Research On Passenger Volume Forecast Of Light Railway Line One In Changchun

With the quickly developing of the society and the economic in our country recent years, the traffic problem also becomes more serious than before. Therefore, developing the light railway transit(LRT) which own the characters of high velocity and large capacity is one of the effective ways to release the urban traffic problem.As the LRT has the characters of long route,long construction time and once finished it won't be easily changed,so before the construction we must do the passenger volume forcasting well.In this paper the author expatiates the Four Stages method and the basic theory of sensitivity analysis detailed, then takes the example of ChangChun Light Railway 1 for the sake of passenger volume forcasting and sensitivity analyse. In the process of passenger volume forcasting,the author uses the Four Stages method:takes the Origin Unit method firstly to forcast the volume of the travel generation,then uses the Frator method to forcast the travel distribution.In the process of Model split and Traffic assignment,the author uses the"Two-Way Logit"and the UE model in TransCAD separately. In the process of sensitivity analysis,the autor analyses some factors which cannot be easily controlled and quantified in qualitative way and selects some factors which can be easily qualified to do sensitivity analysis.Firstly the author sets up model between passenger volume and fare and population,and checkout the model in statistical way,then do sensitivity analysis.The results of the analysis reveals that during test-run time and early time the volume is most sensitive to the fare,the futher to the origin,the less sentive to the fare.So we can conclude that with the increase of people's income,the restriction that people select the travel pattern resulted by the fare will be less. In the process of sensitivity analysis to population, we find that passenger volume is more sensitive to population.The study content of this article is the follows:1. Analysis of the trip characters of Changchun residentsThe analysis of trip characters of Changchun residents is the foundation of passenger volume forecasting of subway line 1. The analysis concludes trip volumes, trip motivation, trip modes and the trip distribution in time and distance. At the same time, the characters of comman public transportation and the construction environment of railway is obtained. All of these works build foundation for passenger volume forecasting of Line 1.2. Trip generation forecastingAt first, we devide the research area into some traffic zones follow the common princles, and then study the traffic zones in detail with the center of railway station, which make the relationship of traffic zones volumes and railway network closer. According to the land use characters and city planning, we predict the passenger volumes by primary unit method.3. Trip distribution forecastingThe task of trip distribution forecasting is to forecast the spatial distribution of trip generation volumes and trip attraction volumes. The process is: we get the trip distribution of Changchun residents in 2008 including the 189 traffic zones according to the trip investigated data.We forecast the volumes and OD distribution of 2015using 2008 as the base year.4. Traffic modes splitCalibrating the parameters of traffic modes split model using the historica data and get the split ratio of public transportation, bicycle, taxi and walking. We use multinomial logit model in Transcad to forecast the split ratio.5. Trip assignment forecastingIn this part we build the trip assignment forecasting model and determine the prospective volme. Then we calculate the transfer volume in different stations. The trip assignment method is the SUE model in Transcad. The competition of railyway and common public transportation is affected by the travel time, transferring time and ticket price.6. Sensitive analysisIn the process of sensitive analysis, we make some qualitative describe in some factors that are difficult to control. In this paper, we select the ticket price and population number as the sensitive factors and the analysis also be obtained for the calculating results.