The Research Of Traffic Stream-based Path Selection For VANETs

In recent years,with the dramatic development of social economy as well as the ever-accelerating pace of life,the demand of people's travel has become increasingly high.Nevertheless,a series of undesirable consequences resulted from the increase of traffic requirement and car ownership lead to a discomfort to the people's travel,which influences the travel efficiency to some degree.As one crucial component of the Intelligent Transportation System(ITS),Vehicular Ad-Hoc Networks(VANETs)play an important role in enhancing the travel efficiency due to its advantages in the path planning.The existing works which focus on exploring the path planning scheme are mainly classified into two categories,namely,the micro level and the macro level.At the macro level,the designer chooses one best travel route for vehicles from the perspective of the whole topological structure of city's roads,while,at the micro level,the designer selects the optimal route for data packets by carefully designing one data dissemination strategy.In order to improve the travel efficiency,from the perspectives of micro level and macro level,taking the vehicle and data packet as the research objects respectively,we propose different path selection schemes based on traffic stream for VANETs.The main contributions of this dissertation are summarized as follows:In the urban environment,the performance of data transmission is significantly affected by the traffic density with time changing in vehicle ad-hoc network.For making the data quickly reach the destination,the node need choose the optimal packet transmission path with the minimum latency when make planning.In order to find the optimal path,we choose the latency of data transmission as the route metric,and propose a mathematical analysis model to estimate the packet transmission delay along the path.This model gives a detailed process to analyze the latency for different traffic densities.With theproposed model,the node can estimate the transmission delay of packets given the density only.Numerical results show that in urban environments,our analysis model has high accuracy under different configurations.In vehicle movement,the effective traffic prediction could provide the better routeplanning for people to quickly reach the destination.In order to improve the traffic efficiency,we propose a prediction method that can estimate the traffic conditions in urban environment,which puts the real-time GPS data with long-length interval and errors into the dynamic traffic prediction.The proposed method begins with the pre-process of the raw GPS data and map matching to restore the accurate location of the report in the road,as well as filter and supplement the obtained data of specified road sections.Next,we combine fuzzy theory with Markov progress to build the prediction model,where the fuzzy state classification method is used to classify traffic conditions.For the purpose of improving prediction accuracy,the continuous three-step average method is used to establish the equal-dimensional new information model.Finally,we choose speed and traffic as the metrics of traffic state and use the fuzzy reasoning rules to judge the traffic state.The simulation shows the proposed model with high accuracy can be used efficiently in the dynamic traffic state prediction for the urban traffic flow guidance.