Open AccessApplication Research of Short-term Traffic Flow Forecast Based on Bat Algorithm Support Vector Machine
Author(s) -
Rongxia Wang
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/740/1/012168
Subject(s) - support vector machine , intelligent transportation system , traffic flow (computer networking) , computer science , term (time) , randomness , generalization , identification (biology) , process (computing) , artificial intelligence , data mining , algorithm , machine learning , engineering , mathematics , mathematical analysis , statistics , civil engineering , physics , computer security , botany , quantum mechanics , biology , operating system
Characteristics transition from certainty to randomness, and the prediction difficulty of traffic flow also increases.Short-term traffic flow prediction technology can help cities to induce intelligent traffic by a Urban road traffic is a dynamic and complex system. With the reduction of observation time range, traffic nalyzing and predicting traffic flow. Through the analysis of traffic flow data and the identification and processing of erroneous and missing data, the influence of noise on the prediction process is reduced. Intelligent Transportation System (ITS) is getting more and more attention. At the same time, people put forward higher requirements for vehicle type recognition, license plate recognition, traffic flow prediction and other technologies. Support vector machine (SVM) can find a compromise between model complexity and learning ability according to limited sample data, in order to obtain the best generalization ability. Based on bat algorithm (BA) support vector machine, this paper studies the basic algorithm of pattern recognition and regression analysis and its application in short-term traffic prediction of intelligent transportation system.