Freight mode choice models using artificial neural networks

Civil Engineering and Environmental Systems

Volumn 16, Issue 4, 1999, Pages 267-286

  Abdelwahab, Walid ^a   Sayed, Tarek ^b  

^a ISRA UNIVERSITY   (Jordan)

^b UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Artificial neural network; Decisions; Freight mode; Transportation

Indexed keywords

BACKPROPAGATION; DECISION MAKING; DECISION SUPPORT SYSTEMS; FEEDFORWARD NEURAL NETWORKS; LEARNING ALGORITHMS; MARKETING; MATHEMATICAL MODELS; OPTIMIZATION;

BEHAVIORAL CHOICE MODELING;

FREIGHT TRANSPORTATION;

ARTIFICIAL NEURAL NETWORK; FREIGHT TRANSPORT; METHODOLOGY; TRAVEL BEHAVIOR;

UNITED STATES;

EID: 0033382729     PISSN: 10286608     EISSN: None     Source Type: Journal    
DOI: 10.1080/02630259908970267     Document Type: Article

References (15)

1. Abdelwahab, W. M. and Sargious, M. A. (1992) Modeling the demand for freight transportation - a new approach. Journal of Transport Economics and Policy, 26(1), 49-70.
2. Ben-Akiva, M. and Lerman, S. R. (1987) Discrete Choice Analysis: Theory and Application to Travel Demand. 2nd edn., Mass. Inst. of Technology, Cambridge, Mass.
3. Daganzo, C. F. (1979) Multinomial Probit: the theory and its applications to demand forecasting. Academic Press, New York.
4. Dougherry, M. S., A review of neural networks applied to transport. Transportation Research, 3C(4), 247-260.
5. Finney, D. (1971) Probit Analysis. 3rd edn., Cambridge University Press, Cambridge, England.
6. Ghareib, A. H. (1996) Evaluation of logit and probit models in mode-choice situation. ASCE Journal of Transportation Engineering, 122(4), 282-290.
7. Hausman, J. and Wise, D. (1978) A conditional probit model for qualitative choice: discrete decisions recognizing interdependence and heterogeneous preferences. Economitrica, 52, 1219-1240.
8. Jones, W. and Hoskins, J. (1987) Back-propagation. A generalized delta learning rule. Byte Magazine, October, 10-15.
9. Kaseko, M. S., Lo, Z.-P. and Ritchie, S. G. (1994) Comparison of traditional and neural classifiers for pavement-crack detection. ASCE Journal of Transportation Engineering, 120(4), 552-569.
10. McFadden, D. (1978) The theory and practice of disaggregate demand forecasting for various modes of urban transportation. In: Emerging Transportation Planning Methods, U.S. Department of Transportation, Report No. DOT-RSPA-DP8-50-78-2, Washington, DC.
11. Pant, P. D. and Balakrishnan, P. (1994) Neural network for gap acceptance at stop-controlled intersections. ASCE Journal of Transportation Engineering, 120(3), 432-446.
12. Rumelhart, D. E. and McClelland, J. (Eds.) (1986) Parallel Distributed Processing, MIT Press, Cambridge, 1.
13. Sayed, T. and Abdelwahab, W. (1998) A comparison of fuzzy and neural classifiers for road accidents analysis. ASCE Journal of Computing in Civil Engineering, 12(1), 42-47.
14. Tam, K. Y. and Kiang, M. (1990) Predicting bank failures: A neural network approach. Applied Artificial Intelligence, 4, 265-282.
15. U.S. Bureau of the Census Commodity Transportation Survey, Technical Documentation, Economic Survey Division, Transport Branch, Census of Transportation, U.S. Department of Commerce, Washington, D.C., 1977.