Regression model for daily passenger volume of high-speed railway line under capacity constraint

Abstract

A non-linear regression model is proposed to forecast the aggregated passenger volume of Beijing–Shanghai high-speed railway (HSR) line in China. Train services and temporal features of passenger volume are studied to have a prior knowledge about this high-speed railway line. Then, based on a theoretical curve that depicts the relationship among passenger demand, transportation capacity and passenger volume, a non-linear regression model is established with consideration of the effect of capacity constraint. Through experiments, it is found that the proposed model can perform better in both forecasting accuracy and stability compared with linear regression models and back-propagation neural networks. In addition to the forecasting ability, with a definite formation, the proposed model can be further used to forecast the effects of train planning policies.

Keywords

high-speed rail / Jinghu high-speed railway (HSR) / demand / capacity / forecasting

References

[1] BaoY, LiuJ, MaM-s, MengL-y. Seat inventory control methods for Chinese passenger railways [J]. Journal of Central South University, 2014, 21(4): 1672-1682

[2] HallP, BanisterD. The second railway age [J]. Built Environment, 1993, 19: 157-162

[3] GivoniM. Development and impact of the modern high-speed train: A review [J]. Transport Reviews, 2006, 26(5): 593-611

[4] CamposJ d, RusG. Some stylized factors about high-speed rail: A review of HSR experiences around the world [J]. Transport Policy, 2009, 16: 19-28

[5] VlahogianniE I, GoliasJ C, KarlaftisM G. Short-term traffic forecasting: Overview of objectives and methods [J]. Transport Reviews, 2004, 24(5): 533-557

[6] SmithB L, WilliamsB M, OswaldR K. Comparison of parametric and nonparametric models for traffic flow forecasting [J]. Transportation Research Part C, 2002, 10: 303-321

[7] DudduV R, PulugurthaS S. Principle of demographic gravitation to estimate annual average daily traffic: Comparison of statistical and neural network models [J]. Journal of Transportation Engineering, 2013, 139: 585-595

[8] TsengF M, YuH C, TzengG H. Combining neural network model with seasonal time series ARIMA model [J]. Technological Forecasting and Social Change, 2002, 69(1): 71-87

[9] ArmstrongJ SPrinciples of forecasting: A handbook for researchers and practitioners [M], 2001NorwellKluwer Academic Publishers

[10] GardnerE S. Exponential smoothing: the state of the art-Part II [J]. International Journal of Forecasting, 2006, 22: 637-666

[11] DuncansonA VShort-term forecasting [C], 1974

[12] LeeS, FambroD B. Application of subset autoregressive integrated moving average model for short-term freeway traffic volume forecasting [J]. Transportation Research Board, 1999, 1678: 179-188

[13] MakridakisS, AndersenA, CarboneR. The accuracy of extrapolation (time series) methods: Results of a forecasting competition [J]. Journal of Forecasting, 1982, 1: 111-153

[14] WickhamR REvaluation of forecasting techniques for short-term demand of air transportation [D], 1995MITFlight Transportation Lab

[15] LawR, AuN. A neural network model to forecast Japanese demand for travel to Hong Kong [J]. Tourism Management, 1999, 20: 89-97

[16] ChiJ, BaekJ. A dynamic demand analysis of the United States air-passenger service [J]. Transportation Research Part E, 2012, 48: 755-761

[17] WardmanM. Demand for rail travel and the effects of external factors [J]. Transportation Research Part E, 2006, 42: 129-148

[18] BatleyR, DargayJ, WardmanM. The impact of lateness and reliability on passenger rail demand [J]. Transportation Research Part E, 2011, 47: 61-72

[19] WeiY, ChenM C. Forecasting the short-term metro passenger flow with empirical mode decomposition and neural network [J]. Transportation Research Part C, 2012, 21: 48-162

[20] VulcanoG, van RyzinG J. Estimating primary demand for substitutable products from sales transaction data [J]. Operations Research, 2012, 60(2): 313-334

[21] JiangX-b. Thoughts on passenger flow principle and marketing strategies of Beijing-Shanghai high-speed railway [J]. Railway Transport and Economy, 2014, 36(2): 48-51

[22] TsaiT H, LeeC K, WeiC H. Neural network based temporal feature models for short-term railway passenger demand forecasting [J]. Expert Systems with Applications, 2009, 36: 3728-3736

[23] LeeC K, LinT D, LinC H. Pattern analysis on the booking curve of an inter-city railway [J]. Journal of East Asia Society for Transportation Studies, 2005, 6: 303-317

[24] SeberG A F, WildC JNonlinear regression [M], 2003Hoboken, NJWiley-Interscience

[25] DraperN R, SmithHApplied regression analysis [M], 1998Hoboken, NJWiley-Interscience

[26] ZhangG, PatuwoB E, HuM Y. Forecasting with artificial neural network: The state of the art [J]. International Journal of Forecasting, 1998, 14: 35-62