Computational Intelligence And Study On Its Application To Urban Traffic Guidance System

With the increasing development of social economy and urbanization, urban population and vehicles increase rapidly. Traffic congestion has become a prevalent problem for metropolis all over the world. Traffic accident, energy wasting, and air pollution resulted from traffic congestion not only seriously restrict the sustainable development of social economy, but also severely influence the urban living environment. So adopting intelligent transportation system (ITS) technology to solve these problems has become an important direction in the future transportation engineering development.Traffic guidance system is an important subsystem of ITS, which can lead vehicles to move in road network effectively, reduce driving time, and finally realize that the traffic volume distributes equably in the whole road network. Applying the newest research of science and technology to urban traffic guidance system can not only improve transportation safety, production efficiency and revenues, but also connect with land resource and energy exploitation, environment improvement, which is of the most important significance to meet social demand, accelerating the progress of nation and society, and driving subject development.Urban road traffic system, which integrates human, vehicles, roads, environment and other complex factors, is of high complexity, time-dependence and randomicity. It is difficult for traditional control methods based on precise mathematical models to solve complex modern urban traffic problems. And computational intelligence (CI) is a computing methodology from nature, which simulates and researches the intelligent behavior from the lowest level of the creature. CI develops the traditional style of computation without establishing complicated mathematical models, which has many advantages, such as intelligence, parallel processing and self-adaptive ability. Thus it provides a fruitful approach to solve complex problems.Stemming from the key project——"Research on Dynamic Congestion Warning and Evanesce Decision-making for Urban Traffic Network", funded by Natural Science Foundation Project of CQ CSTC under Grant 2006BA6016,on the basis of reviewing the existing outcome in this field, this dissertation introduces computing intelligence theory to make a comprehensive and deep research on several important problems from urban traffic guidance system. The main original points of this paper lie below:①Regression accuracy and generalization performance of support vector regression (SVR) models depend on proper setting of its parameters. An optimal selection approach of SVR parameters is proposed based on catastrophic FS algorithm and applied on traffic flow forecasting. The decision mechanism of individual initial position is improved through introducing cusp-catastrophe strategy to reduce reliability on search radius, and to extend the area of feasible solutions and enhance the global search ability. Through a rolling forecasting simulation experiment on real traffic volumes, the experimental results show that the proposed method is feasible and effective for the optimal selection of SVR parameters, and has better generalized performance and prediction accuracy than rule of thumb.②Due to the disadvantage of local optimum of basic ant colony algorithm, chaotic selection strategy are proposed to improve ant colony algorithm, and applied in optimal route of urban road network. Chaos perturbation is used to improve selection strategy to avoid precocity and stagnation. The road network of Chongqing Yuzhong Peninsula is taken as an example to calculate the optimal route based on the least travel time, and the experimental results show that this algorithm has much higher capacity of global optimization than basic ant colony algorithm and it is feasible and effective for optimal route choice.③Due to the disadvantage of slow convergence and local optimum of particle swarm algorithm, introducing relative distances among particles to improve probability selection formula, an improved particle swarm optimization with immune mechanism is proposed. Particles update their velocity and position not only by individual and global optima, but also by individual optima of a specific particle selected by roulette method according to certain probability, to keep the variety of the population and avoid precocity and stagnation. This algorithm is applied to solve the maximum entropy model, estimating OD matrix from traffic link flows. Through a test on a specific crossroad in Chongqing City, the experimental results show that particle swarm algorithm is feasible and effective for OD matrix estimation, overcomes the shortcoming of Newton's method that strictly depends on initial values, and the particle swarm algorithm has much higher capacity of optimization than basic particle swarm algorithm and basic genetic algorithm.④Due to uncertainty of urban traffic state, and initialization sensitivity and local search of fuzzy c means (FCM), a new FCM algorithm (SFLA-FCM) based on shuffled frog leaping algorithm (SFLA) is proposed and applied on study about urban traffic state. SFLA is a new recta-heuristic population evolutionary algorithm and it has fast calculation speed and excellent global search capability. SFLA-FCM uses SFLA to replace the iteration process of FCM based on the gradient descent and avoids the disadvantages of local optimality and initialization dependence. The experimental results show that the proposed method is more accurate and efficient than FCM and it is feasible and effective for traffic state identification.In short, an in-depth study for urban traffic guidance system is carried out comprehensively by computing intelligence theory and method, which has great significance of solving urban traffic prolems furtherly.