城市快速路交通狀態(tài)估計與控制研究
發(fā)布時間：2018-07-28 13:50
【摘要】：城市快速路是全部或部分采用立體交叉與控制出入，供車輛以較高的速度行駛的道路，快速路的修建在一定程度上為緩解城市交通擁堵做出了貢獻。隨著交通出行的日益增加，快速路也出現(xiàn)了交通擁擠現(xiàn)象，而且愈加嚴重。為解決快速路交通擁擠問題，需要對快速路的交通狀態(tài)進行估計，以全面掌握快速路的交通情況，為快速路交通流的管理、控制、誘導等提供有效的數(shù)據(jù)支撐。 快速路交通狀態(tài)是借助于交通流的相關(guān)參數(shù)來衡量某種交通狀態(tài)的近似范圍的，所以可以采用對交通狀態(tài)參數(shù)進行估計的方式，間接實現(xiàn)對交通狀態(tài)的估計。從本質(zhì)上講，交通狀態(tài)參數(shù)估計是一個非線性、非高斯問題，鑒于粒子濾波在處理該類問題時，具有較好的優(yōu)越性，本研究將快速路二階宏觀交通流模型與粒子濾波算法有效結(jié)合，建立交通狀態(tài)參數(shù)估計模型，借助于MATLAB軟件平臺，實現(xiàn)對交通狀態(tài)參數(shù)的準確估計。結(jié)果表明，基于基本粒子濾波構(gòu)建的交通狀態(tài)參數(shù)估計模型可以對快速路交通狀態(tài)參數(shù)做出較好的估計。 在粒子濾波算法應(yīng)用過程中，會出現(xiàn)粒子退化現(xiàn)象。粒子退化現(xiàn)象的發(fā)生，不僅使得粒子多樣性喪失，而且將大部分的計算集中到了一些對結(jié)果貢獻很小的粒子上。為避免粒子退化現(xiàn)象的發(fā)生，本研究采用免疫粒子群算法對粒子濾波進行優(yōu)化，運用改進后的粒子濾波算法進行實例驗證，并與基本粒子濾波算法比較，結(jié)果表明，，改進后的粒子濾波算法可以實現(xiàn)對交通狀態(tài)參數(shù)更精確的估計。 交通狀態(tài)本身具有一定的模糊性和不確定性，適合用模糊理論對其進行劃分，本研究采用模糊C均值聚類方法將交通狀態(tài)劃分為暢通、輕度擁擠和擁擠三種。根據(jù)交通狀態(tài)參數(shù)估計結(jié)果，將其聚類到相應(yīng)的狀態(tài)中，得到交通狀態(tài)估計結(jié)果。 在對快速路交通狀態(tài)準確估計的基礎(chǔ)上，可以實現(xiàn)對快速路主線及其匝道的有效控制，以最大發(fā)揮快速路的服務(wù)能力。將可變限速控制和入口匝道控制相結(jié)合，考慮不同交通狀態(tài)下的特性，建立快速路聯(lián)合控制模型，采用遺傳算法對模型進行求解，并對其進行實例驗證及應(yīng)用。結(jié)果表明，聯(lián)合控制模型可以較好的實現(xiàn)對快速路的有效控制。
[Abstract]:Urban expressway is a kind of road which is used in all or part of it to cross and control the entrance and exit of vehicles at a high speed. To some extent, the construction of expressway has contributed to the alleviation of urban traffic congestion. With the increasing of traffic travel, traffic congestion also appears on the expressway, and it is becoming more and more serious. In order to solve the problem of expressway traffic congestion, it is necessary to estimate the state of expressway traffic, so as to master the traffic situation of expressway and provide effective data support for the management, control and induction of expressway traffic flow. Expressway traffic state is based on the relative parameters of traffic flow to measure the approximate range of traffic state, so the estimation of traffic state parameters can be used to indirectly realize the estimation of traffic state. In essence, traffic state parameter estimation is a nonlinear, non-Gao Si problem. In view of the superiority of particle filter in dealing with this kind of problem, In this paper, the second order macroscopic traffic flow model of expressway and particle filter algorithm are combined effectively, and the traffic state parameter estimation model is established, and the accurate estimation of traffic state parameter is realized by means of MATLAB software platform. The results show that the traffic state parameter estimation model based on basic particle filter can estimate the traffic state parameters of expressway. Particle degradation will occur in the application of particle filter algorithm. The occurrence of particle degradation not only leads to the loss of particle diversity, but also concentrates most of the calculations on some particles that contribute little to the result. In order to avoid the phenomenon of particle degradation, the immune particle swarm optimization algorithm is used to optimize the particle filter, and the improved particle filter algorithm is used to verify it. The results show that the improved particle filter algorithm is compared with the basic particle filter algorithm. The improved particle filter algorithm can achieve more accurate estimation of traffic state parameters. The traffic state itself has certain fuzziness and uncertainty, which is suitable to be divided by fuzzy theory. In this study, the traffic state is divided into three types by fuzzy C-means clustering method: smooth flow, mild congestion and congestion. According to the estimation results of traffic state parameters, the traffic state estimation results are obtained by clustering them into the corresponding states. On the basis of the accurate estimation of the state of the expressway traffic, the main line of the expressway and its ramp can be effectively controlled so as to maximize the service capacity of the expressway. By combining variable speed limit control with on-ramp control and considering the characteristics of different traffic conditions, a joint expressway control model is established. Genetic algorithm is used to solve the model, and an example is given to verify the model and its application. The results show that the joint control model can effectively control the expressway.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491
本文編號：2150356
[Abstract]:Urban expressway is a kind of road which is used in all or part of it to cross and control the entrance and exit of vehicles at a high speed. To some extent, the construction of expressway has contributed to the alleviation of urban traffic congestion. With the increasing of traffic travel, traffic congestion also appears on the expressway, and it is becoming more and more serious. In order to solve the problem of expressway traffic congestion, it is necessary to estimate the state of expressway traffic, so as to master the traffic situation of expressway and provide effective data support for the management, control and induction of expressway traffic flow. Expressway traffic state is based on the relative parameters of traffic flow to measure the approximate range of traffic state, so the estimation of traffic state parameters can be used to indirectly realize the estimation of traffic state. In essence, traffic state parameter estimation is a nonlinear, non-Gao Si problem. In view of the superiority of particle filter in dealing with this kind of problem, In this paper, the second order macroscopic traffic flow model of expressway and particle filter algorithm are combined effectively, and the traffic state parameter estimation model is established, and the accurate estimation of traffic state parameter is realized by means of MATLAB software platform. The results show that the traffic state parameter estimation model based on basic particle filter can estimate the traffic state parameters of expressway. Particle degradation will occur in the application of particle filter algorithm. The occurrence of particle degradation not only leads to the loss of particle diversity, but also concentrates most of the calculations on some particles that contribute little to the result. In order to avoid the phenomenon of particle degradation, the immune particle swarm optimization algorithm is used to optimize the particle filter, and the improved particle filter algorithm is used to verify it. The results show that the improved particle filter algorithm is compared with the basic particle filter algorithm. The improved particle filter algorithm can achieve more accurate estimation of traffic state parameters. The traffic state itself has certain fuzziness and uncertainty, which is suitable to be divided by fuzzy theory. In this study, the traffic state is divided into three types by fuzzy C-means clustering method: smooth flow, mild congestion and congestion. According to the estimation results of traffic state parameters, the traffic state estimation results are obtained by clustering them into the corresponding states. On the basis of the accurate estimation of the state of the expressway traffic, the main line of the expressway and its ramp can be effectively controlled so as to maximize the service capacity of the expressway. By combining variable speed limit control with on-ramp control and considering the characteristics of different traffic conditions, a joint expressway control model is established. Genetic algorithm is used to solve the model, and an example is given to verify the model and its application. The results show that the joint control model can effectively control the expressway.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：U491
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