Theoretical Research For Short-term Traffic Flow Prediction In Multi Traffic States On Urban Expressway Network

Traffic states analysis, prediction and intelligent traffic operation can be realized as the development of traffic data collection technology on the road. However, most existing researches focus on the short-term traffic flow prediction in singleness traffic state on the singleness section, which can hardly meet the needs of traffic guidance system. Therefore, it is imperative to further investigate into the methodology for short-term traffic flow prediction in multi traffic states, which is not only a core element of intelligent transportation systems but also an important base of traffic information service, traffic control and guidance which can provide travelers with efficient information and help them to choose an optimal path so as to perform path guidance, to save travel time of travelers, to relieve traffic congestion, to reduce air pollution and to save energy.This dissertation centers on the methodology for short-term traffic flow prediction in multi traffic states, taking into account the spatial-temporal characteristics of the traffic flow on urban expressway network. Firstly, the existing relevant researches were reviewed, categorized, and analyzed in perspectives of methodologies and model developments, which lays groundwork for further research in this dissertation. Secondly, established traffic flow models based on linear regression analysis with characteristics of traffic flow on urban expressway, split traffic states. An analytical model for the short-term traffic flow prediction influenced by spatial-temporal characteristics was proposed base on multi traffic states. Thirdly, accounting for the spatial-temporal characteristics in multi traffic states, such as free traffic, congested traffic and jam traffic, short-term traffic flow prediction modeles were proposed based on traffic flow conservation equation which was discrete by adopting spatial-temporal discretization idea of partial differential equation. And then, consideration the influence factors of on and off ramp, alteration of lanes number and road grade, the model of short-term traffic flow prediction was converted into the state-space model of short-term traffic flow prediction. Fourthly, the parameters of the proposed state-space model were estimated by qualitative and quantitative analysis methods and the estimation algorithm of the proposed state-space model was designed based on Kalman method. Finally, in view of identification of traffic states and identification of traffic states variation trend, two methodology of short-term traffic flow prediction were proposed based on the state-space model of short-term traffic flow prediction in multi traffic states. The short-term traffic flow prediction in multi traffic states on urban expressway network was realized by detection data.What it follows contains the main conclusions of this dissertation:1. A short-term traffic flow prediction model to consider the spatial-temporal characteristics in free traffic on urban expressway was developed based on traffic flow conservation equation. Short-term traffic flow prediction model was dispersed based on upwind difference scheme of partial differential equation. And then, consideration the influence factors of on and off ramp, alteration of lanes number and road grade, the model of short-term traffic flow prediction was converted into the state-space model of short-term traffic flow prediction in free traffic. At last, the estimation algorithm of the proposed state-space model was designed based on Kalman method.2. The model of short-term traffic flow prediction in jam traffic was proposed based on conservation equation, which was dispersed by adopting Lax-Wendroff scheme. Then, consideration the factors of on and off ramp, alteration of lanes number and road grade, the model of short-term traffic flow prediction was converted into the state-space model of short-term traffic flow prediction in jam traffic. The estimation algorithm of the proposed state-space model was designed based on Kalman method.3. Considering the spatial-temporal characteristics of traffic flow, speed model was dispersed by adopting the improved Lax-Wendroff scheme. Then, the state-space model of short-term traffic flow prediction was proposed in congested traffic. Estimation algorithm of proposed state-space model was designed based on Kalman method.4. Two models were proposed based on SVM model and Elman neural network method to realize identification of traffic states and traffic states variation trend. Short-term traffic flow prediction in multi states was realized by adopting state-space model of short-term traffic flow prediction in free traffic, congested traffic and jam traffic based on the traffic states identification. Empirical study and prediction result were analysis in the end.