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Events during an emergency unfold in an unpredictable fashion which makes management of traffic during emergencies pretty challenging. Furthermore, some vehicles would need to be evacuated faster than others e.g., emergency vehicles or large vehicles carrying a lot more people. The Prioritized Routing Assistant for Flow of Traffic (PRAFT) enables prioritized routing during emergencies. However, the PRAFT solution does not compute multiple plans that can help handle better dynamic nature of emergencies. PRAFT maps the prioritized routing problem to the Minimum-Cost Maximum-Flow (MCMF) problem, hence its solution can accommodate maximum flow while routing vehicles based on priority (maps higher priority vehicles to better quality routes (i.e., ones with minimum cost)). We build upon the PRAFT solution to make the following contributions: (a) Develop a Pareto Minimum-Cost Maximum-Flow (Pareto-MCMF) algorithm which can compute all the possible MCMF solutions. (b) Through a series of experiments performed using the well known traffic simulator SUMO, we could show that all the solutions generated by Pareto-MCMF indeed have properties similar to a MCMF solution thus providing multiple high quality options for traffic police to pick from depending on the situation.

Towards a Better Management of Emergency Evacuation using Pareto Min Cost Max Flow Approach