Open AccessResearch on Short-term Traffic Flow Forecast and Auxiliary Guidance Based on Artificial Intelligence Theory
Author(s) -
Rongxia Wang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1544/1/012164
Subject(s) - traffic flow (computer networking) , intelligent transportation system , term (time) , computer science , advanced traffic management system , floating car data , field (mathematics) , control (management) , artificial intelligence , operations research , simulation , engineering , traffic congestion , transport engineering , computer security , physics , mathematics , quantum mechanics , pure mathematics
With the development of social economy, intelligent transportation system (ITS) has been booming. It is mainly to realize the omni-directional, real-time, accurate and efficient guidance and control of transportation in a large range. Traffic flow prediction, especially short-term traffic flow prediction, is the basis of urban traffic control and guidance, so traffic flow prediction system is one of the important subsystems in its. Short-term traffic flow prediction technology belongs to the important research field of intelligent traffic control and vehicle guidance. It can help cities to conduct intelligent traffic guidance and enable users to choose the optimal path. Prediction of road traffic flow state and auxiliary guidance are very important basic theories. Based on the theory of artificial intelligence, this paper analyzes the short-term traffic flow prediction and auxiliary induction methods, points out the limitations of the existing prediction methods, and proposes a short-term traffic flow intelligent prediction method combined with artificial intelligence technology.