Beyond Static Estimates: Dynamic Simulation of Fire–Evacuation Interaction in Historical Districts

Abstract

Historical districts face pressing disaster preparedness challenges due to their special spatial properties—risks compounded by static approaches that overlook dynamic fire–pedestrian interactions. This study employs an agent-based model (ABM) for fire simulations and AnyLogic pedestrian dynamics to address these gaps in Dukezong Ancient Town, Yunnan Province, China, considering diverse ignition points, seasonal temperatures, and wind conditions. Dynamic simulations of 16 scenarios reveal critical spatial impacts: within 30 min, ≥28% of streets became impassable, with central ignition points causing faster obstructions. Static models underestimate evacuation durations by up to 135%, neglecting early stage congestions and detours caused by high-temperature zones. Congestions are concentrated along main east–west arterial roads, worsening with longer warning distances. A mismatch between evacuation flows and shelter capacity is found. Thus, a three-stage interaction simplification is derived: localized detours (0–10 min), congestion-driven delays on critical roads (11–30 min), and prolonged structural damage afterward. This study challenges static approaches by highlighting the “fast alert-fast congestion” paradox, where rapid alerts overwhelm narrow pathways. Solutions prioritize multi-route guidance systems, optimized shelter access points, and real-time information dissemination to reduce bottlenecks without costly infrastructure changes. This study advances disaster modeling by bridging disaster development with dynamic evacuation, offering a replicable framework for similar environments.

1. Introduction

Within the framework of ongoing global climate change, significant scholarly attention has increasingly focused on disaster mitigation and emergency evacuation within built environments, particularly in historical districts. These areas present dual challenges: their unchangeable physical state as heritage sites and their heightened susceptibility to disasters [1,2], which are exacerbated by dense tourist populations [3]. The inherent resistance of historical structures to modernization further complicates disaster preparedness and evacuation planning [4,5]. Consequently, systematic evaluations of built environment responses, along with detailed evacuation analyses under dynamic disaster conditions in historical districts, are imperative. These evaluations are critical for both heritage preservation and public safety [6,7,8].

Existing scholarship on historical districts and similar environments predominantly investigates four principal research dimensions: disaster impact assessments, encompassing scenarios such as fires [9], post-earthquake conflagrations [10,11], floods [12], intense precipitation [13], and landslides [14]; evacuation behavior studies that explore the physical and psychological factors [15] influencing human responses in densely populated traditional settings [16,17,18]; advancements in disaster and evacuation simulations, including agent-based modeling (ABM) for environmental impacts and social force models (SFMs) for pedestrian behavior analysis [19]; and the development of integrated evacuation planning tools as multi-objective strategies [20], smart planning [21], and algorithms such as 2SFCA [22], 3SFCA [23], and GA2SFCA [24].

Nevertheless, the current literature exhibits several shortcomings. Foremost among these is the lack of sufficient observation and reflection on how distinctive spatial characteristics inherent to historical districts, such as cul-de-sacs, confined pathways, and combustible building materials, may affect evacuation behaviors [25]. Additionally, most studies adopt static analytical frameworks, overlooking the nonlinear and temporal dynamics inherent to evacuations in evolving disaster scenarios [26,27], neglecting critical variables such as congestion dynamics, detours, psychological influences, and real-time information exchange [28]. Furthermore, analyses frequently omit complete evacuation processes extending from historical districts to designated shelter facilities [29,30].

This research, therefore, aims to evaluate evacuation in fire, one of the most destructive hazards for timber structures, using dynamic holistic evacuation modeling. The outcomes are expected to yield pragmatic enhancements, including refined evacuation route planning, heightened preparedness protocols, and improved emergency communication mechanisms. These advancements will substantially elevate resilience and safety outcomes in historical districts.

2. Methods

This study aims to evaluate the evacuation toward an emergency shelter for historic districts during fires, considering fire dynamics, citizen response, and evacuation interactions.

2.1. Study Area

The research area, comprising 89.15 hectares, is situated in Dukezong Ancient Town, a provincially designated historical district in southern Diqing, Yunnan Province. Characterized by narrow alleyways, predominantly less than three meters wide, and numerous T-junction intersections, the district exemplifies spatial configurations typical of historical urban centers in Southwest China. These features inherently complicate emergency evacuation and disaster management protocols. Situated approximately 260 m north of the district boundary, Diqing Square functions as the designated emergency shelter, encompassing 3.3 hectares and providing an effective evacuation space of approximately 10,128 m² [31]. The residential catchment area between the historical district and Diqing Square covers 64.45 hectares, characterized primarily by four-story residential buildings housing an estimated population of 4538 residents (Figure 1) [32]. Given Yunnan Province’s susceptibility to seismic events—documented by 42 earthquakes with magnitudes exceeding 5.0 between 2008 and 2023—and historical vulnerabilities exemplified by the 2014 fire disaster affecting 6.6 hectares within the district, comprehensive planning for secondary fires has become critically important [33]. Although Dukezong Ancient Town currently employs fire-fighting technologies and proactive public education campaigns, persisting vulnerabilities such as dead-end roads and inadequate infrastructure still highlight ongoing gaps [34]. Thus, an index of the built environment conditions related to evacuation and disaster development according to current findings is listed in

Table 1. Indicators of built environment [35,36,37].

Built-Environment Indicators	Indicator Value
Average Building Height 3	3.34 m
Floor Area Ratio 1,2,3	0.85
Timber Building Percentage 3	96.15%
Average Plot Area 2,3	7545.67 m2
Road Density 3	2.16%
Max Street Width 4	5 m
Min Street Width 4	2 m
Median Street Width 4	3 m
Dead-end Road Proportion 3	28.07%
Max Dead-end Road Length 5	104 m
Min Dead-end Road Length 5	19 m
Average Dead-end Road Length 5	57.2 m
T-junction Proportion 3	42.11%
Four-way Intersection Proportion 1,3	29.82%
Number of Open Spaces > 100 m2 3	6

¹ The Floor Area Ratio indicator is sourced from Reference [35], and the Four-way Intersection Proportion indicator is sourced from Reference [35]. ² The Floor Area Ratio indicator is sourced from Reference [36], and the Average Plot Area indicator is sourced from Reference [36]. ³ The indicator is sourced from Reference [37]. ⁴ Derived from the Road Density indicator in Reference [37] combined with the content of this research. ⁵ Derived from the Dead-end Road Proportion indicator in Reference [37] combined with the content of this research.

.

2.2. Framework

This study includes five parts: (1) fire simulation, (2) multi-case fire comparison, (3) evacuation simulation under fire, (4) multi-scenario evacuation comparison, and (5) assessment and comparison, as outlined in Figure 2.

Fire simulations are commonly conducted using software like PyroSim, CFAST, and NetLogo (6.3.0), with the latter being well-suited for large-scale, multifactor cases [38,39,40]. The fire-spread module was implemented in NetLogo 6.3.0. Custom Logo code imports the study-area map, assigns ignition points, and simulates flame propagation, the latter being governed by the heat-transfer equations (Equations (1)–(5)). To ensure the reliability of fire simulations, an agent-based model (ABM) is established based on general fire development rules while the simulation parameters are optimized through validation against historical records for current research. Afterwards, the results under different ignition conditions are compared to identify the most severe cases.

The evacuation module was implemented in AnyLogic Professional 8.9.0. A detailed road-network model of the study area was built in AnyLogic, and the fire-induced “impassable” zones exported by NetLogo were imported as dynamic obstacles. By means of AnyLogic’s built-in event block, these obstacles are activated at scheduled time steps, reproducing the progressive closure of streets as the fire advances and thereby altering pedestrians’ route choices. Throughout the simulation, each agent’s position and time-stamp are saved to CSV files for subsequent analysis. Based on this simulation framework, different evacuation strategies are tested and their results are compared to identify scenarios with the worst evacuation outcomes [41,42]. Finally, the evacuation results from within the historical districts to the emergency shelter are analyzed and compared with outcomes from a static analysis framework. The spatial and temporal evolution of fire incidents and their associated environmental changes are characterized. The interdependencies between environmental modifications and evacuation behaviors within the historical districts are analyzed. Comparative analyses between static and dynamic modeling approaches are then conducted.

2.3. Fire Simulation and Parameters

The ABM has been widely tested in disaster development simulations, such as disaster spread in urban environments [43] and fire spread in chemical industrial parks [44], highlighting its capability. The ABM of fire development in the current study is based on knowledge of fire development while the simulation results are verified with historical fire data.

2.3.1. Fire Development Model

Fire development can be divided into four stages: ignition to flashover, flashover to full development, full development to collapse, and collapse to extinction [45]. The corresponding temperature and heat release characteristics are displayed in Figure 3a. The maximum combustion temperature, T_max, is set at 800 °C, while the peak heat release rate, Q_max, is set at 40 MW [46]. The parameter α represents the temperature rise coefficient, α′ is the HRR (Heat Release Rate), increase coefficient, β is the cooling coefficient, and β′ is the HRR decrease coefficient.

Following ignition, the fire can spread to adjacent buildings through direct flame contact, thermal radiation, and heat plumes.

An adjacent building is considered ignited when the net heat flux q_w exceeds 18 kW/m² and can be calculated as follows [46]:

$$\left\{\begin{array}{c}{q}_w=q_{w,ij}-q_{w,ji}\\ q_{w,ij}={\epsilon }_w\left[\left(1-\sum {\phi }_R\right)\sigma T_i^4+\sum {\phi }_R{q}_R\right]+h_w\\ q_{w,ji}={\epsilon }_w\sigma T_w^4+h_w{T}_w\end{array}\right.$$

(1)

where i represents the exterior; j represents the interior; q_w is the heat flux at the wall surface; q_w,ij is the heat flux from exterior to interior; and q_w,ji represents the heat flux from interior to exterior. ε_w is the emissivity of the wall, typically set to 0.9 for timber walls; φ_R represents the configuration factor of external heat sources; q_R is the heat radiation power; and h_w is the convective heat transfer coefficient of the wall, calculated using the Dittus–Boelter correlation; T_w represents the wall surface temperature; T_i is the exterior temperature, determined using the plume model; and σ is the Stefan–Boltzmann constant.

Thermal radiation q_R is calculated by relating the radiative heat flux to the diffusion of hot gases and flames through openings as follows [46]:

$$\left\{\begin{array}{c}{q}_R={\displaystyle \frac{1}{\phi }}{\displaystyle \frac{q_D{A}_D}{A_D+A_W}}={\displaystyle \frac{k}{\phi }}{q}_D\\ q_D=\sigma T^4\end{array}\right.$$

(2)

where φ is the attenuation factor (typically set to 0.8), k represents the opening ratio of the wall; T is the average indoor temperature of the burning building; A_D is the area of the window, A_w is the area of the exterior wall (Figure 3b).

The calculation of heat plumes follows the model proposed by Himoto and Tanaka and is illustrated in Figure 3c. The mathematical models from Himoto and Tanaka [46] are directly implemented in this study without modification, as these models calculate heat transfer in fire propagation, which we apply in NetLogo for real-time heat calculation of each patch to determine ignition status based on the calculated heat values. The temperature variation along the centerline of the heat plume is described by [46]

$$\Delta T_0=\left\{\begin{array}{c}900\left(z/Q_C^{2/5}<0.08\right)\\ 60{\left(z/Q_C^{2/5}\right)}^{-1}\left(0.08\le z/Q_C^{2/5}<0.2\right)\\ 24{\left(z/Q_C^{2/5}\right)}^{-5/3}\left(0.2\le z/Q_C^{2/5}\right)\end{array}\right.$$

(3)

where z is the distance from a point on the heat plume to the building, and Q_C is the HRR of the burning building. The temperature rise $\Delta T\left(r\right)$ for the surrounding environment is calculated as

$$\left\{\begin{array}{c}\Delta T\left(r\right)/\Delta T_0=\mathit{exp}\left[-\beta {\left(r/l_1\right)}^2\right]\\ \mathit{tan}\theta =0.1{\left[U_{\infty }/{\left(Q^{\prime }g/{\rho }_{\infty }{C}_p{T}_{\infty }\right)}^{1/3}\right]}^{-3/4}\\ Q^{\prime }=Q_c/\sqrt{A_{Bfloor}}\end{array}\right.$$

(4)

where r is the radial distance from the centerline of the heat plume to the heated building; l₁ is the Gaussian half-width, which can be taken as 0.13z; β is the ratio of temperature half-width to velocity half-width, typically 0.95; θ is the angle between the plume centerline and the ground under wind influence; Q_C is the HRR (Heat Release Rate); A_Bfloor is the total floor area of the burning building; U_∞ is the ambient wind speed near the fire source; ρ_∞ is the ambient air density; C_p is the specific heat capacity of air; T_∞ is the ambient temperature in Kelvin, representing the undisturbed air temperature away from the fire source.

The combined effect of multiple heat plumes on the overall temperature rise is calculated as [46]

$$\Delta T={\left[\sum _{i=1}^N{\left(\Delta T_i\right)}^{3/2}\right]}^{2/3}$$

(5)

where ΔT is the total temperature rise in the heated building, ΔT_i is the temperature rise caused by a single plume i, and N is the number of plumes.

2.3.2. Simulation Verification

The verification simulation is conducted under the same conditions as reported in the historical fire reports, including an easterly wind at 5 m/s, an ambient temperature of 0 °C, a 9 h and 40 min duration, and the same ignition point. The layout of the ancient town is modeled using historical satellite imagery from Google Earth in May 2014 (Figure 4a), and the building materials, identified primarily as timber, are based on on-site inspections.

To validate the simulation, metrics are derived from existing simulations as outlined in

Table 2. The fire simulation validation indicators.

Validation Indicators	Previous Simulations Results		Threshold
	Wenquan Village, Guizhou	Dukezong Ancient Town
MaxAD 1	44.50%	63.10%	44.50%
MinAD 1	40.40%	60.80%	40.40%
CD 2	8.80%	7.47%	7.47%
ASD 3	107.041°	19.579°	19.579°

¹ MaxAD, MinAD: Major (Minor) Axis Difference Ratio of Fitted Ellipse = (Simulated Major (Minor) Axis of Fitted Ellipse for Burn Area Contour—Actual Major (Minor) Axis of Fitted Ellipse for Burn Area Contour)/Actual Major (Minor) Axis of Fitted Ellipse for Burn Area Contour. ² CD, Centroid Distance Difference Ratio = Centroid Distance/Major Axis of Fitted Ellipse for Burn Area Contour. ³ ASD, Angle of Fire Spread Direction = The angle between the vector from the ignition point to the simulated centroid of the burn area and the vector from the ignition point to the actual centroid of the burn area.

[47], with the minimum deviations from previous simulations employed as thresholds.

2.4. Multi-Case Fire Development

Various fire developments of different condition combinations, such as two wind directions, two seasons, and four ignition points are compared. The cases with largest loss are selected for further analysis.

2.4.1. Fire Development Conditions

Ignition Point Location: Based on previous research, ignition points are typically located in areas with high combustible loads, such as restaurants [48,49]. A total of 265 Points of Interest (POIs) for restaurants and hotels in the historic district are identified using Baidu Map data. Four locations (A, B, C, D), situated in areas densely populated with restaurants and hotels, are selected as potential ignition points (Figure 1b).

Wind Direction and Speed: Meteorological data indicate a predominant southwest wind direction, with an additional easterly wind condition observed during the historical fire. The wind speed is set to 5 m/s for the simulation [50].

Ambient Temperature: Temperatures corresponding to summer (15 °C) and winter (0 °C), when fires are more likely to happen, are used, based on meteorological data [51].

Combining the four ignition points (A–D) with the two wind directions and two seasonal temperatures yields a full-factorial set of 4 × 2 × 2 = 16 cases across these three key factors. This design brackets the most common and the most critical physical conditions for Dukezong. In particular, ignition Point D lies on the sole northbound corridor to Diqing Square, thereby capturing the worst-case situation in which the designated evacuation route is ignited at its origin.

2.4.2. Selection of Worst Fire Development Cases

Based on existing research, destructive indicators in fire, such as currently burning area, burning perimeter, total burned area, and their proportions, are employed to assess severity [52].

2.5. Evacuation Simulation

AnyLogic [53]—developed by The AnyLogic Company (formerly XJ Technologies) —has been widely tested in various emergency evacuations, such as in neighborhoods [16], schools [54], urban parks [55], and heritage sites [17] and is employed in current evacuation analysis.

Under the most severe fire cases, fire dynamics are input into AnyLogic. The emergency shelter is designated as the destination, with evacuees automatically selecting the nearest entrance and path. Each simulation is repeated five times to calculate the mean value for result.

2.5.1. Fire Dynamics Input

To address the lack of fire spread information in evacuation analyses [8], the obstacles that occur during fire dynamics are incorporated.

Exposure to Cumulative Equivalent Minutes (CEMs) at 43 °C for 30 min can cause mild skin damage [56]. Although evacuees do not remain in such conditions, they may attempt to pass through heated areas quickly. Based on the equivalent heat formula [57], 30 min at 43 °C is equivalent to 14 s at 50 °C. Thus, zones with temperatures exceeding 50 °C and widths requiring more than 14 s to cross are considered impassable. These zones are derived from fire simulation data and updated in the evacuation model at 10 min intervals.

2.5.2. Evacuation Parameters

Evacuation parameters include evacuee distribution, evacuation speed, response time, warning distance, and congestion conditions.

The resident population within Dukezong is 525, but this increases to approximately 9800 during peak tourist periods. Residents are distributed across non-hotel and non-restaurant buildings, while tourists are placed in hotels, attractions, and restaurants based on Points of Interest (POI) data. Outside the district, 4538 evacuees are in the emergency shelter catchment (Figure 1c). Population distribution is proportional to building area, with a minimum of one person per building.

All evacuations are assumed to occur on foot due to traffic restrictions in Dukezong, with an average speed of 1.24 m/s [58]. Speed reduction due to crowding is automatically set in AnyLogic.

Warning distance refers to the distance between the point at which fire danger is detected and the buildings when individuals decide to evacuate. If the distance is too short, evacuees inside a dead-end alley may not be able to safely evacuate if a fire occurs at one end. In Dukezong, a minimum distance of 75 m is established. Additional warning distances of 150 m, 300 m, and 600 m are also examined.

Response time, defined as the delay after the decision to evacuate, is set at 3 min, based on the fire resistance of buildings [59].

Congestion is defined as crowd density exceeding 4 people per square meter for a duration exceeding 3 min [60].

2.5.3. Evacuation Shelter Parameters

The capacity of the shelter is assessed by regulatory standards [61], with an upper evacuation limit of 5000. Evacuees over this limit are allowed to enter the shelter at half speed, showing signs of inadequate service capacity.

Considering potential smoke dispersion effects, relevant research indicates that outdoor environments within several hundred meters of fire sources may pose health risks due to harmful concentrations of PM2.5, carbon monoxide, and toxic chemicals [62,63,64]. Therefore, it is necessary to assess whether the evacuation destination is located downwind of the fire smoke dispersion pattern, as this could significantly compound health risks for evacuees.

2.5.4. Worst Evacuation Selection

In this study, the most representative criteria, evacuation duration, and evacuation distance within the historical district are selected for identifying the most adverse evacuation scenarios [65]. When evacuation distances are similar, the scenario with the longer evacuation duration is considered more adverse.

2.6. Indicators in Analysis

This study systematically examines the interactions between evacuation dynamics and changes within the built environment.

2.6.1. Built Environmental Indicators

Fire propagation patterns and intensity are assessed through quantification of burned and actively burning structural areas, expressed as percentages of the total study area. Additionally, the impacts of structural and environmental modifications resulting from fire events are measured through detailed spatial analyses of high-temperature zones. Key indicators include the proportional area covered by high-temperature zones, perimeter measurements, and centroid distances relative to ignition points, given their significant influence on evacuation route viability. Further evaluations consider road network conditions, specifically measuring the proportion of impassable segments within the overall network, blockage rates on principal evacuation routes, and the generation of dead-end streets.

2.6.2. Evacuation Indicators

Comprehensive examination is conducted on evacuation metrics, including distances and durations, considering average, minimum, and maximum values, and the distribution of evacuees across specified distance and time intervals. These empirical results are then compared to established benchmark durations for evacuations, calculated based on typical walking speeds (400 s for 500 m). Additional analyses address congestions, specifically quantifying congestion frequency, duration, and spatial proximity to areas exceeding temperatures of 50 °C. Moreover, this study evaluates detour ratios, indicating the proportion of evacuees compelled to utilize suboptimal evacuation routes, and systematically quantifies evacuee throughput at various exits.

2.6.3. Dynamic Indicators

The research contrasts static and dynamic evacuation modeling methodologies through detailed comparative evaluations of road network utilization and exit throughput. Static analysis assumes consistent route availability, while dynamic analysis incorporates route disruptions and variations in accessibility throughout the fire development process. Quantitative data on cumulative road usage and evacuee movements facilitates the identification and interpretation of methodological differences.

3. Results

In this section, we present the results of our multi-stage analysis. Section 3.1 validates the fire-spread model by comparing simulated and observed burn patterns. Section 3.2 identifies and analyses the worst-case fire and evacuation scenarios derived from sixteen simulations. Section 3.3 investigates the resulting changes to the built environment, while Section 3.4 and Section 3.5 evaluate evacuation performance inside the historical district and in the surrounding urban area, respectively.

3.1. Fire Simulation and Validation

The simulation model exhibits robust predictive capabilities regarding the shape, location, and directional progression of burned areas, meeting all established threshold criteria (

Table 3. Model verification.

Evaluation Metric	Actual	Simulated	Difference	Difference Ratio	Threshold
Fitted Ellipse Major Axis (Burn Area)	331 m	449 m	118 m	35.60%	44.50%
Fitted Ellipse Minor Axis (Burn Area)	246 m	305 m	59 m	24.00%	40.40%
Centroid Point (Coordinates)	99.71, 27.81	99.71, 27.81	22 m	2.29%	7.47%
Fire Spread Direction	SW17.882°	SW22.199°	4.317°	-	19.579°

, Figure 4a,b). Specifically, the simulated major and minor axes of the fitted ellipse are measured at 449 m and 305 m, respectively, surpassing the actual dimensions of 331 m and 246 m by 118 m and 59 m. These deviations result in proportional errors of 0.356 and 0.24, respectively, both remaining within the established threshold limits (0.445 and 0.404), indicating slight overestimations of area size. Additionally, the simulated centroid coordinates (99.7, 27.8) differ from the actual centroid position (99.7, 27.8) by only 22 m, producing a proportional discrepancy of 0.0229, significantly below the allowable threshold of 0.0747, thus underscoring the model’s precision in spatial localization. Furthermore, the simulated fire spread direction, recorded as SW22.199°, demonstrates a minor divergence of 4.317° from the actual observed direction of SW17.882°, considerably lower than the permitted threshold of 19.579°, confirming the model’s efficacy in capturing dynamic fire progression. Overall, these results substantiate the simulation model’s substantial reliability and practical applicability for accurately forecasting characteristics of burned regions.

3.2. The Worst Fire and Evacuation Scenarios

Sixteen fire cases are simulated within the historical district (

Table 4. The combination of fire development conditions and results.

Case	Ignition Point	Burned Area (m2)	Burned Area Proportion	Burning Area (m2)	Burning Area Proportion	Wind Direction	Ambient Temperature (°C)
2	A	133,410	54.01%	95,149	38.52%	Southwest	15
16	D	129,894	52.59%	91,521	37.05%	East	15
Average	-	92,188	37.32%	65,389	26.47%	-	-

). The most severe cases identified are Case 2 and Case 16, which account for the largest and second-largest burned and burning areas.

The fire development simulations for the two most critical cases, with corresponding high-temperature zones, are displayed in Figure 5a–j, respectively. The combination of a centrally located ignition point, prevailing southwest winds, and elevated ambient temperatures in Case 2 leads to rapid fire spread, resulting in a burned area approximately 44.7% greater than the average burned area across all 16 analyzed cases. Case 16, which originates from an eastern point under winter conditions with an east wind also at 5 m/s, predominantly directs fire progression westward. Consequently, this case yields a burned area 40.9% greater than the average of all evaluated cases.

Based on two critical scenarios (Case 2 and 16) and four warning distances (75 m, 150 m, 300 m, and 600 m), evacuation simulations are conducted, generating eight sets of results (

Table 5. Evacuation-performance metrics for the worst scenarios (within the historical district).

Criteria	Scenarios
	2-600 1	Average of Case 2 2	16-600 1	Average of Case 16	Static
Minimum Evacuation Distance (m)	1.01	5.08	5.29	6.97	3.59
Maximum Evacuation Distance (m)	685.16	743.62	539.19	613.63	-
Average Evacuation Distance (m)	254.36	249.04	260.2	252.34	209.4
Minimum Evacuation Duration (s)	9	7.5	7	9.25	-
Maximum Evacuation Duration (s)	2088	1752.25	1729	1822.25	-
Average Evacuation Duration (s)	586.52	426.95	638.76	525.23	447
Congestion quantity	14	7	14	6.25	-
Average Distance between zones over 50 °C to Congestions (m)	169.05	108	308.48	268	-

¹ The first number in “Scenarios” denotes the fire development case, and the second indicates the evacuation warning distance; for example, “2-600” refers to Fire Development Case 2 with a warning distance of 600 m. ² “Case 2” denotes Fire Development Case 2, while “Average of Case 2” represents the mean value derived from the four warning-distance scenarios (75 m, 150 m, 300 m, and 900 m) within Fire Development Case 2.

). Scenarios 2-600 and 16-600 are selected for detailed discussion because their average evacuation distances—254.36 m and 260.2 m, respectively—exceed the group averages (249.04 m and 252.34 m) by 2.1% to 3.1%. Their average evacuation durations—586.52 s and 638.76 s—also significantly surpass the group averages (426.95 s and 525.23 s), with increases ranging from 37.4% to 49.5%.

In comparison, Scenario 16-600 demonstrates a longer average evacuation time (638.76 s) and a longer average evacuation distance (260.2 m). Additionally, congested zones in Scenario 2-600 are located significantly closer to high-temperature areas, with an average distance of only 108 m. Both scenarios exhibit the same number of congested zones (14), indicating a similar degree of congestion.

3.3. Built Environmental Changes

This study evaluates the impacts of previously identified critical fire scenarios on environmental parameters within a historical district, as displayed in Figure 5.

3.3.1. Fire Spread Evolution

In the initial phases (approximately three hours), both scenarios exhibit near-radial fire propagation, subsequently transitioning to directional growth influenced by prevailing winds by the five-hour mark. Case 2 originates centrally within the historical district under southwest wind conditions, initially exhibiting radial dispersion before predominantly extending northeastward and eastward, resulting in an off-center expanding damage zone. Case 16, initiated at the district’s eastern boundary under persistent east winds, initially affects peripheral areas before progressing centrally, representing an “edge-to-center” fire propagation pattern (Figure 5).

3.3.2. Impact on the Built Environment

The extent of burned building area exhibits slow linear growth beyond 300 min from ignition, accelerating progressively afterwards. Case 2 demonstrates notable acceleration post-400 min, culminating in a final burned area proportion of 27.8%, substantially higher than the 12.1% observed in Case 16 (Figure 6a,b).

Conversely, the actively burning area grows differently, experiencing gradual expansion up to 350 min, accelerating between 350–430 min. The result of Case 2 increases from 30.9% to 43.3% during this, surpassing Case 16 by approximately 38%. Beyond 430 min, Case 2 plateaus in growth rate, whereas Case 16 continues to escalate, ultimately achieving 56.5%—approximately 10% higher than Case 2 (Figure 6a,b).

High-temperature zones typically encompass actively burning areas, demonstrating parallel developmental trends. Notable differences occur in Case 2 within the 400–600 min range, where actively burning area expansion slows while high-temperature zones continue to expand consistently (Figure 6a,b). Perimeter lengths of high-temperature areas similarly diverge beyond 360 min, with Case 2’s perimeter expanding more rapidly, exhibiting a more irregular geometry compared to Case 16’s elongated elliptical form (Figure 6c).

3.3.3. Impact on Evacuation Conditions

The impact of fire on evacuation conditions occurs primarily through two mechanisms: the presence of high-temperature zones affecting evacuation environments and the obstruction of escape routes.

Initially, the centroid distance between the ignition point and the high-temperature zone center gradually increases, accelerating significantly beyond 180 min. For centrally positioned fires, the centroid of high-temperature zones consistently remains closer to the ignition point (approximately 8.06 m at 150 min). At 580 min, the centroid distance for peripheral fires extends to 154.5 m, notably exceeding the central type by 74.56 m (Figure 6d). The perimeter growth of high-temperature zone of central fires is always greater. Consequently, while central fires affect more widely, peripheral fires affect more areas away from ignition.

The road sections rendered impassable due to fire account for 66.39% of the total, while the length ratio of blocked roads, specifically those of main roads that are wider than 5 m (Figure 6j), reaches 85.36% (Figure 6f). This development parallels the expansion pattern of high-temperature zones, with both scenarios displaying accelerated road blockage ratios beyond 200 min, and central-type fires consistently exhibiting more rapid development (Figure 6d). The centrally situated fires, coupled with their proximity to main roads, affects primary evacuation routes more severely.

Additionally, the number of dead-end roads, significantly influencing evacuation, steadily rises as the fire expands, accompanied by a concurrent reduction in three-way and four-way intersections, with three-way intersections declining more rapidly after 200 min. Centrally located fires exhibit the highest increase in dead-end roads within 100 m (three roads every 10 min), markedly surpassing the rate of building destruction. Conversely, peripheral fires experience peak growth rates (five roads every 10 min) between 200 and 350 m, culminating in a greater total number of dead-ends (Figure 6g). Thus, evacuation routes progressively diminish in alternative branches and increase in dead-ends over the duration of a fire, with central fires exerting a more immediate impact, while peripheral fires present a gradually intensifying accumulation of effects beyond 300 min.

The fire-induced damage in historical districts exhibits spatial segmentation and wind-driven characteristics. Centrally originated fires exert a more immediate and direct impact on the built environment, whereas peripherally initiated fires display a pronounced delayed accumulation effect.

3.4. Evacuations in Historical District

To better understand the interaction between evacuation and building environment changes in the two most severe cases, this section analyzes evacuations within the historical district (

Table 6. Detailed comparison of the worst scenarios: inside vs. outside the historical district.

Criteria		Inside Historical District			Outside Historical District
		2-600	16-600	Static	2-600	16-600	Static
Minimum Evacuation Distance (m)		1.01	5.36	3.59	296.41	299.93	290.39
Maximum Evacuation Distance (m)		685.16	556.98	551.69	702.58	891.32	687.15
Average Evacuation Distance (m)		264.36	260.2	209.4	469.51	446.16	443.96
Evacuation Distance Percentage (%)	≤100 m	18.33	12.16	12.36	0.00	0.00	0.00
	100–300 m	39.90	39.69	40.15	33.96	33.22	33.14
	300–500 m	40.86	47.50	46.82	44.36	43.54	44.05
	>500 m	0.91	0.65	0.67	21.68	23.25	22.81
Minimum Evacuation Duration (s)		9	7	4	241.94	241.94	241.94
Maximum Evacuation Duration (s)		2088	1729	628	1928	1875	715.79
Average Evacuation Duration (s)		586.52	638.7	250	692.78	641.29	359.46
Evacuation Duration Percentage (%)	≤200 s	21.40	14.51	42.30	0.00	0.00	0.00
	200–400 s	18.93	20.10	56.31	8.73	8.83	68.74
	400–600 s	15.79	17.47	1.59	19.07	20.60	30.68
	600–800 s	15.42	15.32	0.00	20.30	21.46	0.57
	>800 s	28.46	32.60	0.00	51.90	49.11	0.00
Congestion quantity		14	14	-	-	-	-
Average Distance between zones over 50 °C to Congestions (m)		169.05	308.48	-	-	-	-
Total evacuees of western exits		7629	7881	7765	-	-	-
Total evacuees of eastern exits		2171	2189	2137	-	-	-
Time of maximum capacity		-	-	-	16 m 45 s	13 m 48 s	-

and Figure 7).

In (f), the average evacuation detour distance refers to the sum of detour distances of evacuating individuals who take detours at each moment divided by the total number of evacuees at that moment, calculated over a time interval of 5 min.

In (g), (h), the western exits refer to the w1, w2, w3, w4, and e3 exits, and the eastern exits refer to the e2 and e1 exits of the historical district.

3.4.1. Evacuation Distance and Duration

In both critical evacuation scenarios, evacuation distances increase rapidly during the initial 15 min, followed by stabilization. Ultimately, the average evacuation distances converge, with a final discrepancy of 1.6%. Scenario 2-600 has a higher proportion of evacuees within the ≤100 m range (18.33% vs. 12.16%), while Scenario 16-600 shows a higher proportion within the 100–500 m range (87.19% vs. 80.76%).

Regarding evacuation duration, the 16-600 scenario exhibits a longer average evacuation duration, exceeding that of 2-600 by approximately 52.2 s overall. Scenario 16-600 has a slightly higher percentage in the 200–400 s range (20.10% vs. 18.93%), while Scenario 2-600 has a higher proportion in the ≤200 s range (21.40% vs. 14.51%). However, in the >800 s range, 16-600 also has a higher percentage (32.60% vs. 28.46%).

Analysis of evacuation time and distance over time highlights the first 15 min as the most critical period, during which both metrics increase rapidly. Due to the greater evacuation pressure on roads in the latter case, wider evacuation pathways are required to accommodate the increased demand.

3.4.2. Congestions

Congestion occurs under both of the most unfavorable scenarios, primarily within the first 25 min. Given that the development and impact range of the fire remain limited during this period, these congestion points are identified as the primary factors contributing to detours and deceleration, which consequently increase evacuation duration. Both the 2-600 and 16-600 scenarios report an equal number of congestion points, totaling 14 each.

Further analysis indicates that variations in evacuation efficiency and the number of congestion points are observed across different warning distances (Figure 7e). As the warning distance increases, congestion becomes more frequent. As indicated in Figure 8a,b, congestion points are predominantly located along the principal east–west roads, a phenomenon attributed to the elevated road network density and the presence of multiple exits within the district, rendering these roads critical evacuation routes. This suggests that the width of primary evacuation routes, among the built environment factors associated with early congestion, plays a pivotal role, with roads connecting the greatest number of branches being more susceptible to congestion than those representing the shortest paths.

Beyond their quantity, the spatial distribution of congestion points is of critical importance. In the 2-600 scenario, the average distance between congestion points and high-temperature zones (exceeding 50 °C) is 169.05 m, compared to 308.48 m in the 16-600 scenario. Congestion in the former scenario poses a greater threat to vulnerable populations and is more likely to necessitate detours, a pattern primarily linked to the eastward wind in Case 16, which causes the fire’s development center to shift rapidly away from the ignition point.

3.4.3. Detours

In both scenarios, the average evacuation distance exceeds that of the static analysis, with peak values occurring primarily within the first 25 min following fire ignition. Thus, the average detour distance in the period is analyzed (Figure 7f).

The first five minutes after ignition are found as the most rapid moments for average detour distance increasing, with values beginning to decline around the tenth minute. Although the proportion of evacuees taking detours is relatively low—resulting in a modest increase of less than 30 m in overall average evacuation distance compared to the static scenario—the variation in individual-level risk is substantial. Evacuees located near the ignition point may divert over 250 m to avoid high-temperature zones, nearly doubling their evacuation distance. For vulnerable populations, such as the elderly or mobility-impaired, this significantly elevates evacuation risk.

After the 10 min mark, average detour distances decrease rapidly, approaching zero by the 30 min point. However, the two scenarios exhibit distinct patterns. In the 16-600 scenario, where the ignition point is located near the district boundary, the affected area is limited, and detour distances diminish almost entirely within 10 min. In contrast, the 2-600 scenario features a centrally located ignition point adjacent to the W4 exit, the shortest evacuation route, thereby affecting a broader population and prolonging detour behavior considerably.

These findings highlight that the spatial relationship between the ignition point and the shortest evacuation path plays a crucial role in determining the extent and duration of detour behavior. Evacuees situated near the fire source may experience significantly increased evacuation distances, underscoring the need for location-specific evacuation planning.

3.4.4. Exit Usage Patterns

Evacuation behavior within the district influences subsequent evacuation outside the district. In both scenarios, the total number of evacuees utilizing the western passage increases rapidly, potentially reaching 3 times or more that of the eastern passage at the same moment (3.52:1 to 3.60:1). This pattern is attributed both to the higher number of exits on the western side and to the fact that the W4 exit serves as the shortest evacuation route. However, the number of evacuees using each exit is also influenced by fire ignition. In 16-600, the number of evacuees using eastern exits (2189) is slightly higher than in Scenario 2-600 (2171), possibly due to early stage accessibility before fire spread.

This phenomenon underscores the critical role of the fire ignition location, the distance between the ignition point and the shortest evacuation route, and wind direction in determining evacuation patterns within historical districts.

3.4.5. Dynamic vs. Static

The evacuation and the estimation without considering fire development are compared (Figure 8). In both cases, the average evacuation distance is 24% to 26% greater than the static result, while the average evacuation duration exceeds the estimated values by 135% to 155%. Though individuals experience evacuation distances that align with the static results, only 30% to 34% of individuals have evacuation durations consistent with the planned values. Additionally, in 2-600, the impact of fire on the shortest path to the W4 exit introduces further divergence between actual road usage and static traffic load estimations.

The comparative results suggest that, while differences in evacuation distance remain modest, the differences in evacuation duration are considerable. Two primary factors may explain this divergence: (1) Localized detours caused by fire and high-temperature zones are not captured in the static analysis, leading to a slight underestimation of average evacuation distance and subtle changes in route selection; (2) Fire-induced congestion and its cascading effects—especially during early rapid evacuations—are overlooked, resulting in a substantial underestimation of total evacuation time.

3.5. Evacuations out of Historical District

During the evacuation from the fire-affected historical district to the designated emergency shelter area, the average evacuation distances in the 2-600 and 16-600 scenarios increased by approximately 5.75% and 0.5%, respectively, compared to the static scenario—substantially lower than the increases observed within the historical district. However, the growth in evacuation duration remains significant, with average evacuation times in both scenarios exceeding the static estimates by 78% to 93%. The distribution of evacuation time shows that over half of the evacuees required more than 800 s, markedly deviating from static assumptions.

In addition, notable discrepancies are observed between the flow distribution at the emergency shelter entrances and the evacuee flow exiting the historical district (Figure 9). The southeastern entrance consistently exhibited a significantly higher pedestrian flow per meter of width compared to the western entrance. Further analysis reveals that, although the number of evacuees exiting the western side of the historical district is more than three times that of the eastern side, the western entrance of the shelter (70 m wide) is considerably wider than the southeastern entrance (7 m wide). Meanwhile, the northeastern entrance—despite its potential—is not utilized, likely due to its distance from nearby exit points.

This mismatch in spatial layout and flow direction causes greater congestion pressure at the southeastern entrance, where the maximum and average pedestrian flow per meter of width are 3 to 4 times higher than at the western entrance. This discrepancy is especially pronounced during the first 15 min, when the southeastern entrance flow rate increased rapidly, creating conditions that may heighten the risk of evacuation-related accidents (Figure 9e,f).

The differences between the two unfavorable scenarios are relatively minor. However, the 16-600 scenario exhibited a faster increase in entrance flow during the initial 15 min. At approximately 17.5 min, the flow rate at the southeastern entrance in the 16-600 scenario reached 261 persons per meter, compared to 224 persons per meter in the 2-600 scenario. This disparity can be attributed to a higher proportion of detour behavior in 2-600, which reduced the number of evacuees arriving at the shelter at the same time and thereby alleviated short-term traffic pressure.

Regarding smoke dispersion analysis, the prevailing wind directions examined in this study are southwest and east winds. Under these wind conditions, the designated evacuation destination (Diqing Square) is not positioned downwind from the historical district. While the emergency shelter falls within the potential outdoor smoke influence range of several hundred meters, it does not lie directly in the downwind path of smoke dispersion from either fire scenario.

These findings indicate that there is a mismatch between the location and capacity of shelter entrances and the spatial–temporal characteristics of evacuation flows from the historical district, posing potential risks to evacuation safety and efficiency.

4. Discussions

This study examines the dynamic effect of fire on the built environment and its developing interaction with evacuation in historical districts with ABM and SFM models. This study reveals that the location and progression of a fire significantly influence its spread, the extent of damage to the built environment, and its interaction with evacuation dynamics. While fires continuously destroy the built environment, their impact on buildings and roads is not synchronized—road obstruction occurs earlier than the expansion of burned building proportions and exhibits greater persistence. Changes in the built environment and evacuation routes affect evacuation behavior through different mechanisms at various stages of the evacuation process. Congestion and detours primarily result from the interaction between inappropriate evacuation strategies and changes in the built environment.

Moreover, all temporally dynamic analyses deviate from static analysis results, especially in the first 25 min in evacuation duration-related indicators. Additionally, a mismatch exists between the evacuation destinations outside the historical district and the evacuation patterns within the district. Ultimately, 50% to 80% of evacuees in the historical district experience longer evacuation duration, with an average increase of 150%. These findings provide critical insights for understanding fire-induced evacuations in historical districts and formulating more effective evacuation policies.

4.1. Bridging Dynamic Interactions Between Evacuation and Building Environment Changes in Disasters

A major limitation of previous research lies in its failure to dynamically capture the evolving interactions between changes in the built environment and evacuation behavior during fires in historical districts. To address this gap, this study introduces a three-stage temporal framework that reflects the progression of fire and its impacts on evacuation, while considering the role of evacuation policy.

In the first stage (0–10 min post-ignition), the spatial extent of fire impact remains limited and mostly concentrated around the ignition point. Nevertheless, it significantly affects nearby evacuees, especially by inducing detours, which disproportionately burden vulnerable groups such as the elderly and mobility-impaired individuals. At this stage, fire dynamics can largely be simplified; attention should instead focus on analyzing evacuation route changes based on estimated high-temperature zones and behavior patterns.

The second stage (11–30 min post-ignition) is characterized by a near-radial expansion of both direct (flames) and indirect (heat, smoke) fire effects, progressing slowly at an average rate of 0.37–0.47 m/s—slower than human walking speed. Thus, evacuation is mainly influenced by static environmental conditions such as the number of available exits, road width and connectivity, and the location of ignition. Of particular concern during this stage is the risk of congestion on highly connected arterial roads, especially when long pre-warning distances are applied. Simultaneously, congestion at evacuation destinations becomes increasingly salient, necessitating careful consideration of entrance widths and shelter capacity. In this phase, nonlinear fire dynamics can still be reasonably simplified; the primary analytical focus should instead be on how evacuation strategies influence internal congestion and access bottlenecks near shelter entrances.

The third stage (after 30 min) involves fewer evacuees remaining in the district. At this point, attention should shift toward assessing fire-induced structural damage to the built environment rather than active evacuation behavior. While fire dynamics can no longer be ignored, their impact on ongoing evacuation activities becomes secondary. This phased framework improves both analytical precision and computational efficiency, as simplifications can be applied to selected components of fire and behavioral modeling at each stage. Furthermore, the use of clearly defined metrics facilitates targeted intervention strategies.

Based on this conceptual model, we argue that the unique nature of built environment transformation in historical districts requires deeper investigation—particularly in the second stage—into the compatibility between evacuation strategies and physical evacuation conditions. While prior research has generally considered expanding district exit widths and increasing internal street widths within historical districts to be the most effective methods for improving evacuation efficiency, this study challenges that assumption in the context of historical districts. For instance, studies on school evacuations frequently highlight the importance of early warning and alarm systems for casualty reduction. However, our findings suggest that such strategies are only effective when the evacuation traffic system possesses adequate capacity.

In the studied historical district, evacuation distances below 75 m were found to trap evacuees in cul-de-sacs. Yet, increasing the evacuation distance often leads to a linear increase in congestion points and evacuation duration, resulting in a structural contradiction between “faster alerts” and “faster congestion”. While space constraints preclude an exhaustive exploration of all scenarios, current results strongly suggest that a major contributing factor is the inadequate width of primary streets within the historical district. A key concern is that large numbers of tourists, unfamiliar with the layout of the district, tend to cluster along major arterial roads [16], leading to significant congestion, while the densely packed secondary roads within the district remain underutilized. Additionally, this imbalance is further exacerbated by potential psychological and social behavioral factors. For example, crowd density and panic levels have been shown to reduce evacuation efficiency in panic dynamics models [55], while stronger herd behavior has similarly been linked to lower evacuation efficiency in cruise ship emergencies [56].

To address these challenges, we propose implementing a “coordinated evacuation” strategy tailored to historical districts. This strategy should prioritize the identification and guidance of alternative evacuation routes to promote balanced load distribution. Given the spatial constraints that preclude road widening, the focus should shift to optimizing the use of existing infrastructure. Appropriate signage and dynamic path guidance systems can help alleviate potential traffic pressure. Due to the high road network density in historical districts, evacuation flows can theoretically be distributed across multiple routes. Therefore, rather than relying solely on physical expansion, we advocate for multi-route guidance as a viable alternative. This approach can help mitigate bottlenecks while accommodating varying evacuation demands. Moreover, future research should incorporate psychological effects, information dissemination mechanisms, and route accessibility to construct a more comprehensive evacuation framework specific to historical districts. It is also necessary to examine variations in evacuation patterns between day and night to capture temporal shifts in demand [60].

4.2. Optimizing Building Environments for Historical Districts Considering Dynamic Diversity

Although this study proposes a relatively simplified staged model, it remains important to acknowledge significant discrepancies between dynamic evacuation analyses—considering fire progression—and static analyses that neglect such dynamics, both globally and locally, including their nonlinear interactions. Dynamic simulations clearly demonstrate that fire progression continually influences the operational capacity of transportation systems. Specifically, the ignition location and wind direction not only affect disaster propagation patterns but also significantly shape the spatial distribution and intensity of traffic congestion and evacuation flows. Such inherent dynamic complexity presents substantial challenges to previous studies on historical district evacuations, which predominantly relied upon static analyses based on single-scenario assessments.

Several notable differences emerge between static and dynamic approaches. Firstly, evacuation durations significantly increase under multiple dynamic scenarios. Simulation results indicate that more than 50% of evacuees experience evacuation times approximately twice as long as those predicted by static analyses—findings consistent with results reported in other studies [16,17]. Given the typical spatial and infrastructural characteristics of historical districts, these insights are likely generalizable to other similar urban settings.

Secondly, different stages of fire progression have distinct impacts on infrastructure. In particular, during the initial stages of a fire, the rate of road blockage increases substantially faster than the rate at which buildings become fully engulfed by flames. Thirdly, the spatial relationship between the ignition point and the shortest evacuation routes significantly influences evacuees’ detour behaviors. Fourthly, the dynamic traffic loads on individual roads vary considerably across different scenarios. These findings underscore the limitations of previous research, which primarily utilized single-mode, static analyses and therefore could not fully capture the complex evacuation dynamics present in historical districts [20,24,59].

Therefore, we advocate for an approach in fire hazard analysis and evacuation planning that first evaluates a diverse set of scenarios using dynamic methods, incorporating multiple variables and conditions. An envelope approach should be employed to integrate results from multiple worst-case scenarios. For example, given uncertainties associated with specific evacuation warning distances, the most adverse scenario (e.g., a 600 m warning distance) should be applied to determine evacuation corridor widths. Additionally, entrance widths at the external emergency shelter (specifically the 70 m western entrance and 7 m southeastern entrance at Diqing Square) on both the eastern and western sides should be designed to accommodate maximum pedestrian flow observed across all potential scenarios.

This study is limited by methodological constraints, as it does not include real-world validation and does not account for certain fire-related factors such as psychological panic responses on evacuation behavior. Additionally, it omits certain built environment changes and psychological behaviors during evacuation, such as the destruction of evacuation signage due to fire, which could lead to disorientation and panic. Previous studies have demonstrated that these elements can further complicate evacuation dynamics. For example: Crowd density and panic levels have been shown to affect evacuation efficiency in crowd panic dynamics models [55]; stronger herd behavior has been found to reduce evacuation efficiency in cruise ship emergencies [56]; the accuracy of emergency evacuation simulations has been analyzed based on community layout, population distribution, and key parameters [57]; the role of emergency signage in subway evacuations has been assessed for its impact on evacuation efficiency [58].

Although extensive research indicates that smoke can pose significant health impacts within certain outdoor ranges, considering that Diqing Square serves as the evacuation destination for Dukezong Ancient Town, it may be located within the smoke influence zone but is not positioned in the downwind direction under the wind conditions examined in this study. Therefore, the simulation of smoke impacts on evacuee health was not incorporated into this model. The inclusion of such considerations could potentially affect the accuracy of this model and will be considered for future research and model development.

In conclusion, evacuation policy formulation for historical districts should rely on dynamic analyses, incorporating the most severe conditions identified at different stages of fire progression, while simultaneously optimizing the use of existing evacuation infrastructure.

5. Conclusions

This study develops a dynamic fire–evacuation interaction framework for historic districts, using Dukezong Ancient Town as a case study to evaluate how fire progression influences evacuation behavior and built environment conditions.

The established fire simulation model exhibits strong performance, accurately reproducing fire spread patterns. Fire damage outcomes vary significantly under different conditions, with burned areas in the two most critical scenarios exceeding the 16-case average by 40.9% to 44.7%. In worst-case evacuation scenarios, average evacuation distances increase by 2.1–3.1% while durations (586.52 s and 638.76 s) are 37.4–49.5% longer than group averages. Ignition location and wind direction exert distinct impacts: centrally ignited fires cause immediate severe disruptions, whereas peripherally ignited fires result in prolonged cumulative effects. Dynamic simulation results show evacuation metrics increase rapidly during the first 15–25 min, surpassing static estimates by 24–26% in distance and 135–155% in time. Both scenarios exhibit severe congestion within 25 min, each reporting 14 congestion points concentrated along east–west arterial roads. Although average detour increases are modest (less than 30 m), evacuees near ignition points may divert over 250 m, greatly elevating evacuation risk. Evacuations to the emergency shelter show substantial duration increases (78–93%) and pronounced flow imbalance, with eastern entrance pedestrian density reaching 3–4 times that of the west.

Based on these findings, this study proposes a three-stage evacuation–fire interaction model: (1) 0–10 min—localized detours due to fire onset; (2) 11–25 min—congestion driven by interactions between evacuation strategy and road structure; (3) after 25 min—fire predominantly affects the physical environment rather than behavior. The model highlights the structural contradiction between rapid warnings and limited traffic capacity, which can lead to “fast alert-fast congestion” effects. Therefore, multi-route guidance strategies, real-time information systems, and behavioral considerations are recommended over corridor widening. Finally, dynamic simulations reveal that static analyses tend to underestimate evacuation duration and route complexity, underscoring the need for worst-case scenario evaluations and multi-scenario envelope design approaches.

The three-stage framework and dynamic modeling approach are transferable to other heritage districts facing similar fire-evacuation challenges. The findings support multi-route guidance and scenario-based planning as practical alternatives to costly infrastructure modifications, offering valuable insights for emergency management in historic urban environments.