domain where the kinetic model holds, is a rectangle

Bn=xIn,xIn+×yJn,yJn+,

where xIn,xIn+ and xJn,xJn+ belong to the spatial mesh. Again, we denote by An the set of integers representing the labels of the agents present in the microscopic domain at time step tn. The generic agent iAn is characterized by their mass min, which is initially equal to 1, position and fear level from Equation 12. We assume that the discrete probability density fi,j,kn from Equation 14 is defined at all mesh points regardless of whether they below to Bn or not, with fi,j,kn=0 if xiBn. Then, at time tn the total density of the system at the mesh points xi is approximated by

ρi,jn=lAnExixln+kfi,j,knΔq.

where E() is defined component-wise in Equation 16.

We use ρi,jn to determine the discrete positions of the interfaces between microscopic and kinetic domains at the next time step tn+1:

In+1=mini:maxjρi,jnρc,In+1+=maxi:maxjρi,jnρc,Jn+1=minj:maxiρi,jnρc,Jn+1+=maxj:maxiρi,jnρc.

Then, the kinetic domain at time tn+1 is Bn+1=[xIn+1,xIn+1+]×[yJn+1,yJn+1+]. See Figure 2.

Figure 2
A graphical representation shows two rectangular regions labeled \(B^n\) and \(B^{n+1}\). The horizontal axis is marked with \(x_{I_n^-}\), \(x_{I_{n+1}^-}\), \(x_{I_n^+}\), and \(x_{I_{n+1}^+}\). The vertical axis is marked with \(y_{J_n^-}\), \(y_{J_{n+1}^-}\), \(y_{J_n^+}\), and \(y_{J_{n+1}^+}\). The region \(B^n\) is shaded with crossed lines, while \(B^{n+1}\) is larger and unshaded.

Figure 2. Kinetic domains Bn and Bn+1 at two successive time steps. The shaded rectangles form the region in Equation 22.

When an agent i crosses an interface and pass from the microscopic domain to the kinetic domain, the agent is added to the probability distribution function fi,j,kn:

fi,j,knfi,j,kn+ER0xixiER0qjqimi,

where ER0 is defined component-wise in Equation 18. This updated fi,j,kn is plugged into Equation 14 and index i is removed from set An, so that An+1=An\{i}.

Next, we consider crossing the interface in the opposite direction, i.e., from the kinetic domain to the microscopic domain. Suppose that xIn+1>xIn, xIn+1+>xIn+ and yJn+1>yJn, yJn+1+>yJn+, as in Figure 2. Other cases are treated analogously. Then, the following region becomes part of the microscopic domain:

xIn,xIn+1×yJn,yJn+xIn+1,xIn+×yJn,yJn+1.(22)

This is the union of the shaded rectangles in Figure 2.

We explain the procedure we follow for [xIn,xIn+1]×[yJn,yJn+], with the understanding that the same procedure is repeated for [xIn+1,xIn+]×[yJn,yJn+1]. Let I1 be the set of indexes i such that xi[xIn,xIn+1] and J1 be the set of indexes j such that yi[yJn,yJn+]. The mass of people that occupy a position in [xIn,xIn+1]×[yJn,yJn+], which belongs to the microscopic domain at time tn+1, is given by:

iI1jJ1kfi,j,kn|Ωi,j|Δq.(23)

Hence, we create a new agent i at the midpoint of [xIn,xIn+1]×[yJn,yJn+], with mass equal Equation 23 for conservation and fear level equal to the average fear level in [xIn,xIn+1]×[yJn,yJn+]:

xin=12xInn+xIn+1n,yin=12yJn+yJn+,min=iI1jJ1kfi,j,kn|Ωi,j|Δq,qin=iI1jJ1kqkfi,j,kn|Ωi,j|ΔqiI1jJ1kfi,j,kn|Ωi,j|Δq.

Index i gets added to An to form An+1=An{i}. Just like in 1D, if Equation 23 is smaller than 1, a new agent is not created and the position of the interfaces are not changed. However, over a certain number of time steps, the mass leaving the kinetic domain for the microscopic domain will be equal or exceed 1, and then the interface changes.

Let us consider interface {xIn+}×[yJn,yJn+]. For simplicity, we assume that xIn+ corresponds to xi (mesh point) and that θi,j is positive for all j in [yJn,yJn+]. The total number of agents leaving the kinetic domain through this interface over time interval [tm,tn] equals

kl=mnjJ1qkcosθi,jfi,j,klΔyΔqΔt.

When this mass is greater than or equal 1, we create a new agent j with parameters

xjn=xIn+n,qjn=kl=mnjJ1qk2cosθi,jfi,j,klΔyΔqΔtkl=mnjJ1qkcosθi,jfxi,j,klΔyΔqΔt,mjn=kl=mnjJ1qkcosθi,jfi,j,klΔyΔqΔt,

and yjn is randomly chosen from the interval [yJn,yJn+]. A similar computation is performed at the interface [xIn,xIn+]×{yJn+}.

In conclusion, at time tn+1 for all agents iAn+1 in the microscopic domain, position and fear level are given by Equation 12, the mass is computed as explained above, and the average fear level qi*,n is computed by

qi*,n=lAnκ|xlnxin|mlnqln+i,ȷBnkκ|xi,yjxin,yin|qkfi,j,kn|Ωi,j|ΔqlAnκ|xlnxin|mln+i,jBnkκ|xi,yjxin,yin|fi,j,kn|Ωi,j|Δq.

For the cells that belong to the kinetic domain Bn=[xIn+1,xIn+1+]×[yJn+1,yJn+1+], the values of the probability distribution function fj,k,ln+1 are given by scheme Equation 14. At a mesh point (xi,yj) of the kinetic domain, the average fear level qi,j*,n is computed by:

qi,j*,n=lAnκ|xlnxin|mlnqln+i,jBnkκ|xi,yjxi,yj|qkfi,j,kn|Ωi,j|ΔqlAnκ|xlnxin|mln+i,jBnkκ|xi,yjxi,yj|fi,j,kn|Ωi,j|Δq.

4 Numerical results

We assess our hybrid method through test cases in one dimension, presented in Section 4.1, and two dimensions, reported in Section 4.2.

4.1 Tests in one dimension

We consider N=1000 agents that are initially uniformly distributed on the computational domain [50,50], so we have an initial uniform density of 10. We initially set the fear level of the agents in the interval [50,0] to be equal to 1 and the fear level of the agents in the interval [0,50] to be 0. This is an unlikely but challenging case where half of the people in the domain have no fear, and thus do not walk, while the remaining half behind them is panicking and hence getting in motion with maximum walking speed. We recall that everyone walks with a direction aligned with the positive x-axis. We set the strength of fear contagion γ=1 and the interaction radius R=0.1 in Equation 3. The time interval of interest is [0,4].

The agent based dynamics is obtained via the 1D counterpart of Equation 12 with Δt=103. Figure 3 shows the plots of the agent density

ρx,t=iExxit,

and the average fear level

qx,t=iqitExxitiExxit,

with E(x) defined in Equation 16 and r=0.3, over a part of the computational domain at different times. We see that a region of high density develops near the center of the domain and it propagates to the right.

Figure 3
Two line graphs show density functions (left) and average fear level (right) for multiple datasets. The left graph displays sharp peaks of different colors, while the right graph shows corresponding S-shaped curves.

Figure 3. The density of agents (left) and the average fear level (right) from the microscopic model at times t=0 (blue) t=1 (orange) t=2 (yellow), t=3 (purple), and t=4 (green) over x[5,5].

Next, we apply the hybrid microscopic-kinetic methods from Section 3.1, with critical density ρc=15. As can be seen from Figure 3, initially we have ρ<ρc everywhere. Thus, our approach starts with the agent-based model everywhere in [50,50]. Then, as time passes, ρ becomes larger than ρc over part of the domain. This is when our approach becomes indeed hybrid, with the kinetic domain moving to the right as time evolves. We consider 4 different meshes with mesh size Δx=Δy=Δq=0.025 and the time step is set according to Equation 13. We start with the first order numerical scheme, i.e., no limiter is used. Figure 4 compares the crowd density obtained by the hybrid scheme with the density given by the microscopic model at the end of the time interval of interest, i.e., at t=4. We see that for coarser meshes the results given by hybrid model and the microscopic model differ significantly. However, when the mesh is refined the densities computed by the 2 methods become closer and closer, as one would expect.

Figure 4
Four line graphs display \( \rho \) against \( x \), comparing first-order hybrid, agent-based, models. Each quadrant shows peak-shaped curves, with the first-order hybrid (blue) and agent-based (red) lines closely following each other and hovering above the flat critical density (orange) line. The graphs progress from less to more peaked from top-left to bottom-right.

Figure 4. Crowd density given by the microscopic model and the first order hybrid method with meshes Δx=Δq=0.1 (top, left), Δx=Δq=0.05 (top, right), Δx=Δq=0.025 (bottom, left), and Δx=Δq=0.0125 (bottom, right) at t=4.

Now, let us consider the same meshes and time steps as above, with the second-order scheme. Figures 5, 6 show the crowd density when the van Leer limiter and the minmod limiter are applied, respectively, and the comparison with the crowd density given by the microscopic model. Again, we see great agreement as the mesh is refined.

Figure 5
Four line graphs depict density (rho) versus position (x). The graphs compare

Figure 5. Crowd density given by the microscopic model and the hybrid method with the van Leer limiter and meshes Δx=Δq=0.1 (top, left), Δx=Δq=0.05 (top, right), Δx=Δq=0.025 (bottom, left), and Δx=Δq=0.0125 (bottom, right) at time t=4.

Figure 6
Four plots show density (ρ) versus a variable (X or Y). Each plot compares Minmod hybrid (blue), Agent-based (red), and Critical density (yellow). The top two plots have a distinct dip in the blue line on the left, while the bottom plots show peaked curves with blue and red lines closely aligned, above the yellow line indicating steady critical density.

Figure 6. Crowd density given by the microscopic model and the hybrid method with the minmod limiter and meshes Δx=Δq=0.1 (top, left), Δx=Δq=0.05 (top, right), Δx=Δq=0.025 (bottom, left), and Δx=Δq=0.0125 (bottom, right) at time t=4.

To quantify the comparisons in Figures 46 and Tables 1, 2 report the corresponding differences in L1 and L2 norms. From these tables, we see that the second-order schemes perform marginally better than the first-order scheme and are comparable to each other. We notice that this is not a convergence test, as the solution given by the microscopic model cannot be considered the exact solution for the kinetic model. It is just showing that when the crowd is rather dense and the mesh is fine enough, the microscopic model and the hybrid approach are in agreement.

Table 1
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Table 1. Difference between the solution given by the microscopic model and the solution given by the hybrid approaches in L1 norm at time t=4, with relative difference included in parenthesis.

Table 2
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Table 2. Difference between the solution given by the microscopic model and the solution given by the hybrid approaches in L2 norm at time t=4, with relative difference included in parenthesis.

Finally, we present some results that show the importance of model parameter γ. Recall that γ in Equation 1 is the interaction strength, i.e., how much an agent is willing to adjust their fear level, and hence walking speed, to equilibrate it with the local average. The results presented thus far were obtained with γ=1. Figure 7 shows the crowd density given by the hybrid method for γ=0.1,10 with the minmod limiter. All the other settings are the same used so far. By comparing Figure 7 with Figure 3 (left), we see that the crowd density is very sensitive to the value of γ, both in terms of magnitude of the peaks and overall distribution.

Figure 7
Two line graphs are displayed. The left graph shows four overlapping curves in green, purple, yellow, and orange. Both graphs have x-axis labeled

Figure 7. Crowd density given by the hybrid method for γ=0.1 (left) and γ=10 (right) with the minmod limiter at times t=0 (blue) t=1 (orange) t=2 (yellow), t=3 (purple), and t=4 (green) over x[5,5]. The results refer to mesh Δx=Δq=0.025.

While constant γ is convenient, it is not very realistic since different people react differently to a spreading emotion. Figure 8 reports the results obtained with the microscopic model and random values of γ(0,1) assigned to the agents, shown in the leftmost panel. Notice how the heterogeneity in the values of γ leads to local oscillations in the density (central panel) and average fear level (rightmost panel).

Figure 8
Scatter plot, density plot, and average fear level plot are shown in three panels. The scatter plot features random points. The density plot contains five overlapping colored bell curves. The average fear plot shows five colors, each with an S-shaped curve.

Figure 8. Scatter plot showing the random gamma values for each of 1,000 agents (left), along with corresponding agent density (center) and average fear level (right) given by the microscopic model at times t=0 (blue) t=1 (orange) t=2 (yellow), t=3 (purple), and t=4 (green).

4.2 Tests in two dimensions

We consider N=900 agents initially placed uniformly in computational domain [10,10]×[10,10], so that the initial density is equal to 9 everywhere in the domain. The initial fear level is set to 1 inside the circle of radius 3 centered at the origin of the axes, while everywhere else it is set to 0. This set up is meant to reproduce a case where a group of people inside a uniformly dense crowd gets scared (by, e.g., the onset of a fire) and panics, while all other people are not aware of the threat and thus do not move. We assume that all moving agents choose walking direction θ=π/4, possibly the direction where an exit is located. We set γ=1 and R=0.1 in Equation 3. The time interval of interest is [0,5].

The agent based dynamics is obtained by using Equation 12 with Δt=103. Figure 9 shows the plots of the agent density

ρx,t=iExxit,

and the average fear level

qx,t=iqitExxitiExxit

at t=4,4.5,5, with E(x) defined component-wise in Equation 16 and r=0.3.

Figure 9
Two rows of heatmaps illustrating changes over time. The top row (t = 4, 4.5, 5) displays density values (ρ) with colors ranging from dark blue to yellow, indicating increasing density. The bottom row shows corresponding values (q*) with a similar color gradient. The x and y axes range from -10 to 10, with labels and color bars on the right for value reference.

Figure 9. The density of agents (top row) and average fear level (bottom row) given by the microscopic model at times t=4 (left), t=4.5 (center), and t=5 (right).

Next, we apply the hybrid microscopic-kinetic methods from Section 3.2, with critical density ρc=4. Since initially ρ<ρc everywhere in the domain, our scheme starts with the microscopic model everywhere. As time passes, ρ becomes larger than ρc over parts of the domain and our approach becomes truly hybrid. We consider 3 different meshes with mesh size Δx=Δy=Δq=0.25,0.125,0.0625. The time step is set according to Equation 13. We start with the first order numerical scheme, i.e., no limiter is used. Figure 10 compares the crowd density obtained by the microscopic model (top left panel) with the density computed by the hybrid approach with the three meshes under consideration at time t=5. As in the one-dimensional case, the results from the hybrid and microscopic models differ significantly when a coarse mesh is used. However, as the mesh is refined, the densities computed by the two methods become increasingly closer.

Figure 10
Four contour plots with a blue to yellow color gradient represent different simulations. The top left is labeled “microscopic.” Top right shows “hybrid, 1st order” with mesh size 0.25. Bottom left also shows “hybrid, 1st order” with mesh size 0.125. Bottom right is “hybrid, 1st order” with mesh size 0.0625. Axes range from -10 to 10 on both axes.

Figure 10. Crowd density given by the microscopic model (top, left) and first order hybrid method with meshes Δx=Δy=Δq=0.25 (top, right), Δx=Δy=Δq=0.125 (bottom, left), and Δx=Δy=Δq=0.0625 (bottom, right) at t=5.

We repeat the comparison using a second-order schemes, with the same meshes and time step as above. Figures 11, 12 report the microscopic vs. hybrid comparison in terms of crowd density when the van Leer limiter and the minmod limiter are applied, respectively. Again, we see that the agreement improves as the computational mesh is refined.

Figure 11
Four panels comparing simulation results. Top left shows microscopic data with a blurred crescent shape in blue and purple hues. The other panels use a hybrid Van Leer limiter with different mesh sizes: top right at 0.25, bottom left at 0.125, and bottom right at 0.0625. All display variations in color intensity, indicating different data distributions from dark blue to yellow. Color bars range from 0 to 11.

Figure 11. Crowd density given by the microscopic model (top, left) and the hybrid method with the Van Leer limiter scheme for meshes Δx=Δy=Δq=0.25 (top, right), Δx=Δy=Δq=0.125 (bottom, left), and Δx=Δy=Δq=0.0625 (bottom, right) at t=5.

Figure 12
Four plots display density variations with color scales from dark blue to yellow, labeled as follows: top left is “microscopic”; top right “hybrid, minmod limiter” with mesh size 0.25; bottom left “hybrid, minmod limiter” with mesh size 0.125; bottom right “hybrid, minmod limiter” with mesh size 0.0625.

Figure 12. Crowd density given by the microscopic model (top, left) and the hybrid method with the minmod limiter scheme for meshes Δx=Δy=Δq=0.25 (top, right), Δx=Δy=Δq=0.125 (bottom, left), and Δx=Δy=Δq=0.0625 (bottom, right) at t=5.

To quantify the level of agreement shown in Figures 1012, we report in Tables 3, 4 the difference in absolute value between the solution given by the microscopic model and the solution given by the hybrid approach in the L1 and L2 norms. In this comparison, which we recall is not a convergence test, we do not observe a significant difference when using limiters compared to the first-order scheme. Like in the 1D case, we see that when the crowd is rather dense and the mesh is fine enough, the microscopic model and the hybrid approach are in agreement.

Table 3
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Table 3. Difference in absolute value between the solution given by the microscopic model and the solution given by the hybrid approaches in the L1 norm at time T=5, with relative difference included in parenthesis.

Table 4
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Table 4. Difference in absolute value between the solution given by the microscopic model and the solution given by the hybrid approaches in the L2 norm at time T=5, with relative difference included in parenthesis.

We conclude by considering the more realistic case where the value of the interaction strength varies from one agent to the other. Like for the corresponding 1D test, we assign a random value of γ in interval (0,1) to each agent. See the assigned values in Figure 13. Figure 14 reports the crowd density and average fear level at t=4,4.5,5 obtained with the microscopic model and these random values of γ. In both quantities, we observe local oscillations that were absent when the value of γ was constant in space (see Figure 9).

Figure 13
A grid of colored dots ranging from blue to yellow represents data values. The dots are arranged in a pattern with x and y axes labeled from negative ten to ten. A color bar on the right indicates values from zero to one.

Figure 13. Random values of γ for each of the 900 agents.

Figure 14
Two rows of heat maps illustrating changes over time (t = 4, t = 4.5, t = 5) for variables ρ and q*. The top row shows ρ, with darker regions indicating lower values and light green areas indicating higher values, with a scale from 1 to 11. The bottom row shows q*, with a similar color scale, ranging from 0.1 to 1. The maps capture dynamic spatial distributions as indicated by gradients and hotspots.

Figure 14. Crowd density (top row) and average fear level (bottom row) given by the microscopic model with the values of γ shown in Figure 13 at times t=4 (left), t=4.5 (center), and t=5 (right).

5 Conclusions and future perspectives

We developed and numerically analyzed a hybrid modeling approach for simulating crowd dynamics under the influence of propagating fear in walking venues with mixed crowd densities. The hybrid nature is related to the fact that our approach uses a microscopic (agent-based) model in low-density regions, while in higher-density areas it adopts a mesoscopic (kinetic) model, derived from the microscopic model via a mean-field limit approach. The microscopic model captures individual variability and local interactions, while the mesoscopic model efficiently handles collective behaviors in denser crowds. By coupling the two models with appropriate interface conditions, we take advantages of their strengths while mitigating their respective limitations. We ensured consistency between the two models through a mean-field limit and proposed interface conditions to enable seamless coupling of regions of varying density.

We performed tests in one and two dimensional spatial domains. We showed that our hybrid approach produces results in agreement with those provided by the microscopic model when the crowd is sufficiently dense and the computational mesh is fine enough. This validates the effectiveness of our hybrid strategy in accurately capturing crowd dynamics across varying density regimes. Additionally, our tests demonstrate the sensitivity of the computed crowd density and average fear level on model parameter γ, which controls the strength of emotional interactions. Thus, one would have to find a way to carefully set this parameter to realistically simulate crowd responses in fear-driven scenarios.

In order to focus on the behavioral features of crowd dynamics, we have introduced a strong simplification in this work: the walking direction is prescribed. This choice represents the main limitation of the hybrid model presented in this paper. In reality, each individual in a crowd has their own purposes and abilities, which lead to the selection of a walking strategy, i.e., a trajectory to follow in order to reach the desired target(s) and a speed to move along such trajectory. The walking strategy is influenced by the emotional state (considered in this paper) and the interactions with other people in the crowd and the environment, which includes walls, exits, and obstacles (not considered in this paper). A relatively easy way to account for such interactions is with tools of game theory, as we have done in [18, 23]. However, the resulting more realistic model presents difficulties at the numerical level related to need to contain high computational costs. One possible way to meet this need is through splitting algorithms, which break the model into a set of subproblems that are easier to solve and for which practical algorithms are readily available. The inclusion of interactions with people and the environment and the development of suitable splitting algorithms will be object of future work.

Data availability statement

The raw data supporting the conclusions of this article will be made available by the authors upon request, without undue reservation.

Author contributions

IP: Validation, Writing – original draft, Visualization, Methodology, Formal Analysis, Investigation, Software, Writing – review and editing. AQ: Conceptualization, Investigation, Methodology, Supervision, Writing – review and editing, Writing – original draft.

Funding

The author(s) declare that no financial support was received for the research and/or publication of this article.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declare that no Generative AI was used in the creation of this manuscript.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

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Keywords: crowd dynamics, fear propagation, kinetic model, microscopic model, multiscale model, complex systems

Citation: Perepelitsa I and Quaini A (2025) Coupling microscopic and mesoscopic models for crowd dynamics with emotional contagion. Front. Phys. 13:1644470. doi: 10.3389/fphy.2025.1644470

Received: 10 June 2025; Accepted: 14 July 2025;
Published: 20 August 2025.

Edited by:

Ryosuke Yano, Tokio Marine dR Co., Ltd., Japan

Reviewed by:

Nouamane Bakhdil, Cadi Ayyad University, Morocco
Nisrine Outada, Cadi Ayyad University, Morocco

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*Correspondence: Annalisa Quaini, YXF1YWluaUBjZW50cmFsLnVoLmVkdQ==

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