An optimal investment scheduling framework for intelligent transportation systems architecture

Journal of Intelligent Transportation Systems: Technology, Planning, and Operations

Volumn 15, Issue 3, 2011, Pages 115-132

  Zandi, Faramak ^a   Tavana, Madjid ^b  

^a ALZAHRA UNIVERSITY   (Iran)

^b LA SALLE UNIVERSITY   (United States)

Author keywords

Fuzzy Real Option Analysis; Group Multi Objective Decision Making; Intelligent Transportation System Architecture; Multi Criteria Decision Analysis

Indexed keywords

CRISP DATA; DECISION MAKING MODELS; DECISION PROCESS; FUZZY GROUPS; FUZZY RISK; FUZZY TOPSIS; INTELLIGENT TRANSPORTATION SYSTEMS; INVESTMENT EVALUATION; ITS ARCHITECTURE; LINGUISTIC VARIABLE; MULTI OBJECTIVE; MULTI OBJECTIVE DECISION MAKING; MULTI-CRITERIA; MULTI-CRITERIA DECISION ANALYSIS; OBJECTIVE FUNCTIONS; OPTIMAL INVESTMENTS; OPTION VALUE; QUANTITATIVE CRITERIA; REAL OPTION ANALYSIS; REAL OPTIONS; REAL-WORLD PROBLEM; SCHEDULING FRAMEWORKS; SELECTION METHODS; SELECTION PROCESS; TRANSPORTATION PLANNING; VALUE JUDGMENT;

ARCHITECTURE; DECISION MAKING; DECISION THEORY; ECONOMIC ANALYSIS; FUZZY SETS; INTELLIGENT SYSTEMS; MULTIOBJECTIVE OPTIMIZATION; TRANSPORTATION; VEHICLE LOCATING SYSTEMS;

INVESTMENTS;

DATA SET; DECISION MAKING; ECONOMIC ANALYSIS; FUZZY MATHEMATICS; LITERATURE REVIEW; NUMERICAL MODEL; QUALITATIVE ANALYSIS; QUANTITATIVE ANALYSIS; RISK FACTOR; TRANSPORTATION DEVELOPMENT; TRANSPORTATION INFRASTRUCTURE; TRANSPORTATION PLANNING;

EID: 80051771784     PISSN: 15472450     EISSN: 15472442     Source Type: Journal    
DOI: 10.1080/15472450.2011.594669     Document Type: Article

References (50)

1. Belton,V., and Stewart, T. J. (2002). Multiple criteria decision analysis: An integrated approach. Boston, Massachusetts: Kluwer.
2. Benaroch,M., and Kauffman, R. J. (1999). A case for real options pricing analysis to evaluate information technology project investments, Information Systems Research, 10(1), 70-86.
3. Black, F., and Scholes,M. (1973). The pricing of options and corporate liabilities. The Journal of Political Economy, 81, 637-654.
4. Black, F., and Scholes, M. (1974). The effects of dividend yield and dividend policy on common stock prices and returns. Journal of Financial Economics, 1(1), 1-22.
5. Boyle, P. P. (1977). Options: A Monte Carlo approach. Journal of Financial Economics, 4, 323-338.
6. Bristow, A. L., Pearman, A. D., and Shires, J. D. (1997). An assessment of advanced transport telematics evaluation procedures. Transport Reviews, 17, 177-205. (Pubitemid 127661120)
7. Busby, J. S., and Pitts, C. G. C. (1997). Real options and capital investment decisions. Management Accounting, 75(10), 38-39.
8. Carlsson, C., and Fullér, R. (2003). A fuzzy approach to real option valuation. Fuzzy Sets and Systems, 139, 297-312.
9. Collan, M., Fullér, R., andMezei, J. (2009).Afuzzy pay-offmethod for real option valuation. Journal of Applied Mathematics and Decision Sciences, 2009, 1-14.
10. Cox, J., Ross, S., and Rubinstein,M. (1979). Option pricing: A simplified approach. Journal of Financial Economics, 7, 229-263.
11. Crainic, T. G., Gendreau, M., and Potvin, J. Y. (2009). Intelligent freight-transportation systems: Assessment and the contribution of operations research. Transportation Research Part C: Emerging Technologies, 17, 541-557.
12. Datar, V., and Mathews, S. (2004). European real options: An intuitive algorithm for the Black Scholes formula. Journal of Applied Finance, 14(1), 45-51.
13. Datar, V., and Mathews, S. (2007). A practical method for valuing real options: The Boeing approach. Journal of Applied Corporate Finance, 19, 95-104.
14. De Brucker, K.,Verbeke,A., andMacharis,C. (2004). The applicability of multicriteria analysis to the evaluation of intelligent transportation systems (ITS). Research in Transportation Economics, 8, 151-179. (Pubitemid 39752963)
15. De Corla-Souza, P., Everett, J., Gardner, B., and Culp,M. (1997). Total cost analysis: An alternative to benefit-cost analysis in evaluating transportation alternatives. Transportation, 24, 107-123.
16. De Neufville, R. (2003). Real options: Dealing with uncertainty in systems planning and design. Integrated Assessment, 4(1), 26-34.
17. Dos Santos, B. L. (1994). Assessing the value of strategic information technology investments. London, England: Chapman & Hall.
18. Drakopoulos, J. A. (1995). Probabilities, possibilities, and fuzzy sets. Fuzzy Sets and Systems, 75(1), 1-15.
19. Gillen, D., and Levinson, D. (2004). Assessing the investment in ITS: An introduction. In D. Gillen and D. Levinson (Eds.), Assessing the benefits and costs of ITS: Making the business case for ITS investments (pp. 1-16). Boston, MA: Kluwer.
20. Haynes, K. E., and Li, M. (2004). Analytical alternatives in intelligent transportation system (ITS) evaluation. Research in Transportation Economics, 8, 127-149. (Pubitemid 39752962)
21. Hwang, C. L., and Yoon, K. (1981). Multiple attribute decision making methods and applications. Berlin, Germany: Springer.
22. Hodota, K. (2006). R & D and deployment valuation of intelligent transportation systems: A case example of the intersection collision avoidance systems (Master's thesis).Massachusetts Institute of Technology, Cambridge, MA.
23. Iniestra, J. G., and Gutiérrez, J. G. (2009). Multicriteria decisions on interdependent infrastructure transportation projects using an evolutionary-based framework. Applied Soft Computing, 9, 512-526.
24. Kim, S. H., and Ahn, B. S. (1999). Interactive group decision making procedure under incomplete information. European Journal of Operational Research, 116, 498-507.
25. Kim, G., Park, C. S., and Yoon, K. P. (1997). Identifying investment opportunities for advanced manufacturing systems with comparativeintegrated performance measurement. International Journal of Production Economics, 50, 23-33. (Pubitemid 127382591)
26. Leviakangas, P. (1998, October). Some new aspects and ideas for economic evaluation of ITS investments. Paper presented at the 5th World Congress on Intelligent Transport Systems, Seoul, South Korea.
27. Leviakangas, P., and Lahesmaa, J. (2002). Profitability evaluation of intelligent transportation system investments. Journal of Transportation Engineering, 128, 276-286. (Pubitemid 34500237)
28. Levine, J., and Underwood, S. E. (1996). A multiattribute analysis of goals for intelligent transportation system planning. Transportation Research Part C: Emerging Technologies, 4, 97-111.
29. Ma,Y., Zhou,Y., Chowdhury, M.,Wang, K.-C., and Fries, R.N. (2009). A framework for performance evaluation of communication alternatives for intelligent transportation systems. Journal of Intelligent Transportation Systems, 13, 111-126.
30. Macharis, C., Verbeke, A., and De Brucker, K. (2004). The strategic evaluation of new technologies through multicriteria analysis: The advisors case. Research in Transportation Economics, 8, 443-446.
31. Mathews, S., and Salmon, J. (2007). Business engineering: A practical approach to valuing high-risk, high-return projects using real options. In P. Gray (Ed.), Tutorials in operations research (pp. 157-175). Hanover, MD: INFORMS.
32. McConnell, J. (2005). Lecture notes from MIT 1.212: An introduction to intelligent transportation systems. Boston, Massachusetts: MIT Open Courseware.
33. McGrath, R. G. (1997). A real options logic for initiating technology positioning investments. Academy of Management Review, 22, 974-996.
34. Poyhonen, M. A., Hamalainen, R. P., and Salo, A. A. (1997). An experiment on the numerical modelling of verbal ratio statements. Journal of Multi-Criteria Decision Analysis, 6(1), 1-10. (Pubitemid 127017241)
35. Quek, C., Pasquier, M., and Lim, B. (2009). A novel self-organizing fuzzy rule-based system for modelling traffic flow behavior. Expert Systems With Applications, 36, 12167-12178.
36. Riabacke, M., Danielson, M., Ekenberg, L., and Larsson, A. (2009). A prescriptive approach for eliciting imprecise weight statements in an MCDA process. In F. Rossi and A. Tsoukias (Eds.), Algorithmic decision theory (pp. 168-179). Berlin, Germany: Springer-Verlag.
37. Salo, A. A. (1995). Interactive decision aiding for group decision support. European Journal of Operational Research, 84(1), 134-149.
38. Scarso, E. (1996). Timing the adoption of a newtechnology: An optionbased approach. Management Decision, 34(3), 41-48. (Pubitemid 126058500)
39. Schniederjans,M. J., Hamaker, J. L., and Schniederjans, A.M. (2004). Information technology investment: Decision-making methodology. Singapore: World Scientific Organization.
40. Shaheen, S. A., and Finson, R. (2004). Intelligent transportation systems. In C. Cleveland (Eds.), Encyclopedia of energy (p. 487). Amsterdam, Netherlands: Elsevier.
41. Shih,H. S., Shyur,H. J., and Lee, E. S. (2007).An extension of TOPSIS for group decision making. Mathematical and Computer Modelling, 45, 801-813. (Pubitemid 44937544)
42. Sussman, J. M. (2005). Perspectives on intelligent transportation systems (ITS). New York, NY: Springer.
43. Thill, J. C., Rogova, G., and Yan, J. (2004). Evaluating benefits and costs of intelligent transportation systems elements from a planning perspective. Research in Transportation Economics, 8, 571-603. (Pubitemid 39752980)
44. U.S. Department of Transportation. (2009). National intelligent transportation system (ITS) architecture. Retrieved from http://www.standards. its.dot.gov/learn-NatITS.asp
45. Valls, A., and Torra, V. (2000). Using classification as an aggregation tool in MCDM. Fuzzy Sets and Systems, 15(1), 159-168.
46. Wang, J. G., Lin, C. J., and Chen, S. M. (2010). Applying fuzzy method to vision-based lane detection and departure warning system. Expert Systems With Applications, 37(1), 113-126.
47. Yang, J. B., and Xu, D. L. (2002). On the evidential reasoning algorithm for multiattribute decision analysis under uncertainty. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans, 32, 289-304.
48. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.
49. Zadeh, L. A. (1983). The role of fuzzy logic in the management of uncertainty in expert systems. Fuzzy Sets and Systems, 11, 197-198.
50. Zeleny,M. (1974). A concept of compromise solutions and the method of the displaced ideal. Computers and Operations Research, 1, 479-496.