Study On Short Time Traffic Flow Prediction

Study on short time traffic flow (STTF) prediction is always popular in the world during the past few years.Traffic engineers had done a lot of works in this field, and obtained lots of remarkable fruits.Then futher study on STTF prediction is done in this paper.(1) We review the history and status on traffic engineering study, then, the importance of STTF prediction is introduced in Urban Traffic Control System, Automatic Congestion Identification (ACI) and Traffic Guidance Systems (TGS)–a sub-system of the Intelligent Transportation System (ITS).(2) Based on traffic volume time and space distribution on urban traffic network, main characteristics of traffic volume are introduced and analyzed.(3) In this thesis, we briefly introduce some STTF prediction methods used popular in recent years, point out their merits and shortages. The methods concluding: multiple linear regression, exponential smoothing, artificial neural networks (ANN), time series analysis and multi-model fusion algorithm (MMFA).(4) Based on these methods, traffic volume properties, and traffic volume data from traffic detectors, we build up some models and make a few analysis.An improved time series model is provided in this thesis, the model is based on both current and history data. Comparing to the traditional time series model which is just using current data (dealing with by the first difference firstly), it includes more information and has less error. Then equal step length MMFA and dynamic step length MMFA are also used to predict.At last, the results of models current using and models from this paper are compared and analysised. Then merits and shortages of these models are pointed out, questions need to be sovled are also presented, development of study on STTF prediction in the future is brief introduced.