| dc.description.abstract | Traffic congestion always is a serious problem in urban areas in the world. It is getting worse in regions of all sizes. In 2007, traffic congestion caused urban Americans as many as 4.2 billion hours to travel and to purchase an extra 2.8 billion gallons of fuel for the congestion. Therefore, traffic congestion not only causes economic loss and environment pollution but also damages health of drivers.
With the evolution of car and communication technologies, people developed the intelligent transportation system (ITS) to solve traffic problems. The ITS consists of electronics, communications, information and sensing technologies, and integrated management strategy of human, road and vehicle. Researchers study the ITS to provide real-time information and improve transportation system safety, efficiency and comfort, while reducing air pollution and noise impact on the environment. Vehicular ad hoc network (VANET) is a promising approach for future intelligent transportation system.
In this research, we take into consideration the use of traffic information and deployment of roadside devices. Although Inter-Vehicle Communication (IVC) is popular adopted in VANET, IVC often suffers the packet loss, broadcast storm, and network bandwidth problems which makes traffic message undelivered or delayed. In addition, data communication in VANET should consider characteristics of traffic and travel information, road scale. For these reasons, we develop an efficient mechanism for information collection and dissemination to reduce bandwidth consumption and improve vehicle congestion.
This paper presents a compact and efficient traffic information exchange strategy. It includes information aggregation and dissemination, vehicle routing algorithms and system architecture is designed to simulate proposed mechanism. Moreover, we will examine and verify the contribution of the proposed scheme for data network and traffic road network. These simulation results show that VGED can reduce bandwidth consumption about 90% and 6~10% average travel time and some scenes may be to 21%. These results could support our method strongly.
| en_US |