The design of real-time logistics information system for trucking industry

Computers and Operations Research

Volumn 35, Issue 11, 2008, Pages 3439-3451

  Lo, Shih Che ^a   Hall, Randolph W ^b  

^a NATIONAL TAIWAN UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Taiwan)

^b University of Southern California   (United States)

Author keywords

Fuzzy theory; Logistics information system; Loop detectors; Travel time forecasting; Truck scheduling

Indexed keywords

AIRPORTS; FUZZY CLUSTERING; REAL TIME SYSTEMS; SYSTEMS ANALYSIS; TIME SERIES ANALYSIS; TRAFFIC CONGESTION; TRUCKS;

AIRPORT TERMINALS; FUZZY THEORY; TRUCKING INDUSTRY;

INFORMATION SYSTEMS;

EID: 41149154541     PISSN: 03050548     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cor.2007.01.023     Document Type: Article

References (20)

1. Schrank D., and Lomax T. The 2001 urban mobility report (2001), Texas Transportation Institute
2. Hall R.W. Truck scheduling for ground to air connectivity. Journal of Air Transport Management 7 (2001) 331-338
3. Lighthill M.J., and Whitham G.B. On kinematic waves. I. Flood movement in long rivers. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences 1178 229 (1955) 281-316
4. Lighthill M.J., and Whitham G.B. On kinematic waves. II. A theory of traffic flow on long crowded roads. Proceedings of the Royal Society of London, Series A, Mathematical and Physical Sciences 1178 229 (1955) 317-345
5. Newell G.F. A simplified theory of kinematic waves in highway traffic, part I: general theory. Transportation Research, Part B: Methodological 27 4 (1993) 281-287
6. Newell G.F. A simplified theory of kinematic waves in highway traffic, part II: queueing at freeway bottlenecks. Transportation Research, Part B: Methodological 27 4 (1993) 289-303
7. Daganzo C.F. The cell transmission model: a dynamic representation of highway traffic consistent with the hydrodynamic theory. Transportation Research, Part B: Methodological 28 4 (1994) 269-287
8. Daganzo C.F. The cell transmission model, part II: network traffic. Transportation Research, Part B: Methodological 29 2 (1995) 79-93
9. Matsumura S, Yamashita H, Iwaki S, Sugimura H. Experimental verification of travel-time prediction method. Fifth world congress on intelligent transport systems; 1998.
10. Dailey D.J. A statistical algorithm for estimating speed from single loop volume and occupancy measurements. Transportation Research, Part B: Methodological 33 (1999) 313-322
11. Zhang X., and Rice J.A. Short-term travel time prediction. Transportation Research, Part C: Emerging Technologies 11 3-4 (2003) 187-210
12. Palacharla P.V., and Nelson P.C. Application of fuzzy logic and neural networks for dynamic travel time estimation. International Transactions in Operational Research 6 (1999) 145-160
13. Park D., Rilett L.R., and Han G. Spectral basis neural networks for real-time travel time forecasting. Journal of Transportation Engineering 125 3 (1999) 515-523
14. Van Lint J.W.C., Hoogendoorn S.P., and Van Zuylen H.J. Accurate travel time prediction with state-space neural networks under missing data. Transportation Research, Part C: Emerging Technologies 13 (2005) 347-369
15. Chien SI.-J., and Kuchipudi C.M. Dynamic travel time prediction with real-time and historic data. Journal of Transportation Engineering 129 3 (2003) 608-616
16. Wang Y., Papageorgiou M., and Messmer A. A real time freeway network traffic surveillance tool. IEEE Transactions on Control Systems Technology 14 1 (2006) 18-32
17. Jia Z, Chen C, Coifman B, Varaiya P. The PeMS algorithm for accurate, real-time estimates of g-factors and speeds from single-loop detectors. IEEE intelligent transportation systems conference proceedings, Oakland, CA; 2001. p. 536-41.
18. Sreedevi I, Black J. Inductive loop detectors. ITS decision report; 2001.
19. Van Lint J.W.C., and Van der Zijpp N.J. Improving a travel time estimation algorithm by using dual loop detectors. Transportation Research Record 1855 (2003) 41-48
20. Zadeh L.A. Fuzzy sets. Information and Control 8 (1965) 338-353