Study On Urban Road Transportation Network Design And Traffic Demand Management Under Uncertainty

With the rapid development of national economy and the deepening of urbanization,urban traffic demand has risen sharply,and traffic congestion has become more and more serious.A series of urban problems caused by traffic congestion have become important factors restricting the sustainable development of cities.The essential reason of urban traffic congestion problem is the imbalance of traffic supply and demand.Therefore,alleviating urban traffic congestion often needs to play a regulating role from both the supply and demand sides.Only on the basis of moderately increasing the supply of road resources(achieved by urban road transportation network design),while strengthening the traffic demand management and optimizing the management scheme can ensure the effectiveness of road resource supply and the rationality of travel demand.At the same time,the uncertain factors in the urban transportation system have an important impact on urban road transportation network design and traffic demand management.Ignoring these uncertainties may result in unreasonable transport planning and management,and then make imperfect travel decisions for travelers,even road network construction management creates immense risk.In view of this,this thesis intends to conduct integrated study on urban road transportation network design and traffic demand management on the basis of fully considering the impact of uncertain factors in the urban road transportation system,in order to further enrich and improve the current urban transport planning and management theory,and provide theoretical support and scientific basis for traffic operators and departments to formulate relevant policies.Firstly,this thesis proposes a stochastic bi-level programming model for urban road transportation network design with uncertain Origin-Destination(O-D)demand,and designs a solution algorithm based on back-propagation(BP)neural network.After presenting the solution algorithm,the numerical experiments on two networks of different sizes are illustrated.On the one hand,this study selects the nine-node network for numerical experiments,and solves the problem separately by BP neural network method and Monte Carlo simulation method,and compares and analyzes the predicted output results,which verifies the feasibility of the BP neural network algorithm.On the other hand,the Sioux Falls network is used as an example to verify the application of the BP neural network method in a large-scale transportation network.The results show that when the training sample size is sufficient,the BP neural network method can obtain a better distribution of the total system travel time,and can also yield a more reasonable and narrower prediction interval than the traditional Monte Carlo simulation calculation.When the O-D traffic demand obeys uniform distribution,normal distribution and lognormal distribution respectively,the total travel time distribution of the system is relatively similar,close to the normal distribution.Secondly,this thesis establishes a stochastic bi-level programming model to describe the urban road transportation network design problem under the uncertainty of supply and demand,and adopts local sensitivity analysis with interaction(LSAI)that considers the interaction to decompose the uncertain factors into uncertainty in supply and uncertainty in demand,and uncertainty generated by the interaction between the two,so as to quantitatively analyze the impact of each uncertainty factor on the urban road transportation network.During the numerical experiments,small and medium-scale transportation networks(Nine-node network and Sioux Falls network)are adopted to quantitatively analyze the influence of each uncertain factor on network performance(total system travel time,road traffic flow,etc.).It is found that supply uncertainty,demand uncertainty,and the interaction between supply and demand factors all have an impact on network performance such as total system travel time.And the interaction between stochastic supply and stochastic demand may amplify or reduce the impact of individual uncertainty factors on total system travel time due to parameters such as transportation network size,travel demand between O-D pair and capacity for links.Therefore,when planning and managing the transportation network,the dual impact of supply uncertainty and demand uncertainty should be fully considered.Finally,this thesis studies the integration of urban road transportation network design and traffic demand management by means of tradable credit scheme under uncertain conditions.Taking account of the uncertainty of supply and demand,starting from the two aspects of road capacity improvement and traffic demand management,the stochastic bilevel integrated model from both supply and demand sides under uncertain conditions is proposed.The nine-node network is adopted for numerical experiments,and the integrated models under double uncertainty or under single uncertainty are solved based on the expected value model solution algorithm and the relaxation algorithm,combined with the BP neural network solution method proposed in Chapter 3.Numerical experiments verify the feasibility and effectiveness of the proposed integrated model,and study the changes and impacts of uncertain conditions of the transportation network(the uncertainty of supply and demand)on management schemes,so as to provide reference for planning decision makers to formulate more realistic transportation planning and management schemes.