Theoretical Analysis And Empirical Study On Optimization For Urban Road Traffic Network

In recent years, with economic development and urban population increase, many cities in China, particularly the major cities and provincial cities, traffic congestion is becoming increasingly serious, give priority to developing urban public transport system to become sustainable and harmonious at the inevitable choice. As city traffic study in the field of hot spots, transportation network optimization is being more and more attention. The contradiction between supply and demand of the traffic is prominently shown on the uneven development of the road. Have a long history in the large and medium-sized cities, to carry out the transformation of roads little room for the urban area can provide traffic supply is very limited. With the ever-increasing vehicle traffic, the road has been alone can not effectively change the vicious cycle of the tension between supply-demand, it lies in a scientific and rational planning, optimizing the use of urban transport and road networks.In this paper, we summarized the research status of optimization for urban traffic network at home and abroad. By introducing the relevant model of optimization for urban traffic network, the multiple objects optimal model for network planning is deduced by using the traditional model of network planning. And we solve the model of optimization of multi-objective network with the genetic algorithm in this study, which provides a new way for the multi-objective network. Namely, we put forward a new solution to the model of optimization of stability multi-objective traffic network and the multi-objective algorithm of this model. At last, we use the above results to analysis the reform of public transportation network in Nanchang city. According to the empirical study, the difference between theoretical model and practical application of it provides a theoretical base for the improvements on optimization of the future public transportation. The main results of this paper are as follows: (1) Unattainalbe global optimum, only placing the extra emphasis on local traffic-network, and the scheme is not very precise are three weak points of traditional traffic network design and optimization. Taking into consideration the above weak points, we proposed the improved optimum model of multi-objective network under long-term uncertainty by introducing uncertainty and multi-objective into optimization of stability network. Results of the improved model may agree better with the practical situation better in contrast to traditional model, which provides a good reference for the future investment decision of the network reform. (2) We introduced basic structure, research overview and overall classification of multi-objective optimization algorithm and proposed the key problem of it. Because the traditional approach to solving network optimization is too complicated, we tried to solve the optimum model of multi-objective stability network in order to make for the above weak point by introducing a genetic algorithm and the improved differential evolution algorithm. According to the numerical experiment and comparing to other algorithm, we have the conclusions about the improved differential evolution algorithm as follows: it has better robustness; with regard to the high-dimensional test problem, it could convergence to the overall optimal point with very high reliability; what's more, from the standpoint of optimization of single-object continuous real domain problem, it has better calculated performance than genetic algorithm. (3) We investigated and analyzed the current situation of traffic of Nanchang city (including resident trip, vehicle trip, public traffic, urban transport facilities and facility management, municipal traffic management, research of traffic of the roads, and situation of municipal traffic safety, etc.). The above proposed model was checked by using renovation project of two analog and one real network. We checked the validity of the algorithm when using the above two analog network. The results show that the proposed optimum model of multi-objective stability network has some improvement in comparison with traditional network optimization. Meanwhile, we calculated the relevant problems of the reform of public traffic network of Nanchang city by using this model, and the results indicate that our model has considerable suitability and stability to the real network. At last, we took the historical datas of Nanchang city accumulated by Department of Motor Vehicles (DMV) for example, and tested the proposed assumption through the using of Generalized Method of Moments when using optimum model of multi-objective stability network. The results basically support the testing result proposed in this paper, namely, the optimum model of multi-objective network has some improvement in stability and reform cost of the network.