Guojiang Shen

Study on Road Network Traffic Coordination Control Technique With Bus Priority

On the basis of distributed traffic control framework, fuzzy theory, and artificial neural networks technique, the road network traffic intelligent coordination control technique with bus priority was proposed. The whole road network was regarded as a large-scale system, and the subsystems were the intersections. Multiphase intelligent signal controller that controlled its own traffic and cooperated with its neighbors was installed at each intersection. By exchanging information collected from its social vehicle detectors and the bus detection and location devices, and cooperating with adjacent signal controllers, social vehicle coordination and bus priority in the whole road network were realized. Bus priority module, green observation module, and phase switch module comprised the hard core of the controller. In each module, the fuzzy rule base system was designed in detail. To improve the control systems robusticity, the fuzzy relations of the three modules were implemented by one neural network. The target of this proposed method was to maximize the possibility for vehicles to depart from the upstream intersection, and the traveling bus nearby the local intersection to pass the local intersection without stoppage while the utility efficiency of the green signal time was at a relatively high level. The actual application shows that the proposed method can decrease the average vehicle delay and average travel time effectively.

Urban Traffic Trunk Two-direction Green Wave Intelligent Control Strategy and Its Application

A fire-new two-direction green wave intelligent control strategy was presented to solve the coordinated control problem of urban traffic trunk. The whole control structure was divided into the coordination layer and the control layer. The optimal public cycle time, up-run offset and down-run offset were calculated in the coordination layer, and the splits of each intersection in the trunk were adjusted in the control layer at the end of each cycle. The cycle time was adjusted by one fuzzy neural network arithmetic according to the traffic flow saturation degree of the key intersection in trunk. The offsets were calculated by the up-run speeds or the down-run speeds. The variable splits of each intersection were based on historical and real-time traffic information. The goal of this control strategy was to achieve the zero stop of the up-run or down-run vehicles in the trunk and make the vehicle average delay time less. The factual application results have shown to be very good

Application of fuzzy control theory in multi-phase traffic control of single intersection

The model of the single traffic intersection is described according to the actual situation at the intersection of Tiyuchang Road with Wulin Road in the city of HangZhou. A fuzzy control approach for this intersection is presented by means of the vehicle flow information in four directions and the model described. With the performance criterion being the average delay of vehicles, the timing plan is determined. Simulations show that the fuzzy control can perform the multiphase signal traffic control of a single intersection effectively and the results obtained are satisfactory.

An intelligent hybrid forecasting model for short-term traffic flow

According to the thought of intelligent forecasting and hybrid forecasting, an Intelligent Hybrid (IH) model for short-term traffic flow forecasting was presented. The IH model had three sub-models: History Mean (HM) model, Artificial Neural Network (ANN) model and the Fuzzy Combination (FC) model. By means of the good static stabilization character of HM method, the HM model predicted the traffic flow by the Single Exponential Smoothing method based on the historical traffic data. Otherwise, the ANN model was a 1.5-layer feed-forward neural network built by some common S-function neurons. Because of the strong dynamic nonlinear mapping ability of ANN, the ANN model can estimate the actual traffic flow in a very precise and satisfactory sense. The FC model mixed the two individual forecasting results by fuzzy logic and its output was regarded as the final forecasting of the traffic flow. Factual application results show that the IH model, which takes advantage of the unique strength of the HM model and the ANN model, can produce more precise forecasting than that of two individual models. Thus, the IH model can be an efficient method to the short-term traffic flow forecasting.

The Traffic Flow Model for Single Intersection and its Traffic Light Intelligent Control Strategy

From the viewpoint of macroscopic dynamic characteristics of single intersection traffic flow, a commonly used macroscopic dynamic deterministic traffic flow model for traffic control is developed. Furthermore, according to fuzzy control theory and the policemans experience, multi-phase traffic light control strategy based on the length of the waiting queue is presented. In this strategy, the length of queue is regarded as the control object and the length of queue on the contiguous phase is used to determine the timing plan. The simulation research and actual application show that this new control strategy is more near to the manned decision process and can perform the multi-phase signal traffic control of a single intersection more effectively than the fixed-time plan. The application result is satisfied

Urban arterial traffic intelligent coordination control technique and its application

This paper presents a new intelligent control strategy to solve the coordination control problem of urban arterial traffic. The whole control structure includes two layers - the coordination layer and the control layer. Public cycle time, splits, inbound offset and outbound offset are calculated in the coordination layer. Public cycle time is adjusted by fuzzy rules according to the traffic flow saturation degree of the key intersection. Moreover, the fuzzy rules are implemented by artificial neural networks to improve control systems robusticity. Splits are calculated based on historical and real-time traffic information. Offsets are calculated by the real-time average speeds. The control layer determines phase composition and adjusts splits at the end of each cycle. The target of this control strategy is to maximize the possibility for vehicles in each direction along the arterial road to pass the local intersection without stop while the utility efficiency of the green signal time is at relatively high level. The actual application results show the proposed method can decrease the average travel time and average number of stops, and increase the average travel speed for vehicles on the arterial road effectively.

Multi-agent fuzzy signal control with bus priority

An urban region traffic intelligent coordinated control method based on multi-agent is presented, and the control target is to make bus-weighted vehicle average delay least. A two-layer hierarchical control architecture is used. The high-layer is the zone intelligent controller (ZCA) deciding key intersection and adjusting common cycle length real-time. The low-layer is constructed by intersection intelligent controllers (ICA). An ICA is divided into 3 modules: phase choosing module, green observation module and decision module. ICAs cooperating with each other, can optimize phase sequence and phase length. Multi-agent fuzzy signal control strategy with bus priority is used and the fuzzy rules are optimized by genetic algorithm (GA). Simulation results indicate, compared with fixed-time control, this control strategy can reduce social vehicles mean delay efficiently, and reduce bus mean delay more.