Jinan Bus Network Optimization Research Based On GIS

At present, most of the cities in our country, public transportation and thedevelopment of urban economy both exist serious disconnect phenomenon, theserious lag in the latter, unable to meet the needs of residents in urban public transport.So, optimize the urban public traffic network, to make it more reasonable is verynecessary. The aim of the urban public traffic line network optimization is based onthe existing urban road system and public transportation capacity, on the basis ofreasonable layout of urban public traffic line network, maximizes the best benefit ofthe public transport system. Increasingly mature GIS technology, makes the urbanpublic traffic line network optimization design is introduced into GIS technologybecome a necessity. Public transportation passenger traffic demand forecasting is akey link in the process of urban public traffic line network optimization design is oneof the prediction result is correct or not, has a great influenceon the planning is scientific and rationality.This paper takes the jinan public transportation passenger flow demandforecasting for the specific case study, first of all, in the introduction part introducesthe research purpose of this article, research significance, research status at home andabroad, the research content and methods, etc.; Secondly, on the bus networkoptimization design and the basic theory of GIS are briefly introduced; Again, byorganizing the trip survey in jinan, bus passenger flow investigation, traffic status quoinvestigation and survey of land use and transportation planning informationcollection, relatively complete mastery of the basic data of public traffic analysis andforecasting modeling; Fourth, to analysis the characteristic of jinan city dweller,mainly including residents and residents’ public transportation characteristics of twoparts; Fifth, the paper introduces the principles and contents of the publictransportation passenger flow demand forecasting, and studies the publictransportation passenger flow demand forecasting of trip generation, trip distributionprediction, way classification prediction, such as public transportation passenger flowdistribution model.