Emergency Evacuation Planning: Minimizing Gridlock and Improving Public Safety

Problem:

Intelligent transportation systems monitor and analyze how vehicles, roadways, and environmental factors affect traffic flow. Most of us are familiar with the frustration of a traffic jam. A typical rush hour impedes the mobility of individual vehicles and significantly slows the overall flow of traffic. This phenomenon is compounded by events of mass mobilization, such as during an evacuation due to a hurricane or other event. When this occurs, traffic can reach a state of gridlock. ITS researchers sought to understand how public safety could be improved during such events by incorporating communication among a percentage of the vehicle population.

Solution:

Road Network Evacuation Model

Road Network Model with Radiation Plume
and Affected Roads

One of the oldest and largest independent, non-profit, applied research and development organizations in the US, approached this problem using AnyLogic to explore if having a percentage of vehicles connected via a smartphone or a dedicated short range communication (DSRC) radio could improve the coordination of vehicles during large-scale evacuations. The vehicle agents incorporate parameters that represent the likelihood of being equipped with a communication device, and the likelihood that they will follow the vehicle in front of them when they are in a congested state. This second parameter approximates the behavior of human drivers who follow other drivers in the assumption that they have knowledge of a better route. Researchers then ran scenarios based on these two parameters and compared total accumulated radiation exposure and time in congestion.

The AnyLogic modeling tool combines agent-based and system dynamic modeling techniques and has a powerful, intuitive graphical interface for constructing this type of model. Researchers modeled an evacuation scenario based on a radioactive spill in an urban area. The model included a simplified traffic system based on the highways of San Antonio, Texas, and three agents representing a vehicle, a roadway network, and an event notification.

Vehicle Agent State Chart and System Dynamics Architecture for Congestion

Vehicle Agent State Chart and System Dynamics Architecture for Congestion

Results:

The results of this research quantified the performance of the traffic system, as measured by average and total radiation exposure to the vehicle population, as well as overall congestion as reported by each vehicle agent, for a number of different scenarios. The results illustrated the safety impact when even a small number of vehicles receive targeted, timely information regarding a potential danger on their current route. The simulations also show a benefit from the vehicle following behavior, which is a secondary beneficial effect for a traffic system comprised of human drivers (the inclusion of autonomous vehicles in a traffic system may negate some of these types of effects; however, AVs will also likely be connected via communication devices).

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