title: Key Technology of Real-Time Road Navigation Method Based on Intelligent Data Research.
abstract: The effect of traffic flow prediction plays an important role in routing selection. Traditional traffic flow forecasting methods mainly include linear, nonlinear, neural network, and Time Series Analysis method. However, all of them have some shortcomings. This paper analyzes the existing algorithms on traffic flow prediction and characteristics of city traffic flow and proposes a road traffic flow prediction method based on transfer probability. This method first analyzes the transfer probability of upstream of the target road and then makes the prediction of the traffic flow at the next time by using the traffic flow equation. Newton Interior-Point Method is used to obtain the optimal value of parameters. Finally, it uses the proposed model to predict the traffic flow at the next time. By comparing the existing prediction methods, the proposed model has proven to have good performance. It can fast get the optimal value of parameters faster and has higher prediction accuracy, which can be used to make real-time traffic flow prediction.
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Haijing
authors.initials: H
authors.lastname: Tang
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yu
authors.initials: Y
authors.lastname: Liang
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Zhongnan
authors.initials: Z
authors.lastname: Huang
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Taoyi
authors.initials: T
authors.lastname: Wang
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Lin
authors.initials: L
authors.lastname: He
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Yicong
authors.initials: Y
authors.lastname: Du
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Xu
authors.identifier: 0000-0001-7983-6473
authors.initials: X
authors.lastname: Yang
authors.affiliation: School of Software, Beijing Institute of Technology, Beijing 100081, China.
authors.forename: Gangyi
authors.initials: G
authors.lastname: Ding
mesh_terms: D000465:Algorithms; D001334:Automobile Driving; D001336:Automobiles; D057225:Data Mining; D005544:Forecasting; D006801:Humans; D018986:Motor Vehicles; D016571:Neural Networks, Computer; D011336:Probability; D013997:Time Factors