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ICTD 2025

Connected & Automated Vehicles (CAV) Impacts

POSTER RECEPTION (View list of posters here)

Modeling Traffic Dynamics to Optimize Emergency Evacuations: A Case Study of Saratoga Springs, Utah

Tuesday, June 10, 2025

4:45 PM – 6:00 PM MT

Primary Author(s)

AA

Asad Ali (he/him/his)

Graduate Research Assistant
North Dakota State University
Fargo, ND, United States

Co-Author(s)

JF

Jack Firman, n/a

Student
NDSU
fargo, North Dakota, United States
MG

Mingwei Guo, Ph.D. Candidate

Graduate Research Assistant
North Dakota State University
Fargo, North Dakota, United States
Talha Ahmed (he/him/his)

Graduate Research Assistant
North Dakota State University
Fargo, ND, United States
Ying Huang, Ph.D.

Welch Faculty Fellow Professor
North Dakota State University
Fargo, North Dakota, United States
PL

Pan Lu, Ph.D.

Associate Professor of Transportation and Supply Chain
North Dakota State University
Fargo, North Dakota, United States

Submitter(s)

AA

Asad Ali (he/him/his)

Graduate Research Assistant
North Dakota State University
Fargo, ND, United States

Abstract: Natural disasters like hurricanes, wildfires, and flash floods pose immediate hazards that require swift emergency evacuations to safeguard lives and minimize damage. This study focuses on the use of travel demand modeling to analyze traffic dynamics during wildfire evacuations in Saratoga Springs, Utah, an area with a history of significant wildfire events. Since the Knolls Fire in 2020, the city has experienced a 50% increase in both population and vehicle ownership, accompanied by substantial infrastructure improvements, particularly the expansion of the primary evacuation route to a four-lane, two-way road. However, it is essential to assess whether this enhanced infrastructure can adequately accommodate evacuation traffic during an emergency. This research aims to employ a comprehensive travel demand model to forecast and simulate multiple evacuation scenarios and examine the influence of various factors, including road capacity, traffic control measures, and the penetration of autonomous vehicles (AVs) on congestion and overall evacuation efficiency. The study compares simulation outcomes with historical data from past wildfire events to evaluate the performance of the enhanced road network during evacuations. Anticipated results indicate that incorporating AVs into the evacuation process, along with strategic routing and effective traffic management, can significantly improve evacuation effectiveness. This research emphasizes the essential role of data-driven modeling in enhancing disaster preparedness and ensuring timely, efficient evacuations as urban areas increasingly face risks from natural disasters.

Learning Objectives:

Attendees can expect to learn the following from this session:

List the factors influencing traffic dynamics during wildfire evacuations
Describe the impact of infrastructure improvements on evacuation efficiency
Define the role of autonomous vehicles in improving evacuations