Integrated Management of Emergency Vehicle Fleet

Abstract

The growing public concerns for safety and the advances in traffic management systems, that have made the availability of real-time traffic information a reality, have created an opportunity to build integrated decision support systems that can improve the coordination and sharing of information between agencies that are responsible for public safety and security and transportation agencies to provide more efficient Emergency Response Service.

In an Emergency Response System, reduction of the duration of response time can yield substantial benefits. The response time plays a crucial role in minimizing the adverse impacts: fatalities and loss of property can be greatly reduced by reducing the response time for emergencies. In this dissertation, we have developed an integrated model that can assist emergency response fleet dispatchers in managing the fleet. This model can help reduce the response time and improve service level by specifically accounting for the following:

Vehicle Deployment: given real-time information about the status of the emergency response fleet, traffic information and the status of emergency calls, select proper fleet assignment schemes that satisfy various operation requirements.

Vehicle Routing: given real-time traffic information, provide real-time route guidance for drivers of dispatched vehicles. This goal is achieved by applying various shortest path algorithms into the solution procedure.

Planning and Evaluation: given the status of the fleet and the frequency of emergency calls in various areas of a region, the model can help evaluate the performance of the current system and help plan for potential sites for the relocation of vehicles and allocate an appropriate fleet of vehicles to these sites.

The vehicle deployment problem is formulated as an integer optimization problem. Since this problem has been shown to be NP-hard and because of the nature of emergency response, we developed heuristics which can provide quality solutions with short computational times. Several test algorithms are proposed to solve the emergency response vehicle deployment problem. Different methods for obtaining lower bounds for the value of objective function are analyzed in this dissertation. To evaluate the performance of the system under various scenarios, a simulation model is developed. The simulation system is calibrated based on real-world data. The results of simulation and analysis show the proposed system can effectively improve the emergency response service level. Application of this model in facility allocation illustrates its usage in other relevant operational scenarios.