研 究 生：杨 文
A dissertation submitted to
Tongji University in conformity with the requirements for
the degree of Doctor of Philosophy
Traffic Flow and State Prediction Based on
School: School of Transportation Engineering
Discipline: Transportation Engineering
Major: Transport Planning and Management
Candidate: Yang Wen
Supervisor: Prof. Yang Dong-yuan
ITS(Intelligent Transportation System) is considered as one of the most effective methods to ease traffic congestion , reduce vehicle emissions , decrease traffic accident numbers and solve many other traffic problems.Short-term traffic flow analysis is the core of ITS. Exact prediction and state estimation of traffic flow are the foundation of traffic information service , control and guidance. Therefore, the study of effective theories and methods for short-term traffic flow analysis and prediction, to accurately predict and determine the road traffic state with the information obtained, is not only the urgent need in the current development of ITS in practice,but also one of the most important and difficult problems.
This paper will focus on the analysis of traffic flow parameters to find out the regularity from the randomness and uncertainty of the traffic flow changes,the establishment of appropriate methods and models to improve the prediction accuracy.On this basis, traffic flow state of future periods will be predicted.
For these issues of cross section detection data,such as errors, losses, noises and others,this paper studied the preprocessing techniques of original traffic flow data,including data cleaning, smoothing, repairing and data generation of lane group at the same place.
The similarity coefficient and the fluctuation coefficient were put forward as the determining criteria of traffic flow cycle similarity. Main coils of the Inner Ring Elevated in Shanghai were selected as analysis examples.These coils were then divided into three categories and predicted respectively according to the cycle similarity of traffic flow. Furthermore , the predicted results were analyzed and compared to verify the predictability of traffic flow.
This paper established a kind of wavelet neural network model combing with the advantages of both wavelet transform and RBF network.Traffic flow data with similar periods were selected and made a wavelet decomposition and reconstruction for flow-time series, then signal components were predicted by RBF neural network,and prediction results were then synthesized. In the actual case, the prediction results using a variety of time scales were compared with different prediction models.
The selection of historical data is essential to predict the short-term traffic flow, different historical data may result in quite diversified prediction results.The traffic flow similarity of each week and that of the same working day in different weeks were analyzed respectively. The characteristic curve of traffic flow was extracted using least squares support vector machine regression,and then the similar traffic flow data were selected as the training data. LSSVM was used for the prediction model,and continuous 5-day prediction was carried out.
Based on analyzing the characteristics of chaotic traffic flow ,this paper established the calculating method of required parameters for phase space reconstruction.Then an improved adding weight one-rank local region prediction model was proposed.The model took into account the weights of Euclidean distance and similarity between phase points.Prediction results of an actual case show that the prediction accuracy of improved model is better than that of the general model.
An improved method for the selection of training samples was proposed,and the multi-step prediction model was established with least squares support vector machines. After the selection of temporary neighboring points using the Euclidean distance, these temporary neighboring points must satisfy certain similarity coefficient. In the actual case,the prediction results of the general method and the improved method were compared.It can be concluded that the prediction model combined with phase space reconstruction and LSSVM could catch the dynamics characteristics of chaotic traffic flow, and the prediction accuracy is much better after the selection of improved method.
An prediction method of traffic state was designed according to the fixed detection data of expressway.Volume and speed were selected as the indicators to evaluate the operating status of traffic flow,and the fuzzy sets of the indicators and the fuzzy rules for the determination of traffic state were designed respectively. 19 detection coils of North-South Elevated Road in Shanghai were selected as analysis examples.The dynamic evolution trend of traffic flow was predicted,and the traffic state of the object link was classified as follows: free, stability, crowd, congestion.
Key Words: flow prediction, similarity coefficient, wavelet analysis, RBF neural network,phase space reconstruction, least squares support vector machine, fuzzy logic