| In recent years, rapid development of our country city group makes each city’straffic demand increasing. However, a lot of center city and the peripheral city lacksgood transverse connection, the inner city traffic structures also exist certain defects.As a kind of large volume, convenient, comfortable, on time, the "bus" features in oneof the intercity rail transportation mode emerge as the times require.Passenger flow forecast plays an important role on each aspect and each stage ofrail transportation. Aiming at the Chang-Zhu-Tan city group rail transit, the thesisresearches passenger flow forecast, firstly, basic discussion of intercity rail transit, adetailed study of the intercity rail transit’s definition, position and characteristic,discussed the importance and main methods for passenger flow forecast. Then,analysis of Chang-Zhu-Tan Intercity Rail transit passenger flow distribution, supplyand demand characteristics and integrated traffic environment. According to theexisting administrative division and rail transit site settings, in the delineation of 26traffic area, using four stages prediction method to passenger flow forecast forChang-Zhu-Tan Intercity Rail Transit. Focus on the passenger flow generation phase,the use of the improved regression model and elastic coefficient method, from the twoprediction results to select the more reasonable results; passenger flow distributionphase, based on the land use distribution model of passenger flow generation volumedistribution prediction; way of dividing stages, with passenger apiration questionnairetraveler for all modes of transportation service level requirements and the choice oftraffic mode when the value of baseline, in the generalized cost calculation to focuson time and cost two influencing factors, established in line with the actual situationin the region of the Logit model. Finally, the passenger flow forecast results of timeestimation, won the day passenger flow, passenger flow volume, day day hourssection passenger flow, diurnal peak hour traffic key quantitative index. |
