Welcome to Francis Academic Press

International Journal of New Developments in Engineering and Society, 2017, 1(2); doi: 10.25236/IJNDES.17220.

Research on the Demand Forecast of Rail Transit and Intercity Railway Passenger Flow based on Improved Logit Model

Author(s)

Cong Zhou

Corresponding Author:
Cong Zhou
Affiliation(s)

Transport College, Beijing Transportation University,  Beijing, 100044

Abstract

This paper analyzes the connotation of urban integrated passenger transport hub from the perspective of traffic engineering, analyzes the characteristics of urban integrated passenger transport hub, divides the types of integrated passenger transport hubs, and analyzes the types of hubs used by different types of cities. The passenger demand, the willingness and the ability to purchase the passenger service needs as the premise, define the urban comprehensive passenger terminal passenger demand research scope, namely the hub external passenger demand, the hub demand and the hub parking demand three aspects. Based on the survey of passenger transport demand and passenger characteristics and the data collection, this paper analyzes the formation mechanism and selection rule of passenger preference, introduces the concept of passenger dependency on the characteristics of passenger service, quantifies the passenger willingness, The mapping of passenger service characteristics. The probability of distribution of different passenger services is subject to a number of logit probability distribution models as assumptions to solve the passenger willingness and dependency of different passenger services.

Keywords

Logit Model, Railway and Intercity Passenger, Demand Forecast

Cite This Paper

Cong Zhou.Research on the Demand Forecast of Rail Transit and Intercity Railway Passenger Flow based on Improved Logit Model.  International Journal of New Developments in Engineering and Society (2017) Vol.1, Num.2: 81-84.

References

[1]Sun Bin, Yao Haitao. Short-term wind speed prediction based on PSO optimized LSSVM [J]. Power System Protection and Control. 2012 (05)
[2]Liu Yan, Qin Huanmei, Pan Xiaosong, Guan Hongzhi, Ao Xianglong. Investigation and Analysis of Parking Demand in Beijing [J]. Journal of Transportation Engineering and Information. 2011 (03)
[3]He Dahai, CHANG Yun-tao. Study on the Planning of Changchun Station Integrated Transportation and Transfer Hub [J]. Transportation World (Transportation. Vehicles). 2011 (08)
[4]Liang Yulin, Wu Ping, Liu Yi. Multi-information fusion algorithm based on GA-LSSVM [J]. Information and Electronic Engineering. 2010 (06)
[5]Wang Jiangtao, Ma Si. Selection of Characteristic Variables of MNL Model for Forecasting Passenger Deduction Rate [J]. Journal of Chongqing Jiaotong University (Natural Science Edition) 2010 (06)
[6]Long Donghua, Shao Yiming, Xiang Hongyan. Prediction model and application of parking demand forecast based on neural network [J]. Journal of Transportation Information and Security. 2010 (05)