Research On Passenger Flow Forecastmodel Of Multi-level Rail Transit In Dense City Group

Reforming and opening comes35years, the Pearl River Delta, Yangtze River Delta and Beijing Tianjin Hebei city groups which represented the highly development of economic and social has formed along with the rapid development of economy. The formation of the city groups promoted the integration of urban and rural development, blurred the concept of urban and rural areas, induced city groups radiate from the center or the Deputy Center City in the main city zone and formed a dense city group with the surrounding township.The tranffic integration is the key to realize the functions of dense city group. Breaking the administrative boundaries, allocating traffic resources in dense city groups reasonablly and constructing high quality passenger traffic system can give full play to the important role of transportation in economic development. To develop multi-level rail transit is the inevitable choice to perfect the dense city group transportation integration due to the high population density and bigger volume of traffic. While the scientific passenger flow forecast is the key to meet the high quality travel demand of residents and economic benefits of rail transit operation. Therefore, how to predict the rail passenger demand accurately has became a pilot project to control the construction and operation scale of railway and improve the service level of different modes of rail transit.This paper made analysis of the economic society, land use and traffic distribution of dense city group and defined the region, the city and the city area rail passenger flow according to the different travel origins and destinations. Corresponding forecasting methods which relying on the idea of "four stages" passenger flow forecast were proposed for different levels of passenger flow in each stage.First of all, traffic zone division principle of dense city group was studied in the passenger flow forcast and distribution stage combined with the rail transit classification, and passenger flow forcast model were researched respectively in terms of the levels of the city and the city area. Due to the reason that the spatial span of study region is much more wilder, double maximum entropy distribution model was built as a result of drawing lessons from the traffic impedance model of gravity distribution model and generalized travel cost.The two stage prediction method which include the initial division and subdivision of passenger flow was proposed based on dense city group travel features at the level of the regional passenger flow division stage. In the initial division stage, the rail transit was regarded as one kind of tranffic mode according to the resource allocation theory, then the passenger flow rate of rail transit, highway, voyage, waterway and other tranffic modes were studied. The rail transit was divided into three different modes--passenger dedicated line, intercity rail and the existing railway--in the subdivision stage, double objective optimization model based on dynamic generalized cost was built on the basis of Steinberg game theory. Dongguan-Guangzhou channel was used as an example to verify the model.At the levels of the city and the city area passenger flow division stage, factors that influence passenger flow of dense city group making travel mode choice were both specified based on the existing rail transit passenger flow prediction theories and methods. Then the NL and MD models which both were based on the pattern of low carbon were proposed on the basis of the concept of low carbon transportation to respectively predict the city and the city area traffic mode split of dense city group.In the rail transit passenger flow distribution stage, passenger transfer time was calculated based on the analysis of passenger travel characteristics of rail transit network in dense city group. The rail transit line capacity and integrated impedance which include passenger transfer time and road impedance form the constraint conditions of the double constrained orbit traffic network distribution model. Integrated hybrid intelligent algorithm according to stochastic simulation, neural network and genetic algorithm was proposed to solve the model.The paper put forward a series of methods and models which make use of the research on passenger flow forcast method and model of multi-level rail transit in dense city group. The methods and models can forecast the rail transit passenger flow of the region, the city area and the city in dense city group and can provide reference for the future construction and the operation scale of rail transit, and it is important to promote the improvement and perfection of the existing prediction methods of rail transit passenger flow.