Comparative Analysis of Peak Hour Factors for Four Major Collector Roads in Sulaymaniyah City, Iraq

Volume 13 ,Issue 1 ,December 2026 ,Pages 10-26

Authors

Laveen Kawa Burhan Alden 1 ; Chro Haidar Ahmed 1

1 Civil Engineering Department, College of Engineering, University of Sulaimani , KR, Iraq

DOI logo 10.17656/sjes.10229

Keywords

Abstract


This study presents a comparative analysis of Peak Hour Factors (PHF) for four major collector roads in Sulaymaniyah City, Iraq. Traffic volume data were collected using video recording techniques and processed at 15-minute intervals over a 24-hour period. The Peak Hour Factor was calculated for each site to assess the uniformity of traffic demand during peak periods. The results reveal notable variations in PHF values among the selected roads, indicating differences in within an hour demand concentration that were not directly proportional to peak-hour traffic volume. PHF values ranged from 0.849 to 0.986, with both extremes recorded at Twimalik during the morning period in opposite directions of travel: inbound direction showed the greatest within-hour concentration, while outbound direction showed the most even within-hour distribution. Kaniba exhibited consistently high PHF values (0.924–0.969) despite carrying the largest peak-hour volumes (up to 1,807 veh/h per direction). The lowest PHF coincided with the lowest observed volume, indicating that a low PHF does not necessarily correspond to a high hourly traffic demand. The largest differences occurred between the two directions of the same corridor rather than between corridors, with Twimalik ranging from 0.849 to 0.986 across its four directional observations. The observed PHF values were generally within ranges reported in comparable studies, although such comparisons are used only as contextual references rather than as classifications of traffic performance. The findings provide a descriptive basis for identifying periods and directions with greater within-hour demand concentration and for guiding future detailed operational investigations. Because PHF enters capacity analysis as a demand adjustment factor (v = V/PHF), the reported values also indicate the extent to which the design flow rate exceeds the average hourly volume at each site. The study contributes the first documented directional 15-minute traffic dataset covering a full 24-hour period for these corridors, providing a quantitative baseline for future surveys and identifying the periods and directions in which detailed operational investigation would be most productive. Because the data were collected on a single weekday during the summer holiday period, the values reported characterize the survey day.

References


  1. Bashingi, L., Mostafa, S., & Das, A. (2020). The state of congestion in the developing world. Transport Policy and Planning, 8(3), 45–62.
  2. Tarko, A. P., & Perez-Cartagena, R. I. (2005). Variability of peak hour factor at intersections. Transportation Research Record, 1920(1), 115–123. https://doi.org/10.1177/0361198105192000115
  3. Intrans. (2018). Section 5B-1: Street classifications [Manual on Uniform Traffic Control Devices]. Iowa Institute of Technical Assistance.
  4. Abdou, H., Ahmed, A., & Hassan, K. (2024). Driving risk identification of urban arterial and collector roads based on traffic characteristics. Accident Analysis & Prevention, 195, 107–119.
  5. Transportation Research Board. (1978). Peak-period traffic congestion in urban areas. National Research Council.
  6. Arterials. (2025). Understanding Peak Hour Factor (PHF) in traffic studies. https://www.arterials.co/peak-hour-factor-phf-in-traffic-studies/
  7. FHWA. (2025). Understanding traffic counts [Bulletin 25-05]. Federal Highway Administration, U.S. Department of Transportation.
  8. Ciont, N., Cadar, R. D., Iliescu, M., & Lasləu, D. A. (2015). Interactive application for the evaluation of the peak hour factor using weigh-in-motion traffic data. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science, 77(1), 121–128.
  9. Zheng, N., Wang, G., & Liu, Y. (2019). Mapping spatio-temporal patterns and detecting the factors of traffic congestion with multi-source data fusion and mining techniques. Computers, Environment and Urban Systems, 79, 101–118. https://doi.org/10.1016/j.ces.2019.09.009
  10. [10] Ma, F., Xu, J., Gao, C., & Bi, Y. (2022). Study on the applicability and modification of the design hourly volume on rural expressways considering holiday traffic polarization. International Journal of Environmental Research and Public Health, 19(16), 9876. https://doi.org/10.3390/ijerph19169876
  11. Greenshields, B. D., Channing, W., & Mitsu, H. (1934). A study of traffic capacity. Highway Research Board Proceedings, 14, 468-477.
  12. Zegris, C. (2004). The influence of land use on travel behavior. Transportation Research Board Annual Meeting.
  13. Hendrawan, H. (2020). Peak hour factor at urban road network system with fixed hourly interval and moving hourly interval (A case study at urban road in Cimahi City). Creative Research Journal, 6(01), 29. https://doi.org/10.34147/crj.v6i01.256
  14. Liu, Y., Wang, G., & Zheng, N. (2017). Impact analysis of land use on traffic congestion using real‐time data. Journal of Advanced Transportation, 5(3), 234–249. https://doi.org/10.1155/2017/7164790
  15. UNIVSUL. (2025). Effects of traffic violation and demographic characteristics on traffic safety in Sulaymaniyah City. Iraqi Journal for Engineering Sciences, 126. https://sjes.univsul.edu.iq/article?id=126
  16. Transportation Research Board. (2022). Highway Capacity Manual: A Guide for Multimodal Mobility Analysis (7th ed.). National Academies of Sciences, Engineering, and Medicine.
  17. Pal, A., & Roy, S. K. (2016). Evaluation of roadside friction and its effect on traffic flow on urban arterials. Transportation Research Procedia, 17, 616-625.
  18. Barceló, J. (2010). Fundamentals of traffic simulation. Springer.
  19. Turki, I. M., & Shubber, K. H. H. (2024). Hourly traffic flow variation at unsignalized intersection in Najaf city. AIP Conference Proceedings, 3092(1), 070003. https://doi.org/10.1063/5.0199763
  20. Hanumappa, D., Mulangi, R. H., & Kudachimath, N. S. (2018). Traffic characteristics evaluation and traffic management measures: A case study of Dharwad City. The Open Transportation Journal, 12(1), 258–272. https://doi.org/10.2174/1874447801812010258
  21. Lan, C.-J., & Abia, S. D. (2011). Determining peak hour factors for capacity analysis. Journal of Transportation Engineering, 137(8), 520–526. https://doi.org/10.1061/(asce)te.1943-5436.0000241
Statistics
  • Article view15
  • Downloads0
  • First online23 August 2026

  • RIS
  • BibTeX
  • EndNote
  • Mendeley
  • APA (7th edition)
  • MLA (9th edition)
  • Chicago
  • Harvard
  • IEEE
  • Vancouver