Transactions on Transport Sciences 2025, 16(1):35-43 | DOI: 10.5507/tots.2024.020

Development of Congestion Severity Index for Speed Humps Utilizing Fundamental Parameters and Clustering Techniques - A Case Study in India

Malaya Mohantya, Satya Ranjan Samala*, J Cyril Santhoshb
a. School of Civil Engineering, KIIT Deemed to be University, Bhubaneswar, India
b. Coimbatore Institute of Technology, Coimbatore, India

Traffic congestion has widespread negative impacts on the environment, urban development, and road safety, leading to increased commute times and heightened incidents of road rage and accidents. Evaluating congestion, particularly in relation to speed humps, becomes crucial due to their complex impact on traffic flow. Although few studies have explored delay estimation and lane-changing behaviour at speed humps, the larger issue of traffic congestion has received less attention. Recognizing and measuring congestion levels at these humps can be pivotal in devising specified strategies to alleviate the challenge. The present investigation focused on adapting travel time reliability metrics, specifically the Planning Time Index (PTI) and Travel Time Index (TTI), to consider the influence of speed humps. These adjusted metrics have been used to assess congestion in two critical zones: the area before the speed humps where vehicles slow down and the sections covering the humps. The study took a comprehensive approach by using video analysis to gather data on various vehicles operating on the road. Subsequently, the PTI and TTI were analyzed for their relationships with different speed percentiles (98th, 85th, and 15th). The findings revealed compelling correlations allying PTI, TTI, the 15th and 85th percentile speeds, surpassing the relation with the 98th percentile speed. This analysis formed the basis for a congestion severity index, outlining distinct congestion levels. The study employed K-means clustering, ensuring a logical and data-driven categorization of congestion severity at speed humps. To sum up, this research not only enhances our understanding of traffic congestion at speed humps but also lays the groundwork for implementing targeted measures to effectively mitigate these issues.

Keywords: Speed humps; Traffic congestion; Travel time index; Planning time index; Clustering

Received: March 8, 2024; Revised: September 24, 2024; Accepted: October 8, 2024; Prepublished online: January 1, 2025; Published: April 26, 2025  Show citation

ACS AIP APA ASA Harvard Chicago Chicago Notes IEEE ISO690 MLA NLM Turabian Vancouver
Mohanty, M., Ranjan Samal, S., & Cyril Santhosh, J. (2025). Development of Congestion Severity Index for Speed Humps Utilizing Fundamental Parameters and Clustering Techniques - A Case Study in India. Transactions on Transport Sciences, 16(1), 35-43. doi: 10.5507/tots.2024.020
Share...
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Afrin, T., & Yodo, N. (2020). A surve y of road traffic congestion measures towards a sustainable and resilient transportation system. Sustainability, 12(11), 4660, https://doi.org/10.3390/su12114660 Go to original source...
    2. Aftabuzzaman, M. (2007, September). Measuring traffic congestion-a critical review. In 30th Australasian Transport Research Forum, Retrieved from https://australasiantransportresearchforum.org.au/wp-content/uploads/2022/03/2007_Aftabuzzaman.pdf
    3. Akēelik, R., and Besley, M. (2001). Acceleration and deceleration models. In Proceedings of 23rd Conference of Australian Institute of Transport Research. Monash University Melbourne, Australia, Retrieved from https://www.researchgate.net/profile/Rahmi-Akcelik/publication/238778191_Acceleration_and_deceleration_models/links/004635328134528aaa000000/Acceleration-and-deceleration-models.pdf
    4. Antię, B., Pe¹ię, D., Vujanię, M., and Lipovac, K. (2013). The influence of speed bumps heights to the decrease of the vehicle speed-Belgrade experience. Safety Science, 57, 303-312, https://doi.org/10.1016/j.ssci.2013.03.008 Go to original source...
    5. Bennett, C., and Dunn, R.C.M. (1995). Driver deceleration behaviour on a freeway in New Zealand, Transportation Research Record, 1510: 70-75, Retrieved from https://www.safetylit.org/citations/index.php?fuseaction=citations.viewdetails&citationIds[]=citjournalarticle_687929_38
    6. Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data: Recent advances in clustering (pp. 25-71). Berlin, Heidelberg: Springer Berlin Heidelberg, https://doi.org/10.1007/3-540-28349-8_2 Go to original source...
    7. Boora, A., Ghosh, I., & Chandra, S. (2017). Assessment of level of service measures for two-lane intercity highways under heterogeneous traffic conditions. Canadian Journal of Civil Engineering, 44(2), 69-79, https://doi.org/10.1139/cjce- 2016-0275 Go to original source...
    8. He, F., Yan, X., Liu, Y., and Ma, L. (2016). A traffic congestion assessment method for urban road networks based on speed performance index. Procedia engineering, 137, 425-433, https://doi.org/10.1016/j.proeng.2016.01.277 Go to original source...
    9. Indian Roads Congress, IRC:99-2018 Guidelines for Traffic Calming Measures in Urban and Rural Areas (First Revision), website: https://law.resource.org/pub/in/bis/irc/irc.gov.in.099.2018.pdf.
    10. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern recognition letters, 31(8), 651-666, https://doi.org/10.1016/j.patrec.2009.09.011 Go to original source...
    11. Jain, A. K., & Dubes, R. C. (1988). Algorithms for clustering data. Prentice-Hall, Inc., https://dl.acm.org/doi/abs/10.5555/42779
    12. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: a review. ACM computing surveys (CSUR), 31(3), 264-323, https://dl.acm.org/doi/abs/10.1145/331499.331504 Go to original source...
    13. Jain, M., Singh, A. P., Bali, S., and Kaul, S. (2012). Speed-Breaker Early Warning System. In NSDR, website: https://www.usenix.org/conference/nsdr12/speed-breaker-early-warning-system
    14. Kanungo, T., Mount, D. M., Netanyahu, N. S., Piatko, C. D., Silverman, R., & Wu, A. Y. (2002). An efficient k-means clustering algorithm: Analysis and implementation. IEEE transactions on pattern analysis and machine intelligence, 24(7), 881-892, https://doi.org/10.1109/TPAMI.2002.1017616 Go to original source...
    15. Kumar, P. G., Samal, S. R., Prasanthi, L., Bhavitha, V., & Devi, J. M. (2020, December). Level of service of urban and rural roads-a case study in Bhimavaram. In IOP conference series: materials science and engineering, 1006(1), 012018. IOP Publishing, DOI: 10.1088/1757-899X/1006/1/012018. Go to original source...
    16. Lyman, K., and Bertini, R. L. (2008). Using Travel Time Reliability Measures to Improve Regional Transportation Planning and Operation. Journal of the Transportation Research Board, 2046, 1-10, https://doi.org/10.3141/2046-01 Go to original source...
    17. Mohanty, M., & Pratim Dey, P. (2019). Major stream delay under limited priority conditions. Journal of Transportation Engineering, Part A: Systems, 145(3), 05018005, https://doi.org/10.1061/JTEPBS.0000224 Go to original source...
    18. Mohanty, M., Raj, Y., Rout, S., Tiwari, U., Roy, S., & Samal, S. R. (2021). Operational effects of speed breakers: a case study in India. European Transport, 81(1), https://doi.org/10.48295/ET.2021.81.1 Go to original source...
    19. Mohanty, M., Sarkar, B., Pattanaik, M. L., Samal, S. R., & Gorzelańczyk, P. (2023). Development of Congestion Severity Index for Uncontrolled Median Openings Utilising Fundamental Traffic Parameters and Clustering Technique: A Case Study in India. International Journal of Intelligent Transportation Systems Research, 1-12. https://doi.org/10.1007/s13177-023-00365-1 Go to original source...
    20. Mohapatra, S. S., & Dey, P. P. (2015). Lateral placement of U-turns at median openings on six-lane divided urban roads. Transportation letters, 7(5), 252-263, https://doi.org/10.1179/1942787514Y.0000000052. Go to original source...
    21. Mohapatra, S. S., Bhuyan, P. K., & Rao, K. V. (2012). Genetic algorithm fuzzy clustering using GPS data for defining level of service criteria of urban streets. Retrieved from http://hdl.handle.net/10077/8187
    22. Monteserin, A. (2018). Potholes vs. Speed Bumps: A Multivariate Time Series Classification Approach. In UMCit@ KDD (pp. 36-40), Retrieved from https://ceur-ws.org/Vol-2227/KDD-UMCit2018-Paper4.pdf
    23. Padiath, A., Vanajakshi, L., Subramanian, S. C., & Manda, H. (2009, October). Prediction of traffic density for congestion analysis under Indian traffic conditions. In 2009 12th international IEEE conference on intelligent transportation systems (pp. 1-6). IEEE. https://doi.org/10.1109/ITSC.2009.5309716 Go to original source...
    24. Pau, M., and Angius, S. (2001). Do speed bumps really decrease traffic speed? An Italian experience. Accident Analysis & Prevention, 33(5), 585-597. https://doi.org/10.1016/S0001-4575(00)00070-1 Go to original source...
    25. Pollard, K. S., & van der Laan, M. J. (2002). Statistical inference for simultaneous clustering of gene expression data. Mathematical Biosciences, 176(1), 99-121. https://doi.org/10.1016/S0025-5564(01)00116-X Go to original source...
    26. Rao, A. M., and Rao, K. R. (2012). Measuring urban traffic congestion-a review. International Journal for Traffic and Transport Engineering, 2 (4), DOI: http://dx.doi.org/10.7708/ijtte.2012.2(4).01 Go to original source...
    27. Reed, T. and Kidd, J. (2019). INRIX Global Traffic Scorecard. Report, INRIX. Retrieved from http://inrix.com/scorecard/
    28. Reserve Bank of India, Government of India, DBR.RRB.BC.No.36/31.01.002/2016-17. Retrieved from https://www.rbi.org.in/commonman/Upload/English/Notification/PDFs/NOTI134AA16112016.PDF
    29. Rousseeuw, P. J. (1987). Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of computational and applied mathematics, 20, 53-65, https://doi.org/10.1016/0377-0427(87)90125-7 Go to original source...
    30. Samal, S. R., & Das, A. K. (2020). Evaluation of traffic congestion parameters under heterogeneous traffic condition: A case study on Bhubaneswar city. In Transportation Research: Proceedings of CTRG 2017 (pp. 675-684). Springer Singapore, https://doi.org/10.1007/978-981-32-9042-6_53 Go to original source...
    31. Samal, S. R., Kumar, P. G., Santhosh, J. C., & Santhakumar, M. (2020, December). Analysis of traffic congestion impacts of urban road network under Indian condition. In IOP conference series: materials science and engineering (Vol. 1006, No. 1, p. 012002). IOP Publishing, DOI: 10.1088/1757-899X/1006/1/012002. Go to original source...
    32. Samal, S. R., Mohanty, M., & Biswal, D. R. (2022a). A review of effectiveness of speed reducing devices with focus on developing countries. Transactions on transport sciences, 13(1), DOI: 10.5507/tots.2021.018. Go to original source...
    33. Samal, S. R., Mohanty, M., & Biswal, D. R. (2022b). Operational Effectiveness of Speed Humps in Urban Areas-A Review. In Recent Developments in Sustainable Infrastructure (ICRDSI-2020)-GEO-TRA-ENV-WRM: Conference Proceedings from ICRDSI-2020 Vol. 2 (pp. 841-850). Singapore: Springer Singapore, https://doi.org/10.1007/978-981-16-7509-6_66 Go to original source...
    34. Samal, S. R., Mohanty, M., & Gorzelańczyk, P. (2023). Exploring Traffic Congestion and Improving Travel Time Reliability Measures in Heterogeneous Traffic Environments: A Focus on Developing Countries. Communications, 25(4), D71-D82, https://doi.org/10.26552/com.C.2023.074 Go to original source...
    35. Samal, S. R., Mohanty, M., & Gorzelańczyk, P. (2024). PERCEPTION BASED LEVEL OF SERVICE FOR SPEED HUMPS IN MIXED TRAFFIC CONDITIONS-A CASE STUDY IN INDIA. Komunikįcie, 26(1), https://doi.org/10.26552/com.C.2024.010 Go to original source...
    36. Samal, S. R., Mohanty, M., & Santhakumar, S. M. (2021). Adverse effect of congestion on economy, health and environment under mixed traffic scenario. Transportation in Developing Economies, 7(2),15. https://doi.org/10.1007/s40890-021-00125-4 Go to original source...
    37. Samal, S. R., Mohanty, M., & Selvaraj, M. S. (2022). Assessment of Traffic Congestion under Indian Environment-a Case Study. Communications, 24(4), D174-D182, https://doi.org/10.26552/com.C.2022.4.D174-D182 Go to original source...
    38. Samal, S. R., Mohanty, M., Gireesh Kumar, P., & Santhakumar M, M. (2022, May). Evaluation of Functional Effectiveness of Speed Humps in Accordance to IRC Specifications. In RecentAdvances in Civil Engineering: Proceedings of the 2nd International Conference on Sustainable Construction Technologies and Advancements in Civil Engineering (ScTACE 2021) (pp. 105-114). Singapore: Springer Nature Singapore, https://doi.org/ 10.1007/978-981-19-0189-8_9 Go to original source...
    39. Sarstedt, M., & Mooi, E. (2014). A concise guide to market research. The Process, Data, 12, https://doi.org/10.1007/978-3-662-56707-4 Go to original source...
    40. Wang, J., Dixon, K., Li, H., and Ogle, J. (2005). Normal deceleration behaviour of passenger vehicles starting from rest at all way stop controlled intersections, https://doi.org/10.3141/1883-18 Go to original source...
    41. Wortman, R. H., and Fox, T.C. (1994). An Evaluation of Vehicle Deceleration Profiles, Journal of Advanced Transportation, 28(3): 203-215, http://dx.doi.org/10.1002/atr.5670280303 Go to original source...
    42. Yadav, J., & Sharma, M. (2013). A Review of K-mean Algorithm. Int. J. Eng. Trends Technol, 4(7), 2972-2976, Retrieved from https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=65f1232434c5eeddd9e658db7ae0dd5c47b6e20d

    This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content