Sage Journals: Discover world-class research

Abstract

To forecast the induced passenger flow of intercity high-speed rail, two related multinomial logit models are applied to describe travelers’ travel choice behavior under changes in the travel environment. The first model describes different choices of travel mode by travelers between origin–destination pairs, while the second model describes the choice of travel frequency by different travelers. By introducing the consumption surplus variable of travelers, the two selected models are correlated and the model is calibrated using data from a behavioral survey and an intention survey. The results show that the polynomial logit model is applicable for forecasting induced passenger flow after changes in the travel environment. The consumption surplus of travelers is the key factor influencing changes in travel frequency, and the social characteristics of travelers and the economic development features of origin and destination cities have significant impacts on travel frequency. In-transit time is the key factor affecting the demand elasticity of travel frequency. The range of demand elasticity for short-distance business and non-business travel is between −0.61 and −0.79. The results of the demand elasticity analysis were applied to predict that the travel frequency of people from Nanjing to Huai’an, Suqian, Lianyungang, and Yancheng will increase by about 45% after the opening of the Nanjing-Huai’an intercity high-speed railway.

Keywords

transportation demand forecasting choice models mode choice trip generation modeling passenger rail transportation

Get full access to this article

View all access options for this article.

References

Wardman

Demand for Rail Travel and the Effects of External Factors. Transportation Research Part A, Vol. 42, No. 1, 2006, pp. 129–148.

King

Economic Significance of High Speed Rail. ITS Working Paper Series 1991–2000. CD-ROM, Institute of Transport Studies, Faculty of Economics & Business, The University of Sydney, Australia, 1996.

Cascetta

Coppola

New High-Speed Rail (HSR) Lines and Market Competition: Short Term Effects on Services and Demand in Italy. Transportation Research Record: Journal of the Transportation Research Board, 2015. 2475: 8–15.

Standing Advisory Committee on Trunk Road Assessment (SACTRA). Trunk Roads and the Generation of Traffic. Conducted for the Department of Transport, London, UK, 1994.

Hausen

Huang

Road Supply and Traffic in California Urban Areas. Transportation Research Part A, Vol. 31, No. 3, 1997, pp. 205–218.

Noland

R. B.

Relationships Between Highway Capacity and Induced Vehicle Travel. Transportation Research Part A, Vol. 35, No. 1, 2001, pp. 47–72.

Hymel

K. M.

Small

K. A.

Dender

K. V.

Induced Demand and Rebound Effects in Road Transport. Transportation Research Part B, Vol. 44, No. 10, 2010, pp. 1220–1241.

Induced Motor Vehicle Travel From Improved Fuel Efficiency and Road Expansion. Energy Policy, Vol. 39, No. 11, 2011, pp. 7257–7264.

Ponnu

Reddy

Mishra

Arkatkar

S. S.

Sarkar

A. K.

Effect of Induced Traffic on Feasibility of Highway Projects Using Economic Analysis: A Case Study in India. Case Studies on Transport Policy, Vol. 2015, No. 3, 2015, pp. 347–354.

10.

Wang

D. H.

Z. W.

Prediction Method of Highway Induced Passenger Flow Volume Adapting Changchun-Jilin High Speed Railway Project. Journal of Jilin University (Engineering and Technology Edition), Vol. 40, No. 6, 2010, pp. 1518–1522.

11.

Yao

Research on the Forming Mechanisms and Technical Prediction Framework of the IPF of HSR. Journal of Railway Engineering Society, Vol. 185, No. 2, 2014, pp. 1–6.

12.

Zhong

Chen

Shao

Traffic Volume Prediction Method of Railway Passenger Dedicated Line at Initial Operation Stage. Journal of Chongqing Jiaotong University (Nature science), Vol. 37, No. 9, 2018, pp. 107–112.

13.

Yao

Morikawa

A Study of an Integrated Intercity Travel Demand Model. Transportation Research Part A, Vol. 39, No. 1, 2005, pp. 367–381.

14.

Cascetta

Coppola

High-Speed Rail (HSR) Induced Demand Models. Procedia: Social and Behavioral Sciences, Vol. 111, No. 2014, 2014, pp. 147–156.

15.

Gutiérrez

M. D.

Andersen

S. N.

Nilsen

Ø. L.

Generated and Induced Traffic Demand: Empirical Evidence From a Fixed Link Toll Removal in Norway. Case Studies on Transport Policy, Vol. 7, No. 2019, 2019, pp. 57–63.

16.

Hills

P. J.

What Is Induced Traffic?

Transportation, Vol. 23, No. 1, 1996, pp. 5–16.

17.

Litman

Generated Traffic and Induced Travel. The Journal of Institute of Transportation Engineers, Vol. 71, No. 4, 2001, pp. 38–47.

18.

Lawrence

Bullock

Liu

International Developmnet in Focus China’s High-Speed Rail Development. International Bank for Reconstruction and Development/The World Bank, Washington, D.C., 2019.

19.

Train

K. E.

Discrete Choice Methods With Simulation. Cambridge University Press, Cambridge, 2009.

20.

Sarrias

Daziano

Multinomial Logit Models With Continuous and Discrete Individual Heterogeneity in R: The GMNL Package. Journal of Statistical Software, Vol. 79, No. 2, 2017, pp. 1–46.

21.

Hensher

D. A.

Rose

J. M.

Greene

W. H.

Applied Choice Analysis (Second Edition). Cambridge University Press, Cambridge, 2015.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB

Study on Induced Passenger Flow Forecast for Intercity High-Speed Rail

Abstract

Keywords

Get full access to this article

References

Supplementary Material