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A New Approach to Determine Traffic Peak Periods to Utilize in Transportation Planning

  • Research Article-Civil Engineering
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Abstract

Detection of the traffic peak periods for a city has many advantages not only in terms of the traffic operation but also in urban transportation planning. In the planning, future travel demand is analyzed for peak and off-peak periods separately and transportation networks are designed based on these analyses. However, the traffic peak periods may shift in time because of changes in people's activities. Therefore, the detection and update of these periods periodically provide to improve urban transportation planning. The aim of this research is to present a new approach to determine traffic peak periods in order to utilize for use in transportation planning. In order to achieve this goal, the inductive loop detector data are obtained for a city and cleaned. Regular traffic flow patterns (without days that include unexpected incidents) of the whole city are produced, and peak periods are determined with these data. The Facebook Prophet software, which is a forecasting procedure, is presented as a new approach to determine traffic peak periods based on changepoint detection. In order to compare the results, experts' opinion, which is the conventional method, and the k-medoids clustering methods are applied. In conclusion, it is seen that the suggested new approach facilitates so much the accurate determination of traffic peak periods for urban transportation planning.

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References

  1. Doorley, R.; Pakrashi, V.; Caulfield, B.; et al.: Short-term forecasting of bicycle traffic using structural time series models. In: 17th International IEEE Conference on Intelligent Transportation Systems (ITSC), Qingdao, China, October 2014, pp. 1764–1769

  2. FDOT, 'Tampa bay regional planning model v8.0 technical report No. 1 Validation Report' (FDOT, 2015), pp. 1–234

  3. SCAG, 'Southern california association governments regional travel demand model and 2012 model validation' (SCAG, 2016), pp. 1–224

  4. Allen, W.G., Jr.: Analysis of corridor traffic peaking. Transp. Res. Rec. 1305, 50–60 (1991)

    Google Scholar 

  5. Sharmila, R.B.; Velaga, N.R.; Kumar, A.: SVM-based hybrid approach for corridor-level travel-time estimation. IET Intell. Transp. Syst. 13(9), 1429–1439 (2019)

    Article  Google Scholar 

  6. Qi, Y.; Ishak, S.: A hidden markov model for short term prediction of traffic conditions on freeways. Transp. Res. Part C Emerg. Technol. 43, 95–111 (2014)

    Article  Google Scholar 

  7. Zhang, Y.; Liu, Y.: Analysis of peak and non-peak traffic forecasts using combined models. J. Adv. Transp. 45(1), 21–37 (2011)

    Article  Google Scholar 

  8. IBB, 'İstanbul Metropoliten Alanı Kentsel Ulaşım Ana Planı (İUAP)'. (IBB, 2011), pp. 1–424

  9. MBB, 'Muğla Ulaşım Ana Planı (MUAP)'. (MBB, 2018), pp. 1–463

  10. Kieu, L.M.; Bhaskar, A.; Chung, E.: Empirical modelling of the relationship between bus and car speeds on signalised urban networks. Transp. Plan. Technol. 38(4), 465–482 (2015)

    Article  Google Scholar 

  11. Palshikar, G.: Simple algorithms for peak detection in time-series. In: Proceedings of 1st IIMA International Conference on Advanced Data Analysis, Business Analytics and Intelligence, Ahmedabad, India, June 2009, pp. 1–13

  12. Respati, S.; Bhaskar, A.; Zheng, Z., et al.: Systematic identification of peak traffic period. In: Proceedings of the 39th Australasian Transport Research Forum (ATRF), Auckland, New Zealand, November 2017, pp. 1–15

  13. Xiao, J.; Li, H.; Wang, X., et al.: Traffic peak period detection from an image processing view. J. Adv. Transp. 2097932, 1–9 (2018)

    Article  Google Scholar 

  14. Wang, X.; Cottrell, W.; Mu, S.: Using k-means clustering to identify time-of-day break points for traffic signal timing plans. In: Proceedings of the 8th International IEEE Conference on Intelligent Transportation Systems, Vienna, Austria, September 2005, pp. 586–591

  15. Ratrout, N.T.: Subtractive clustering-based k-means technique for determining optimum time-of-day breakpoints. J. Comput. Civ. Eng. 25(5), 380–387 (2011)

    Article  Google Scholar 

  16. Bulut, A.; Yanık, S.: Partitioning the Time of Day for Bus Schedule Optimization. In: International Conference on Intelligent and Fuzzy Systems, Istanbul, Turkey, July 2019, pp. 298–304

  17. Ma, D.; Li, W.; Song, X., et al.: Time-of-day breakpoints optimisation through recursive time series partitioning. IET Intel. Transp. Syst. 13(4), 683–692 (2018)

    Article  Google Scholar 

  18. Yang, M.H.; Luong, T.T.; Recker, W.: Extracting traffic patterns from loop detector data using multiple changepoints detection. In: 93rd Annual Meeting of the Transportation Research Board, Washington DC, USA, January 2014, pp. 1–16

  19. Aminikhanghahi, S.; Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51(2), 339–367 (2017)

    Article  Google Scholar 

  20. Kidando, E.; Moses, R.; Sando, T., et al.: Evaluating recurring traffic congestion using change point regression and random variation markov structured model. Transp. Res. Rec. 2672(20), 63–74 (2018)

    Article  Google Scholar 

  21. Killick, R.; Fearnhead, P.; Eckley, I.A.: Optimal detection of changepoints with a linear computational cost. J. Am. Stat. Assoc. 107(500), 1590–1598 (2012)

    Article  MathSciNet  Google Scholar 

  22. Killick, R.; Eckley, I.A.; Jonathan, P., et al.: Efficient detection of multiple changepoints within an oceano-graphic time series. In: Proceedings of the 58th world science congress of ISI, Dublin, Ireland, August 2011, pp. 1–6

  23. Killick, R.; Eckley, I.: Changepoint: an R package for changepoint analysis. J. Stat. Softw. 58(3), 1–19 (2014)

    Article  Google Scholar 

  24. Nam, C.F.; Aston, J.A.; Eckley, I.A., et al.: The uncertainty of storm season changes: quantifying the uncertainty of autocovariance changepoints. Technometrics 57(2), 194–206 (2015)

    Article  MathSciNet  Google Scholar 

  25. Lebarbier, É.: Detecting multiple change-points in the mean of Gaussian process by model selection. Signal Process. 85(4), 717–736 (2005)

    Article  Google Scholar 

  26. Liu, S.; Yamada, M.; Collier, N., et al.: Change-point detection in time-series data by relative density-ratio estimation. Neural Netw. 43, 72–83 (2013)

    Article  Google Scholar 

  27. Taylor, S.J.; Letham, B.: Forecasting at scale. Am. Stat. 72(1), 37–45 (2018)

    Article  MathSciNet  Google Scholar 

  28. ‘Forecasting at scale—Facebook Prophet’, https://facebook.github.io/prophet/. Accessed 13 February 2020

  29. TURKSTAT: 'The number of road vehicles by province, 2018', Turkey Statistical Institute, Road Motor Vehicles, December 2018

  30. Han, J.; Kamber, M.; Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)

    MATH  Google Scholar 

  31. ‘kmedoids - MATLAB’, https://www.mathworks.com/help/stats/kmedoids.html, Accessed 13 February 2020

  32. Park, H.S.; Jun, C.H.: A simple and fast algorithm for K-medoids clustering. Expert Syst. Appl. 36(2), 3336–3341 (2009)

    Article  Google Scholar 

  33. Choi, J.; Kwon, H.J.: The information filtering of gene network for chronic diseases: social network perspective. Int. J. Distrib. Sens. Netw. 11(9), 1–6 (2015)

    Article  Google Scholar 

  34. ‘GapEvaluation - MATLAB’, https://www.mathworks.com/help/stats/clustering.evaluation.gapevaluation-class.html, Accessed 13 February 2020

  35. Tibshirani, R.; Walther, G.; Hastie, T.: Estimating the number of clusters in a data set via the gap statistic. J. R. Stat. Soc. Ser. B Stat. Methodol. 63(2), 411–423 (2001)

    Article  MathSciNet  Google Scholar 

  36. Scott, C.; Newman, J., Thurston Region Planning Council Travel Demand Model Update Model Development Final Report' (Cambridge Systematics, Inc., 2016), pp. 1–97

  37. TRB: 'Highway capacity manual' (TRB, 2000, 4th edn. 2000)

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Correspondence to Abdulsamet Saracoglu.

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Saracoglu, A., Ozen, H., Apaydin, M.S. et al. A New Approach to Determine Traffic Peak Periods to Utilize in Transportation Planning. Arab J Sci Eng 46, 10409–10418 (2021). https://doi.org/10.1007/s13369-021-05384-2

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  • DOI: https://doi.org/10.1007/s13369-021-05384-2

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