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All issues Volume 58 (2018) SHS Web Conf., 58 (2018) 01009 References
Open Access
Issue
SHS Web Conf.
Volume 58, 2018
GLOBMAR 2018 - Global Maritime Conference
Article Number 01009
Number of page(s) 12
DOI https://doi.org/10.1051/shsconf/20185801009
Published online 03 December 2018
  1. R. A. Daziano, D. Bolduc, Incorporating pro-environmental preferences towards green automobile technologies through a Bayesian hybrid choice model. Transportmetrica A: Transp. Sc., 9, 74-106 (2013) [CrossRef] [Google Scholar]
  2. W. Y. Szeto, Guest Editorial: Special Issue on Quantitative Approaches to Environmental Sustainability in Transportation Networks. Netw. and Spat. Econ., 16, 1-8 (2016) [CrossRef] [Google Scholar]
  3. W.Y. Szeto, Y. Jiang, D.Z.W. Wang, A. Sumalee, A sustainable road network design problem with land use transportation interaction over time. Netw. and Spat. Econ., 15, 791-822 (2015) [CrossRef] [Google Scholar]
  4. S. Cheon, E. Deakin, Supply chain coordination for port sustainability: lessons for new institutional designs. Transp. Res. Rec.: J. of the Transp. Res. Board, 2166, 10-19 (2010) [CrossRef] [Google Scholar]
  5. N. S. Kim, B. Van Wee, The relative importance of factors that influence the break-even distance of intermodal freight transport systems. J. of Transp. Geogr., 19, 859-875 (2011) [CrossRef] [Google Scholar]
  6. C.-C. Chang, C.-M. Wang, Evaluating the effects of green port policy: Case study of Kaohsiung harbor in Taiwan. Transp. Res. Part D: Transport and Environment, 17, 185-189 (2012) [CrossRef] [Google Scholar]
  7. J.-B. Sheu, T.-L. Hu, S.-R. Lin, The key factors of green port in sustainable development. Pakist. J. of Stat., 29, pp. 755-767 (2013) [Google Scholar]
  8. S. Wang, T. Notteboom, The role of port authorities in the development of LNG bunkering facilities in North European ports. WMU J. of Marit. Aff., 14, 61-92 (2015) [CrossRef] [Google Scholar]
  9. B. Wiegmans, R. Konings, Intermodal Inland Waterway Transport: Modelling Conditions Influencing Its Cost Competitiveness. The Asian J. of Shipp. and Log., 31, 273-294 (2015) [Google Scholar]
  10. M. Hann, L. Piotrowski, K. Woś, A new concept for utilising the Oder waterway in intermodal container transport. Sc. J. of Marit. Univ. in Szczecin, 47, 129-135 (2016) [Google Scholar]
  11. Ministry of Maritime Economy and Inland Navigation (2016) [Google Scholar]
  12. R. Rolbiecki, Badania popytu na przewozy ładunków transportem wodnym śródlądowym. Zeszyty Naukowe Uniwersytetu Gdańskiego. Ekonomika Transportu i Logistyka, 43, 41-50 (2012) [Google Scholar]
  13. G. De Jong, A. Daly, M. Pieters, C. Vellay, M. Bradley, F. Hofman, A model for time of day and mode choice using error components logit. Transp. Res. Part E: Log. and Transp. Rev., 39, 245-268 (2003) [CrossRef] [Google Scholar]
  14. M. Börjesson, Joint RP-SP data in a mixed logit analysis of trip timing decisions. Transp. Res. Part E: Logistics and Transportation Review, 44, 1025-1038 (2008) [CrossRef] [Google Scholar]
  15. J. Willigers, M. de Bok, Updating and extending the disaggregate choice models in the Dutch National Model. Paper Presented at the 2009 European Transport Conference, Noordwijkerhout (2009) [Google Scholar]
  16. P.R. Koster, The cost of travel time variability for air and car travellers. Doctoral Theses - Economics and Business Administration (2012) [Google Scholar]
  17. M. Gelhausen, P. Berster, D. Wilken, A new model of long term forecasting air passenger demand and the number of air transport movements of Germany. 2016 Atrs World Conference (2016) [Google Scholar]
  18. X. Feng, M. Saito, Y. Liu, Improve urban passenger transport management by rationally forecasting traffic congestion probability. Int. J. of Prod. Res., 54, 3465-3474 (2016) [CrossRef] [Google Scholar]
  19. R. Żochowska, G. Karoń, R. Janecki, A. Sobota, Selected Aspects of the Methodology of Traffic Flows Surveys and Measurements on an Urban Agglomeration Scale with Regard to ITS Projects. In: Macioszek E., Sierpiński G. (eds) Recent Advances in Traffic Engineering for Transport Networks and Systems. TSTP 2017. Lecture Notes in Networks and Systems, Springer, 21, pp. 37-49 (2017) [CrossRef] [Google Scholar]
  20. W. Abdelwahab, M. Sargious, Modelling the demand for freight transport: a new approach. J. of Transp. Econ. and Pol., 26, 49-70 (1992) [Google Scholar]
  21. G. De Jong, H. Gunn, W. Walker, National and international freight transport models: an overview and ideas for future development. Transp. Rev., 24, 103-124 (2004) [CrossRef] [Google Scholar]
  22. G. De Jong, M. Kouwenhoven, K. Ruijs, P van Houwe, D. Borremans, A time-period choice model for road freight transport in Flanders based on stated preference data. Transp. Res. Part E: Log. and Transp. Rev., 86, 20-31 (2016) [CrossRef] [Google Scholar]
  23. A. Valdas, R. Ruus, R. Põldaru, J. Roots, Forecasting road freight transport alternatives for sustainable regional development in Estonia. Economic Science for Rural Development Conference Proceedings, 42, 171-178 (2016) [Google Scholar]
  24. M. Postan, L. Filina-Dawidowicz, Dynamic Optimization Model for Planning of Supply, Production, and Transportation of Perishable Product. In: Suchanek M. (eds) Sustainable Transport Development, Innovation and Technology. TranSopot 2016. Springer Proceedings in Business and Economics, Springer, 235-244 (2016) [Google Scholar]
  25. C. W. Howe, J. L. Carroll, A. P. HurterJr, W.J. Leininger, S. G. Ramsey, N.L. Schwartz, E. Silberberg, R. M. Steinberg, Inland waterway transportation: studies in public and private management and investment decisions (Routledge 2016) [CrossRef] [Google Scholar]
  26. B. H. Archer, Forecasting demand: quantitative and intuitive techniques. Int. J. of Tour. Manag., 1, 5-12 (1980) [CrossRef] [Google Scholar]
  27. E. I. Vlahogianni, J. C. Golias, M. G. Karlaftis, Short-term traffic forecasting: Overview of objectives and methods. Transp. Rev., 24, 533-557 (2004) [CrossRef] [Google Scholar]
  28. J. Liu, W. Guan, A summary of traffic flow forecasting methods. J. of High. and Transp. Res. and Dev., 3, 82-85 (2004) [Google Scholar]
  29. B. Atasoy, A. Glerum, R. Hurtubia, M. Bierlaire, Demand for public transport services: Integrating qualitative and quantitative methods. 10th Swiss Transport Research Conference (2010) [Google Scholar]
  30. M. Patriksson, The traffic assignment problem: models and methods. Courier Dover Publications (2015) [Google Scholar]
  31. D. A. Hensher & K. J. Button (Eds.), Handbook of transport modeling (Emerald Group Publishing Limited 2007) [Google Scholar]
  32. J. D. D. Ortuzar, L. G. Willumsen, Modelling transport. John Wiey & Sons Ltd., New Delhi (2011) [CrossRef] [Google Scholar]
  33. M. Wegener, Overview of land use transport models. Handbook of transport geography and spatial systems, 127-146 (Emerald Group Publishing Limited 2004) [Google Scholar]
  34. S. P. Washington, M. G. Karlaftis, F. Mannering, Statistical and econometric methods for transportation data analysis (CRC press 2010) [Google Scholar]
  35. R. Chrobok, O. Kaumann, J. Wahle, M. Schreckenberg, Different methods of traffic forecast based on real data. Eur. J. of Oper. Res., 155, 558-568 (2004) [CrossRef] [Google Scholar]

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