A Study on Identifying Road Network Vulnerability
The use of GPS navigation apps has been surging with increasing the use of smartphones. People used to use a map to find a way to their destination, but now the navigation apps provide us with several options that can be taken as the best path for drivers. Also Connected and Autonomous Vehicles (CAVs) have been attracting attention and have improved a lot in tandem with the popularization of electric vehicles. Also, it is expected that autonomous driving which means that the car is self-driven instead of being driven by a person will emerge in the near future. This autonomous driving will rely on algorithms that will determine the best path once the destination is set and will perform dynamic routing to adapt to road network congestion during the trip. These algorithms that suggests the best paths will affect road traffic flow. Furthermore, when more CAVs rely on the same best path algorithm in the future, it will lead to a significant impact on a city’s road network traffic if an adversary can intentionally cause real or virtual disruptions in an few strategic locations.This thesis presents the results of simulation experiments to determine vulnerability in road network in the city of Davis in California. Specifically, by developing a simulation model, this study models how disruption in one part of the network can spread and how this is related to or not related to some of the topological properties of the network. Lastly, this thesis discusses a way to minimize the effect of the vulnerability.