Freeway Traffic Congestion Early-Warning And Control Integrating With Mobile Signal Flow

With the rapid growth of traffic demand, freeway traffic congestion becomes increasingly serious. In order to ensure safety and efficiency of freeway, this research constructs the platform for freeway traffic parameters collection with mobile phone location. Based on the prior founding and knowledge, research on freeway traffic congestion identification, early-warning and control are carried out and developed, which provides theoretical base and decision support for freeway management.Based on the platform for freeway traffic parameters collection, relationship between mobile signal flow which varies with space-time and freeway traffic flow is analyzed. A new map matching method for mobile phone location is proposed. The methods to collect vehicle counts with graph clustering, to identify vehicle type and to estimate mean travel speed, traffic density and volume are presented. The factors which affect traffic parameters collection accuracy are further discussed through simulation.According to characteristics of cyclical changes for traffic parameters, seasonal autoregressive integrated moving average prediction model, grey prediction model and general regression neural network prediction model are established. Integrating advantages of three prediction models, minimum variance combination model is set up. Case study shows that minimum variance combination model predicts more accurately than the single model.In view of the fuzziness and randomness of classification for freeway traffic state, a cloud identification model for freeway traffic state is proposed. Based on determination of input parameters, congestion degree for the identified state is calculated according to synthesized cloud theory and cloud similarity definition. Besides, the conditions for congestion verification and congestion type discrimination are also given. The simulation result shows that the proposed approach not only identifies the traffic state accurately, but also reflects degree of congestion and process of congestion change. Furthermore, occurrence time, section, possibility and congestion degree for recurring congestion is predicted by combining cloud identification model with traffic parameters multi-step prediction model.By analyzing factors which affect freeway incident duration, the relationship model between incident duration and its influential factors are set up respectively based on rough theory and least square support vector machine to predict incident duration dynamically. Based on incident duration dynamic prediction and traffic parameters multi-step prediction, considering random variation of arrival and departure vehicles, stochastic queue and Shockwave theory are respectively used to formulate dynamic prediction models for queue length and congestion duration. The step and flow chart of the two prediction models are presented in detail, and their prediction results are compared through case study. On the basis of incident duration and queue length dynamic prediction, the diversion method suitable to solve nonrecurring congestion is developed. It takes maximal queue length as warning index and respectively uses stochastic queue and shockwave theory to deduce the mathematical relationship between diversion volumes and diversion exerted time.The characteristics and principle of local fixed-time metering, local responsive metering and coordinated fixed-time metering is analyzed. An on-ramp fuzzy control approach suitable to solve local recurring congestion is proposed. A fuzzy controller with correction factor which can adjust control rules is designed. In view of weakness of coordinated fixed-time metering, a dynamic coordinated control model composed of macroscopic traffic flow model, queue length model of on-ramp and queue length model of congestion section is established and solved by particle swarm optimization. The effectiveness of the proposed control approaches are further verified with intensive simulation.