Spatio-Temporal Analysis of Travel Speed for Urban Streets
DOI:
https://doi.org/10.31272/jeasd.2044Keywords:
Spatiotemporal, Travel Speed, Traffic Status, Urban Streets, Visual AnalysisAbstract
This research aims to develop a spatiotemporal visualization of travel speed on urban streets to predict traffic conditions. Three segments of major streets and eight minor collector streets within Palestine Street in Baghdad were selected to examine the distribution of average travel speed. This road network comprises three primary links and six minor collector streets: link 1 (Al-Mawal to Al-Nakhala intersection), link 2 (Al-Nakhala intersection to Al-Sakhara intersection), and link 3 (Al-Sakhara intersection to Beirut intersection). The temporary congestion periods are (12 p.m. to 1 p.m.) and (5 p.m. to 7 p.m.) for Link 1, with speeds ranging from 34 to 35 km/hr, whereas Link 3 is more congested, with speeds of about 29 km/hr. Link 2 indicated moderated congestion. Most of the spatial congestion is either on links 1 and 3, which are the major distributors for production and attraction trips on Palestine’s urban streets. The visualization method based on field data reflects the actual traffic status of speed values. It reveals the temporal and spatial aggregation characteristics of urban travel speed as an integral part of understanding traffic operation behavior.
References
L. Liu, H. Zhan, J. Liu, and J. Man, "Visual analysis of traffic data via spatiotemporal graphs and interactive topic modeling," J. Vis., vol. 22, pp. 141–160, 2018. https://doi.org/10.1007/s12650-018-0517-z
C. Cui, L. Zheng, and D. Sun, “Mining Private Vehicle Hot Routes Using Electronic Registration Identification Data,” pp. 51–56, Jun. 2019, doi: https://doi.org/10.1145/3341620.3341633.
C. Quek, M. Pasquier, and B. B. S. Lim, "POP-TRAFFIC: A novel fuzzy neural approach to road traffic analysis and prediction," IEEE Trans. Intell. Transp. Syst., vol. 7, no. 2, pp. 133–146, Jun. 2006. https://doi.org/10.1109/TITS.2006.874712
R. Pritchard, Y. Frøyen, and B. Snizek, "Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data," ISPRS Int. J. Geo-Inf., vol. 8, no. 5, p. 214, 2019. https://doi.org/10.3390/ijgi8050214
P. P. Chandra and S. Gangopadhyaya, "Speed Distribution Curves under Mixed Traffic Conditions," J. Transp. Eng., vol. 132, no. 6, pp. 475–481, Jun. 2006. https://doi.org/10.1061/(ASCE)0733-947X(2006)132:6(475)
J. McFadden, W. Yang, and S. R. Durrans, "Application of Artificial Neural Networks to Predict Speeds on Two-Lane Rural Highways," Transp. Res. Rec., J. Transp. Res. Board, vol. 1751, no. 1, pp. 9–17, 2001. https://doi.org/10.3141/1751-02
Y. M. Najjar, R. W. Stokes, and E. R. Russell, "Setting Speed Limits on Kansas Two-Lane Highways," Transp. Res. Rec., J. Transp. Res. Board, vol. 1708, no. 1, pp. 20–27, 2000. https://doi.org/10.3141/1708-03
A. S. Al-Ghamdi, "Spot Speed Analysis on Urban Roads in Riyadh," Transp. Res. Rec., J. Transp. Res. Board, vol. 1635, no. 1, pp. 162–170, 1998. https://doi.org/10.3141/1635-22
A. M. Rao and K. R. Rao, "Free Speed Modelling for Urban Arterials – A Case Study on Delhi," Period. Polytech. Transp. Eng., vol. 43, no. 3, pp. 111–119, 2015. https://doi.org/10.3311/PPtr.7599
K. K. Dixson, C.-H. Wu, W. Sarasua, and J. Daniel, "Posted and Free-Flow Speeds for Rural Multilane Highways in Georgia," J. Transp. Eng., vol. 125, no. 6, pp. 487–494, Nov. 1999. https://doi.org/10.1061/(ASCE)0733-947X(1999)125:6(487)
A. Ali, A. Flannery, and M. Venigalla, "Prediction Models for Free Flow Speed on Urban Streets," presented at the 86th Annu. Meet. Transp. Res. Board, Washington, DC, USA, 2007. [Online]. Available: https://trid.trb.org/view/801967
S. C. Himes and E. T. Donnell, "Speed Prediction Models for Multi- Lane Highways: A Simultaneous Equations Approach," J. Transp. Eng., vol. 136, no. 10, pp. 855–862, Oct. 2010. https://doi.org/10.1061/(ASCE)TE.1943-5436.0000149
J. Yang, X. Liu, J. Du, and C. Chen, "Exploring the Relationship between Urban Street Spatial Patterns and Street Vitality: A Case Study of Guiyang, China," Int. J. Environ. Res. Public Health, vol. 20, no. 2, p. 1646, 2023. https://doi.org/10.3390/ijerph20021646
H. Cui, G. Yuan, N. Liu, M. Xu, and H. Song, "Convolutional neural network for recognizing highway traffic congestion," J. Intell. Transp. Syst., vol. 24, no. 3, pp. 279–289, 2020. https://doi.org/10.1080/15472450.2020.1742121
L. Yang, L. Zhang, A. Philippopoulos-Mihalopoulos, E. J. L. Chappin, and K. H. van Dam, "Integrating agent-based modeling, serious gaming, and co-design for planning transport infrastructure and public spaces," Urban Des. Int., vol. 26, no. 1, pp. 67–81, 2021. https://doi.org/10.1057/s41289-020-00117-7
C. Henry, S. M. Azimi, and N. Merkle, "Road segmentation in SAR satellite images with deep fully convolutional neural networks," IEEE Geosci. Remote Sens. Lett., vol. 15, no. 12, pp. 1867–1871, Dec. 2018. https://doi.org/10.1109/LGRS.2018.2864342
Z. Wang, Y. Yang, S. Nijhuis, and S. van der Spek, “Understanding human-environment interaction in urban spaces with emerging data-driven approach: A systematic review of methods and evidence,” Cities, vol. 167, p. 106346, Aug. 2025, doi: https://doi.org/10.1016/j.cities.2025.106346.
G. Andrienko, N. Andrienko, P. Bak, D. Keim, and S. Wrobel, Visual Analytics of Movement. Berlin, Germany: Springer, 2013.
Z. A. Alkaissi, "Traffic simulation of continuous flow intersection with displaced left-turn: a case study," J. Eng. Appl. Sci., vol. 69, no. 1, p. 39, 2022. https://doi.org/10.1186/s44147-022-00091-7
Z. A. Alkaissi, "Traffic Simulation of Urban Street to Estimate Capacity," J. Eng., vol. 28, no. 4, pp. 51–63, 2022. https://doi.org/10.31026/j.eng.2022.04.04
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Copyright (c) 2026 Zainab Ahmed Alkaissi, Mohammad Sadar Jasim, Abbas Mohammed (Author)
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