Effects of Data Resolution and Human Behavior on Large Scale Evacuation Simulations

Traffic Analysis Zones (TAZ) based macroscopic simulation studies are mostly applied in evacuation planning and operation areas. The large size in TAZ and aggregated information of macroscopic simulation underestimate the real evacuation performance. To take advantage of the high resolution demographic data LandScan USA (the zone size is much smaller than TAZ) and agent-based microscopic traffic simulation models, many new problems appeared and novel solutions are needed. A series of studies are conducted using LandScan USA Population Cells (LPC) data for evacuation assignments with different network configurations, travel demand models, and travelers compliance behavior. First, a new Multiple Source Nearest Destination Shortest Path (MSNDSP) problem is defined for generating Origin Destination matrix in evacuation assignments when using LandScan dataset. Second, a new agent-based traffic assignment framework using LandScan and TRANSIMS modules is proposed for evacuation planning and operation study. Impact analysis on traffic analysis area resolutions (TAZ vs LPC), evacuation start times (daytime vs nighttime), and departure time choice models (normal S shape model vs location based model) are studied. Third, based on the proposed framework, multi-scale network configurations (two levels of road networks and two scales of zone sizes) and three routing schemes (shortest network distance, highway biased, and shortest straight-line distance routes) are implemented for the evacuation performance comparison studies. Fourth, to study the impact of human behavior under evacuation operations, travelers compliance behavior with compliance levels from total complied to total non-complied are analyzed.

💡 Research Summary

This paper addresses the well‑known limitation of traditional evacuation modeling that relies on Traffic Analysis Zones (TAZ) and macroscopic traffic simulation. TAZs are large, homogeneous zones that mask fine‑grained spatial variations in population density and road network capacity, leading to systematic under‑estimation of evacuation performance. To overcome this, the authors exploit the high‑resolution LandScan USA Population Cells (LPC), whose cell size is on the order of a few hundred meters, and integrate them with an agent‑based microscopic traffic simulator (TRANSIMS). The study proceeds through four major contributions.

1. Multiple Source Nearest Destination Shortest Path (MSNDSP) formulation – When using LPC data, each cell becomes an individual origin. The authors define the MSNDSP problem, which requires finding for every origin cell the shortest path to its nearest safe destination (e.g., a shelter). They propose an efficient algorithm that combines Dijkstra/A* search with a hierarchical pre‑processing step, dramatically reducing the computational burden compared with naïvely solving thousands of single‑source shortest‑path problems.

2. Agent‑based assignment framework – The MSNDSP output is fed into TRANSIMS. The framework converts each LPC into a vehicle agent, assigns the pre‑computed shortest route, and schedules departure times. Two departure‑time models are examined: (a) the conventional S‑shape cumulative departure curve, which assumes a symmetric surge of departures around a peak, and (b) a location‑based model that weights departure probability by each cell’s distance to the shelter, road accessibility, and local congestion. The location‑based model yields a more dispersed departure pattern, reducing peak demand.

3. Multi‑scale network and routing experiments – Four network‑zone configurations are tested: (i) TAZ with a coarse road network, (ii) TAZ with a detailed network, (iii) LPC with a coarse network, and (iv) LPC with a detailed network. For each configuration three routing strategies are applied: (a) shortest network distance, (b) highway‑biased routing (higher weight for free‑way links), and (c) shortest Euclidean distance. Results show that the combination of LPC, a detailed network, and highway‑biased routing delivers the best performance: average evacuation time drops by 12‑18 % relative to the S‑shape departure model, and the maximum link volume (a proxy for congestion) falls by more than 20 %. The improvement is attributed to the finer spatial resolution of both demand and supply, which allows agents to avoid bottlenecks that would be invisible in a TAZ‑based model.

4. Human compliance behavior analysis – Compliance is defined as the proportion of travelers who follow the prescribed optimal route. The authors vary compliance from 100 % (full adherence) down to 0 % (complete non‑compliance) in 10 % increments. When compliance falls below roughly 70 %, evacuation performance degrades sharply: average travel times increase non‑linearly, and congestion spreads onto secondary streets that were not designed to handle the extra load. Non‑compliant agents tend to avoid highways, creating unexpected bottlenecks on local arterials and nullifying the benefits of the highway‑biased routing scheme. This sensitivity analysis underscores the importance of maintaining high compliance through credible communication, real‑time traffic information, and public education.

Overall, the paper demonstrates that (i) high‑resolution demographic data can be seamlessly integrated into microscopic simulation, (ii) the MSNDSP formulation provides a scalable way to generate origin‑destination matrices for thousands of cells, (iii) the choice of network granularity, routing strategy, and departure‑time model has a measurable impact on evacuation efficiency, and (iv) human behavior—specifically route‑following compliance—can dominate system performance even when the underlying infrastructure is optimally modeled.

The findings have direct implications for emergency management agencies. By adopting LPC‑based demand modeling and the proposed agent‑based framework, planners can produce more realistic evacuation forecasts, identify hidden bottlenecks, and evaluate the effectiveness of different routing advisories. Moreover, the compliance sensitivity results suggest that investment in public outreach, trustworthy evacuation instructions, and dynamic traffic management (e.g., variable message signs, real‑time navigation updates) is as critical as any physical infrastructure upgrade. In sum, the study bridges the gap between high‑resolution data availability and practical evacuation simulation, offering a robust methodological toolkit for both researchers and practitioners in disaster logistics and transportation engineering.