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In recent years, the role of vehicular networks has become increasingly important for the future of Intelligent Transportation Systems, as they are useful for providing safety, assistance to drivers, and traffic control management. Many vehicular network applications such as routing, mobility management, service discovery, and collision avoidance protocols would benefit from possessing vehicles' prior location information to improve their performance. However, the rapid mobility of vehicles and the degree of error in positioning systems create a challenging problem regarding the accuracy and efficiency of any location prediction-based model for vehicular networks. Therefore, a number of location prediction techniques has been proposed in the literature. In this paper, we study and compare the accuracy and effectiveness of the following location-based movement prediction models: Kalman filter, Extended Kalman filter (EKF), Unscented Kalman filter (UKF), and Particle filter for vehicular networks. We compare the performances of these prediction techniques with respect to different mobility models, and provide some insights on their capabilities and limitations. Our results indicate that Particle filter outperforms all other predictors with respect to location error. In addition, EKF and UKF demonstrated an increase in efficiency of more than 50% when additional measurements input were integrated with the predictors. [ABSTRACT FROM AUTHOR]

Differing from the traditional traffic, connected vehicles enable information sharing between vehicles at vicinity to facilitate cooperative path planning, which may positively affect the congestion propagation process. In this paper, we propose to modeling and simulating traffic congestion propagation in such new situation where the path planning is driven by a temporal or spatial preference with aims at investigating the effects of various factors on traffic congestion, e.g. traffic light, mobility pattern, traffic density and communication radius. Simulations show that the traffic congestion is indeed affected by the concerned factors; however, the traffic congestion fails to be mitigated persistently as the communication radius increases beyond a certain threshold. The result is helpful for understanding the traffic congestion propagation in connected vehicles. [ABSTRACT FROM AUTHOR]

In vehicular ad hoc networks, road traffic information can be used to support efficient routing. However, dissemination of up-to-date global road traffic information usually consumes considerable network resources and may also lead to the scalability issue. On the other hand, the highly dynamic nature of road traffic information makes it difficult to collect and disseminate such information in a timely fashion. Outdated information can lead to inefficient routing decisions and thus degraded routing performance. This paper proposes a distributed routing protocol using partial accurate routing information (RPPI). In RPPI, each node uses accurate local traffic information in its local zone and statistical traffic information in remote areas for route selection, which can significantly reduce the communication overhead. Simulation results show that RPPI can achieve better routing performance in terms of end-to-end packet delivery ratio and end-to-end packet delivery delay as compared with existing work. [ABSTRACT FROM AUTHOR]