Research Of Real-time Dynamic Traffic Information And Navigation Based On Time Segment

Intelligence and smartness of urban transportation is an important indicator to measure the quality of urban planning and development.Smart transportation and smart city has also been a frequently seen subject of research both home and abroad.Among all the hot issues,how to enable a highly scalable,precise,real-time dynamic network naturally turns out to be the critical technology and a basic guarantee of smart city development.Based on massive quantity of real-time data collected by GPS systems on taxis,this paper attempted to construct a dynamic network of urban public transportation.On top of this fundamental platform,deep data analysis and mining is conducted to construct a model of road network.In addition,the traditional route search algorithm based on shortest route is revised with new elements added such as time slice and status of city road network.First,to construct a fundamental platform that is strong to load data,agile to respond to service requests and powerful to scale up the system,the paper chooses to adopt HDFS distributed file system to address real-time data collected by taxis.Second,considering the features of real-time taxi GPS data,and combining the HDFS distributed file system and the Spark distributed computing platform,the paper conducts in-depth data mining and analytics.This includes regular,daily travel pattern and service status of of each and every taxi,passenger traffic distribution,city OD as well as other information in relation to transportation.On that basis,speed and location information of taxis are mirrored on the specific city road network,historical data is analyzed and then a trunk public transportation network is constructed.At the end of this paper,results of experiments are compared through visualized approach.The visualization is used in the comparison of experiment result.Lastly the route navigation algorithms such as SPFA which are based on shortest route principle are supplemented and improved.Factors such as travel time and average on-road speed are added.As a result,an optimal route navigation algorithm combining multi-dimensional factors such as route length and travel time is suggested.Construction of a dynamic city transportation network and development of optimal navigation system based on the dynamic network serve as the critical technologies to enable real-time traffic information inquiry and dynamic automobile navigation and develop smart cities at large.Methodologies of constructing fundamental platform of city transportation network and enabling data mining and analytics based on taxi GPS data,and city route navigation algorithm combining multi-dimensional factors can meet the unique needs of modern smart city and therefore has real value in realizing smart and intelligent city transportation.