Arabian Journal for Science and Engineering

, Volume 44, Issue 5, pp 4517–4532 | Cite as

Modelling Distribution of External–Internal Trips and Its Intra-region and Inter-region Transferability

  • Syed F. A. BaqueriEmail author
  • Muhammad Adnan
  • Luk Knapen
  • Tom Bellemans
  • Davy Janssens
Research Article - Civil Engineering


Estimating external–internal (EI) trips is an important component of travel demand modelling which is ignored in earlier studies. This study describes conventional and standardized regression models to analyse EI trips in Karachi metropolis, Pakistan, and in three cities in Belgium. Model development relies only on the travel survey data to obtain travel information of the residents of the study area and on open source platforms for land use and transport network information. Intra-region and inter-region transferability of the model is examined through four transfer methods: naïve, intercept update, coefficients update and standardized regression. Results revealed that the standardized regression model is most suitable for model transferability between two regions. Furthermore, land use profile and type of the study area are the two most significant factors that govern the transferability. The results will help estimate EI trips and incorporate them in the travel demand model estimation.


External trips External–internal trips Open source data Spatial transferability Standardized regression Household travel survey 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Talbot, E.S.; Burris, M.W.; Farnsworth, S.: Estimating through-trip travel without external surveys. Transp. Res. Rec. J. Transp. Res. Board. 2254, 104–111 (2011). CrossRefGoogle Scholar
  2. 2.
    Han, Y.; Stone, J.: Synthesized through-trip models for small and medium urban areas. Transp. Res. Rec. J. Transp. Res. Board. 2077, 148–155 (2008). CrossRefGoogle Scholar
  3. 3.
    Modlin, D.G.: Synthetic through trip patterns. Transp. Eng. J. ASCE. 100, 363–378 (1974)Google Scholar
  4. 4.
    Modlin, D.G.: Synthesized through-trip table for small urban areas. Transp. Res. Rec. J. Transp. Res. Board 842, 16–21 (1982)Google Scholar
  5. 5.
    Horowitz, A.J.; Patel, M.H.: Through trip tables for small urban areas? A method for quick response travel forecasting. Transp. Res. Rec. J. Transp. Res. Board 1685, 57–64 (1999)CrossRefGoogle Scholar
  6. 6.
    Anderson, M.; Abdullah, Y.: A Small Community Through Trip Rate Methodology. University Transportation Center for Alabama, Huntsville (2005)Google Scholar
  7. 7.
    Anderson, M.: Spatial economic model for forecasting the percentage splits of external trips on highways approaching small communities. Plan. Anal. 2005, 68–73 (2005)Google Scholar
  8. 8.
    Martchouk, M.; Fricker, D.J.: Through-trip matrices using discrete choice models: planning tool for smaller cities. In: Transportation Research Board 88th Annual Meeting (2009)Google Scholar
  9. 9.
    Khan, T.; Anderson, M.: Estimation of through trips using existing traffic counts. Int. J. Traffic Transp. Eng. 4, 415–424 (2014). CrossRefGoogle Scholar
  10. 10.
    Anderson, M.; Kenchappagoudra, M.; Dondapati, M.C.; Harris, G.A.: Pass-through freight modeling at the statewide and metropolitan level. Int. J. Traffic Transp. Eng. 4, 1–13 (2014). CrossRefGoogle Scholar
  11. 11.
    Qian, Z.; Han, Y.; Stone, J.R.: Forecasting external trips in small and medium cities based on local economic context. Procedia Soc. Behav. Sci. 43, 284–293 (2012). CrossRefGoogle Scholar
  12. 12.
    Huntsinger, L.F.; Ward, K.: Using mobile phone location data to develop external trip models. Transp. Res. Rec. J. Transp. Res. Board 2499, 25–32 (2015). CrossRefGoogle Scholar
  13. 13.
    Giaimo, G.T.: Modifications to traditional external trip models. Transp. Res. Rec. J. Transp. Res. Board 1817, 163–171 (2007). CrossRefGoogle Scholar
  14. 14.
    Ortuzar, J.D.; Willumsen, L.G.: Modelling Transport. Wiley, London (2011)CrossRefGoogle Scholar
  15. 15.
    Webster, F.V.; Bly, P.H.; Johnston, R.H.; Paulley, N.; Dasgupta, M.: Changing patterns of urban travel. Transp. Rev. 6, 49–86 (1986). CrossRefGoogle Scholar
  16. 16.
    Jansen, G.R.M.; Vuren, V.T.: Travel patterns in Dutch metropolitan cities: the importance of external trips. Transportation (Amst). 15, 317–336 (1988). CrossRefGoogle Scholar
  17. 17.
    Rose, G.; Koppelman F.S.: Transferability of disaggregate trip generation models. In: Proceedings of Ninth International Symposium on Transportation and Traffic Theory, pp. 471–491. VNU Science Press, Utrecht, Netherlands (1984)Google Scholar
  18. 18.
    Kawamoto, E.: Transferability of standardized regression model applied to person-based trip generation. Transp. Plan. Technol. 26, 331–359 (2003). CrossRefGoogle Scholar
  19. 19.
    Cotrus, A.V.; Prashker, J.N.; Shiftan, Y.: Spatial and temporal transferability of trip generation demand models in Israel. J. Transp. Stat. 8, 37–56 (2005)Google Scholar
  20. 20.
    Poplawski, K.; Gould, T.; Setton, E.; Allen, R.; Su, J.; Larson, T.; Henderson, S.; Brauer, M.; Hystad, P.; Lightowlers, C.; Keller, P.; Cohen, M.; Silva, C.; Buzzelli, M.: Intercity transferability of land use regression models for estimating ambient concentrations of nitrogen dioxide. J. Expo. Sci. Environ. Epidemiol. 19, 107–117 (2009). CrossRefGoogle Scholar
  21. 21.
    Sikder, S.; Pinjari, A.R.; Srinivasan, S.; Nowrouzian, R.: Spatial transferability of travel forecasting models: a review and synthesis. Int. J. Adv. Eng. Sci. Appl. Math. 5, 104–128 (2013). CrossRefGoogle Scholar
  22. 22.
    Adeel, M.: Comparing urban footprint of Lahore and Karachi. Accessed 15 Mar 2017
  23. 23.
    Pakistan Bureau of Statistics. Population of Pakistan, Population (English Edition), pp. 24–39 (1998).
  24. 24.
    City population: major agglomerations of the world.
  25. 25.
    Brussels population: world population review.
  26. 26.
    Karasmaa, N.: Evaluation of transfer methods for spatial travel demand models. Transp. Res. Part A Policy Pract. 41, 411–427 (2007). CrossRefGoogle Scholar
  27. 27.
    Koppelman, F.S.; Wilmot, C.G.: The effect of omission of variables on choice model transferability. Transp. Res. Part B 20, 205–213 (1986). CrossRefGoogle Scholar
  28. 28.
    Japan International Cooperation Agency: Karachi Transportation Improvement Project (KTIP)—2030 (2012)Google Scholar
  29. 29.
    Perrakis, K.; Karlis, D.; Cools, M.; Janssens, D.; Vanhoof, K.; Wets, G.: A Bayesian approach for modeling origin-destination matrices. Transp. Res. Part A Policy Pract. 46, 200–212 (2012). CrossRefGoogle Scholar
  30. 30.
  31. 31.
  32. 32.
    OpenStreetMap contributors (2017). Accessed 15 Apr 2017
  33. 33.
    QGIS: Quantum GIS Development Team. Quantum GIS geographic information system. Open source geospatial foundation project (2015). Accessed 22 Apr 2017
  34. 34.
    Frank, L.D.; Schmid, T.L.; Sallis, J.F.; Chapman, J.; Saelens, B.E.: Linking objectively measured physical activity with objectively measured urban form. Am. J. Prev. Med. 28, 117–125 (2005). CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Syed F. A. Baqueri
    • 1
    Email author
  • Muhammad Adnan
    • 1
  • Luk Knapen
    • 1
  • Tom Bellemans
    • 1
  • Davy Janssens
    • 1
  1. 1.Transportation Research Institute (IMOB)-UHasselt- Hasselt University, AgoralaanDiepenbeekBelgium

Personalised recommendations