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A multi commodity flow model incorporating flow reduction functions

  • Bayan BevraniEmail author
  • Robert L. Burdett
  • Ashish Bhaskar
  • Prasad K. D. V. Yarlagadda
Article
  • 23 Downloads

Abstract

In this article an improved multi-commodity network flow (MCNF) model is introduced to holistically assess a multi modal transportation system and to identify the optimal flows achievable. The principal innovation of this model, that distinguishes it from recent approaches, is the inclusion of flow reduction functions (FRF) to automatically scale the flows on specific parts of the network. Reduced flows occur for many reasons but typically as a result of congestion, accidents, road works, and human behaviour. The inclusion of flow reductions permits a more accurate and reliable assessment and reduces overestimated flows. In practice, the speed of vehicles also affects the flow and the level of congestion. Consequently, model extensions have been introduced to facilitate the identification of the most appropriate speed on each link in the network. As the FRF are non-linear, it is necessary to approximate them using piecewise linear mathematical functions and to solve the MCNF using Separable Programming techniques. Several real-life case studies demonstrate the potential of the proposed approach and its general applicability. The numerical investigations highlight the capacity losses incurred, for different FRF, and the ease in which different functions can be chosen and evaluated.

Keywords

Multi-modal transportation system Capacity assessment Flow reduction functions Congestion Speed selection 

Notes

References

  1. ABC News (2018) M1 speed limit drop will improve congestion and travel times, experts say. Retrieved March 1, 2018 from https://www.abc.net.au/news/2018-03-01/m1-speed-limit-drop-to-improve-commonwealth-games-commute-time/9497532
  2. Aftabuzzaman M (2007) Measuring traffic congestion—a acritical review. 30th Australasian Transport Research ForumGoogle Scholar
  3. AIMMS 3.13 (2014) AIMMS modeling guide—integer programming tricks. Retrieved November 19, 2014 from https://download.aimms.com/aimms/download/manuals/AIMMS3OM_IntegerProgrammingTricks.pdf
  4. Archer J, Fotheringham N, Symmons M, Corben B (2008) The impact of lowered speed limits in urban and metropolitan areas. Transport Accident Commission Accident Research Centre, Monash University. Retrieved June 12, 2008 from http://www.monash.edu.au/muarc/reports/muarc276.pdf
  5. Article (2002) 20 mph limits save time and improve traffic flow. Retrieved June 1, 2012 from http://www.20splenty.org/20mph_limits_save_time_and_improve_traffic_flow
  6. Beckmann MJ (2013) Traffic congestion and what to do about it. Transp B Transp Dyn 1(1):103–109Google Scholar
  7. Bevrani B, Burdett RL, Yarlagadda PKDV (2015) A case study of the Iranian national railway and its absolute capacity expansion using analytical models. Transport 32(4):398–414CrossRefGoogle Scholar
  8. Bevrani B, Burdett RL, Bhaskar A, Yarlagadda PKDV (2017) A capacity assessment approach for multi-modal transportation systems. Eur J Oper Res 263(3):864–878MathSciNetCrossRefzbMATHGoogle Scholar
  9. Bliemer M, Raadsen M, Brederode L, Bell M, Wismans L, Smith M (2015) Genetics of traffic assignment models for strategic transport planning. In: Australasian Transport Research Forum (ATRF), 37th, 2015, Sydney, New South Wales, AustraliaGoogle Scholar
  10. Brisbane City Council (2018). Retrieved December 17, 2018 from https://www.brisbane.qld.gov.au/traffic-transport/reducing-congestion
  11. Burdett RL (2015) Multi-objective models and techniques for analysing the absolute capacity of railway networks. Eur J Oper Res 245(2):489–505MathSciNetCrossRefzbMATHGoogle Scholar
  12. Burdett RL (2016) Optimization models for expanding a railway’s theoretical capacity. Eur J Oper Res 251:783–797MathSciNetCrossRefzbMATHGoogle Scholar
  13. Burdett RL, Kozan E (2006) Techniques for absolute capacity determination in railways. Transp Res B Methodol 40(8):616–632CrossRefGoogle Scholar
  14. Burdett RL, Kozan E, Sinnot M, Cook D, Tian YC (2017) A mixed integer linear programming approach to perform hospital capacity assessments. Exp Syst Appl 77:170–188CrossRefGoogle Scholar
  15. Cancela H, Mauttone A, Urquhart ME (2015) Mathematical programming formulations for transit network design. Transp Res B Methodol 77:17–37CrossRefGoogle Scholar
  16. Currie G, Sarvi M, Young B (2007) A new approach to evaluating on-road public transport priority projects: balancing the demand for limited road-space. Transportation 34(4):413–428CrossRefGoogle Scholar
  17. De Palma A, Lindsey R (2011) Traffic congestion pricing methodologies and technologies. Transp Res C Emerg Technol 19(6):1377–1399CrossRefGoogle Scholar
  18. Fageda X, Flores-Fillol R (2016) How do airlines react to airport congestion? The role of networks. Reg Sci Urb Econ 56:73–81CrossRefGoogle Scholar
  19. Fields G, Hartgen D, Moore A, Poole RW (2009) Relieving congestion by adding road capacity and tolling. Int J Sustain Transp 3(5–6):360–372CrossRefGoogle Scholar
  20. Fosgerau M, De Palma A (2013) The dynamics of urban traffic congestion and the price of parking. J Public Econ 105:106–115CrossRefGoogle Scholar
  21. Fröidh O, Sipilä H, Warg J (2014) Capacity for express trains on mixed traffic lines. Int J Rail Transp 2(1):17–27CrossRefGoogle Scholar
  22. HCM (2010) Highway capacity manual, 5th edn. Transportation Research Board, Washington, D.C.Google Scholar
  23. Heydecker BG, Addison JD (2011) Analysis and modelling of traffic flow under variable speed limits. Transp Res C Emerg Technol 19(2):206–217CrossRefGoogle Scholar
  24. Jiang C, Zhang A (2014) Effects of high speed rail and airline cooperation under hub airport capacity constraint. Transp Res B 60:33–49CrossRefGoogle Scholar
  25. Kozan E, Burdett R (2005) A railway capacity determination model and rail access charging methodologies. Transp Plan Technol 28(1):27–45CrossRefGoogle Scholar
  26. Reynolds-Feighan AJ, Button KJ (1999) An assessment of the capacity and congestion levels at European airports. J Air Transp Manag 5:113–134CrossRefGoogle Scholar
  27. SteadieSeifi M, Dellaert NP, Nuijten W, Van Woensel T, Raoufi R (2014) Multimodal freight transportation planning: a literature review. Eur J Oper Res 233(1):1–15CrossRefzbMATHGoogle Scholar
  28. TCQSM (2013) Transit capacity and quality of service manual. Retrieved April 12, 2013 from http://www.trb.org/Main/Blurbs/169437.aspx
  29. TDM Encyclopedia (2018) Speed reductions. Strategies that reduce traffic speeds. Retrieved August 28, 2018 from https://www.vtpi.org/tdm/tdm105.htm
  30. Tirachini A, Hensher DA, Rose JM (2014) Multimodal pricing and optimal design of urban public transport: the interplay between traffic congestion and bus crowding. Transp Res B Methodol 61:33–54CrossRefGoogle Scholar
  31. Wan Y, Zhang A, Yuen ACL (2013) Urban road congestion, capacity expansion and port competition: empirical analysis of US container ports. Marit Policy Manag 40(5):417–438CrossRefGoogle Scholar
  32. Wu D, Yin Y, Lawphongpanich S, Yang H (2012) Design of more equitable congestion pricing and tradable credit schemes for multimodal transportation networks. Transp Res B Methodol 46(9):1273–1287CrossRefGoogle Scholar
  33. Xiao F, Yang H, Han D (2007) Competition and efficiency of private toll roads. Trans Res B Methodol 41(3):292–308CrossRefGoogle Scholar
  34. Yang H, Bell MGH, Meng Q (2000) Modeling the capacity and level of service of urban transportation networks. Trans Res B Methodol 34(4):255–275CrossRefGoogle Scholar
  35. Zhao L, Lai Y-C, Park K, Ye N (2005) Onset of traffic congestion in complex networks. Phys Rev E 71(2):026125CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Chemistry, Physics and Mechanical EngineeringQUTBrisbaneAustralia
  2. 2.School of Mathematical SciencesQUTBrisbaneAustralia
  3. 3.School of Civil Engineering and Built EnvironmentQUTBrisbaneAustralia

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