Effect of human behavior on traffic patterns during an emergency

Humans display a variety of behaviors including greedy and other unexpected biases during emergency situations. In this paper, we study the effect of such behaviors on traffic patterns during an emergency situation. In particular, we assume that during an emergency there are traffic police or other personnel trying to guide the vehicles so that the traffic clearance is sped up (to minimize the human or property losses). However each individual driver agent has its own motives and biases that may slowdown such a clearing up of traffic. In particular, we assume that the traffic police use the Ford-Fulkerson Algorithm to maximize the flow in the (traffic) network and then perform an extensive agent based analysis to study the traffic patterns that arise due to a variety of human factors and biases. Through a series of experiments performed using the well-known traffic simulator SUMO we could show that: (a) Ford-Fulkerson Algorithm is indeed efficient for clearance of vehicles in such settings (b) Driver agents that do not follow the directions due to their preferences and biases lose out on an average and (c) As can be expected, having more information about the environment available via prior knowledge and/or real-time updates can offset the effect of biases to a good extent.