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Arabian Journal for Science and Engineering

, Volume 44, Issue 5, pp 4509–4516 | Cite as

Predicting Traffic Attracted to Schools in Early Planning Stages: A Comparative Study

  • Nedal T. Ratrout
  • Khaled J. AssiEmail author
  • Uneb Gazder
Research Article - Civil Engineering
  • 16 Downloads

Abstract

This paper estimates traffic attracted to schools using attributes that are usually available in the early stages of urban planning, namely the neighborhoods’ average income and lot area. The paper also compares the prediction of trips using attributes that are more informative such as the gross floor area, the number of students and classrooms. The number of students and floor area provided the best trip prediction models, while the neighborhoods’ income and lot area produced reasonable models, but not as robust, for the early planning stage. The observed school trips were compared to the trips estimated by the developed models. Moreover, the observed trips were compared with trips estimated using three models commonly used in the study area, namely the Institute of Transportation Engineers’ (ITE) rates from the USA, Riyadh City rates from Saudi Arabia, and Abu Dhabi rates from the United Arab Emirates. The developed detailed models provided the best results in the study area followed by the ITE rates. The Abu Dhabi rates were also reasonable and better than the Riyadh rates.

Keywords

School trips Early planning stages ITE rates Ridge regression 

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Notes

Acknowledgements

The authors acknowledge the support provided by the Deanship of Scientific Research at King Fahd University of Petroleum and Minerals for funding this work under project No. IN131008.

References

  1. 1.
    Assi, K.J.: The effect of local schools traffic on congestion of urban network, MSc dissertation, King Fahd University of Petroleum and Minerals, Saudi Arabia (2013)Google Scholar
  2. 2.
    Institute of Transportation Engineers: Trip Generation, 10th edn (2017)Google Scholar
  3. 3.
    The Department of Transport (DoT) in Abu Dhabi: “Trip Generation and Parking Rates Manual.” Emirate of Abu Dhabi (2012)Google Scholar
  4. 4.
    Ministry of Municipal and Rural Affairs: Trip Generation Manual for Riyadh City, Saudi Arabia (2009)Google Scholar
  5. 5.
    Slipp, P.R.; Hummer, J.E.: Trip generation rate update for public high schools. ITE J. 66(6), 34 (1996)Google Scholar
  6. 6.
    Dean, D.; Dean, T.; Alfridi, A.: City of Surrey pick-up/drop-off trip generation survey. In: ITE 2010 Annual Meeting and Exhibit Institute of Transportation Engineers (ITE) (2010)Google Scholar
  7. 7.
    Balmer, A.M.; French, L.J.; Eck, R.W.; Legg, J.: Trip generation rates of consolidated schools. Inst. Transp. Eng. ITE J. 70(8), 30 (2000)Google Scholar
  8. 8.
    Millard-Ball, A.: Phantom trips: overestimating the traffic impacts of new development. J. Transp. Land Use 8(1), 31–49 (2015)CrossRefGoogle Scholar
  9. 9.
    Al-Zahrani, A.H.; Hasan, T.: Trip generation at fast food restaurants in Saudi Arabia. ITE J. 78(2), 24–29 (2008)Google Scholar
  10. 10.
    Mahmoudi, J.: Trip generation characteristics of super convenience market-gasoline pump stores. ITE J. 82(6), 16–21 (2012)Google Scholar
  11. 11.
    de Andrade, P.; Portugal, E.; da Silva, L.: Checking the validity of the ITE trip generation models for Brazilian shopping centers. ITE J. 80(8), 40–44 (2010)Google Scholar
  12. 12.
    Vivian, G.M.: Trip generation characteristics of free-standing discount superstores. ITE J. 76(8), 30–32, 37 (2006)Google Scholar
  13. 13.
    Greene, C.; Kannan, V.: A trip generation study of coffee/donut shops in Western New York. ITE J. 81(6), 40–45 (2011)Google Scholar
  14. 14.
    Schneider, R.J.; Shafizadeh, K.; Handy, S.L.: Method to adjust Institute of Transportation Engineers vehicle trip-generation estimates in smart-growth areas. J. Transp. Land Use 8(1), 69–83 (2015)CrossRefGoogle Scholar
  15. 15.
    Central Department of Statistics & Information: Census Report for 2010. Saudi Arabia (2010)Google Scholar
  16. 16.
    Barlett, J.E.; Kotrlik, J.W.; Higgins, C.C.: Organizational research: Determining appropriate sample size in survey research. Inf. Technol. Learn. Perform. J. 19(1), 43 (2001)Google Scholar
  17. 17.
    MacCallum, R.C.; Widaman, K.F.; Zhang, S.; Hong, S.: Sample Size in Factor Analysis. Psychol. Methods 4(1), 84–99 (1999)CrossRefGoogle Scholar
  18. 18.
    Chang, J.S.; Jung, D.; Kim, J.; Kang, T.: Comparative analysis of trip generation models: results using home-based work trips in the Seoul metropolitan area. Transp. Lett. 6(2), 78–88 (2014)CrossRefGoogle Scholar
  19. 19.
    Kutner, M.H.; Nachtsheim, C.; Neter, J.: Applied Linear Regression Models. McGraw-Hill/Irwin, New York (2004)Google Scholar
  20. 20.
    Ratrout, N.T.; Gazder, U.; Assi, K.J.: Effect of public transportation in reducing passenger car trips to schools in Al-Khobar-Dhahran metropolitan area, Saudi Arabia. Transp. Lett. 10(1), 43–51 (2018)CrossRefGoogle Scholar
  21. 21.
    The General Authority for Statistics: “Household Expenditure and Income Survey”. Saudi Arabia (2013)Google Scholar
  22. 22.
    Kaufman, L.; Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis, vol. 344. John Wiley & Sons, New York (2009)zbMATHGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2018

Authors and Affiliations

  • Nedal T. Ratrout
    • 1
  • Khaled J. Assi
    • 1
    Email author
  • Uneb Gazder
    • 2
  1. 1.Department of Civil and Environmental EngineeringKing Fahd University of Petroleum & MineralsDhahranSaudi Arabia
  2. 2.Department of Civil EngineeringUniversity of Bahrain (Issa Town Campus)ManamahBahrain

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