  We consider the escape interdiction problem in a transportation network. In
the absence of traffic in the network, the criminal/attacker tries to escape
from the city using any of the shortest paths from the crime scene to any
randomly chosen exit point. In the presence of traffic, the attacker chooses
the optimal path, which takes minimum time to reach his destination. On the
contrary, police/defenders try to interdict the criminal on his escape route.
This is a challenging task for police with limited resources. Again, a real
city road network is also complex in nature. First, we develop a
simulation-based model for the optimal allocation of resources using the SUMO
simulator. Next, we focus on developing a more advanced search strategy like
routing with optimal resource allocation. We develop a novel meta-heuristic
approach in a simulation environment to interdict the attacker in a dynamic
crime scenario. Like the previous approach, the attacker follows the path with
optimal time to escape from the city. In contrast, defenders try to catch the
attacker regardless of the path which the attacker takes. The defenders aim is
to maximize the interdiction probability. As time plays a vital role, we choose
a meta-heuristic approach to provide quality solutions in a time-efficient
manner. We test the developed meta-heuristic with a case study on the IIT
Kharagpur map, India. We analyze the performance of the mentioned approaches
using the SUMO simulator both in the presence of traffic and without traffic.
We develop a linear regression model to generate optimal path in the presence
of traffic. Here traffic is generated randomly in the whole network using the
SUMO simulator.
