Integrated Modeling of Mobility and Communication in Vehicular Wireless Networks

Abstract

Due to global increase in urbanization, improving the transportation facilities has become a challenge for governments and researchers.

Increasing rates of traffic congestion on the roads have resulted in a significant increase in the travel times, the expenditure of fuel and other resources. These growing concerns about traffic conditions have motivated researchers towards enabling intelligence in the vehicles, so that the drivers can be warned beforehand about the adverse driving conditions that lie far ahead of them. This intelligence has come in the form of equipping the vehicles with computing technologies and wireless communication devices that enables them to communicate with other vehicles and network infrastructure in their proximity. A wide range of traffic flow models have been developed since past half a century to study the operations of road traffic. These existing traffic flow models assume that drivers are aware only of their immediate surroundings that are limited by the range of a human eye-sight. Hence, the drivers' decisions in such models are based on the traffic conditions prevailing in the neighborhood of the vehicle. With the introduction of communication networks, e.g. Vehicular Ad hoc Networks, the visibility of the drivers will increase as they will be able to gather the traffic information at locations that are far ahead of them. This increased visibility will influence the drivers' decisions and the way in which the traffic operations are predicted and modeled. Hence, with the introduction of communication capabilities in the vehicles, there is a need to define new traffic flow models that will take into consideration the impact of communication on the drivers' decisions and the traffic mobility. Till date, however, no work has been done towards the joint consideration of the two areas of traffic mobility and communication. In the research presented in this thesis, we mathematically formulate an integrated traffic flow model based on the partial-differential fluid dynamic equations. This traffic flow model predicts the traffic behavior on the road when a fraction of the vehicles are equipped with the communication capabilities. We also, numerically investigate the predictions made by this model in various traffic scenarios and compare their nature with the actual road traffic simulations that trace the behavior of each individual car.