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Driver behavior modeling at uncontrolled intersections under Indian traffic conditions

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Abstract

Unsignalized intersections lack explicit traffic management mechanisms, which makes them vulnerable to regular disputes and subsequent vehicle crashes. In India, unsignalized intersections predominantly function as uncontrolled intersections. At these crossings, drivers in developing nations such as India fail to yield to movements with a higher priority, increasing vehicle collisions. The objective of the study was to assess the driving behavior of minor roads, considering their aggressive tendency, at uncontrolled intersections. Videographic data were collected at six intersections in tier 2 cities in India. The binary logit model used for minor road right-turning vehicles found that gap acceptance behavior is influenced by vehicle type light commercial vehicle, lag, temporal gap, acceleration/deceleration of the vehicle, and conflicting vehicle speed. In contrast, multilayer perceptron shows that the temporal gap, acceleration, deceleration, and vehicle in line are important parameters that influence driver decisions to accept or reject the gap at an uncontrolled intersection. Because drivers, vehicles, and traffic flow factors all contribute to total traffic behavior, analyzing such crossings is difficult. The correct prediction by the logit and multilayer perceptron models ranges from 68.33 to 66.6% for minor road right turns at uncontrolled intersections. Since planning and decisions for interventions aimed at enhancing road safety depend on a system’s capacity for prediction, both logit and multilayer perceptron models may generally be useful tools for transportation authorities.

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Data availability

Due to the sensitive nature of the data, information created during and/or analyzed during the current study is available from the corresponding author on reasonable request to Bonafide researchers.

References

  1. MoRTH (2022) Road accidents in India. Ministry of Road Transport & Highways, Transport, Research Wing, New Delhi

  2. Amin HJ, Maurya AK (2015) A review of critical gap estimation approaches at uncontrolled intersection in case of heterogeneous traffic conditions. J Trans Lit. 9(3):5–9. https://doi.org/10.1590/2238-1031.jtl.v9n3a1

    Article  Google Scholar 

  3. Ashalatha R, Chandra S (2011) Critical gap through clearing behavior of drivers at unsignalised intersections. KSCE J Civ Eng 15(8):1427–1434. https://doi.org/10.1007/s12205-011-1392-5

    Article  Google Scholar 

  4. Dutta M, Ahmed MA (2018) Gap acceptance behavior of drivers at uncontrolled T-intersections under mixed traffic conditions. J Modern Trans 26(2):119–132. https://doi.org/10.1007/s40534-017-0151-9

    Article  Google Scholar 

  5. Goyani J, Pawar N, Gore N, Jain M, Arkatkar S (2019) Investigation of traffic conflicts at unsignalized intersection for reckoning crash probability under mixed traffic conditions. J Eastern Asia Soc Trans Stud 13:2091–2110

    Google Scholar 

  6. Mohan M, Chandra S (2018) Three methods of PCU estimation at unsignalized intersections. Trans Lett 10(2):68–74. https://doi.org/10.1080/19427867.2016.1190883

    Article  Google Scholar 

  7. Singh D, Das P, Ghosh I (2023) Surrogate safety assessment of traffic facilities under ordered and disordered traffic condition: systematic literature review. KSCE J Civ Eng 27:5008–5029. https://doi.org/10.1007/s12205-023-0979-y

    Article  Google Scholar 

  8. Srinivasula SR et al (2020) Developing proximal safety indicators for assessment of unsignalized intersection–a case study in Surat city. Trans Lett 12(5):303–315. https://doi.org/10.1080/19427867.2019.1589162

    Article  Google Scholar 

  9. Pawar DS, Patil GR (2017) Minor-street vehicle dilemma while maneuvering at unsignalized intersections. J Trans Eng, Part A: Syst 143(8):04017039. https://doi.org/10.1061/JTEPBS.0000066

    Article  Google Scholar 

  10. Nagalla R, Pothuganti P, Pawar DS (2017) Analyzing gap acceptance behavior at unsignalized intersections using support vector machines, decision tree and random forests. Proc Comput Sci 109(2016):474–481. https://doi.org/10.1016/j.procs.2017.05.312

    Article  Google Scholar 

  11. Choudhary P, Velaga NR (2019) Gap acceptance behavior at unsignalized intersections: effects of using a phone and a music player while driving. Traffic Inj Prev 20(4):372–377. https://doi.org/10.1080/15389588.2019.1591619

    Article  Google Scholar 

  12. Vinchurkar S et al (2020) Gap acceptance behaviour of vehicles at unsignalized intersection in urban area. Lect Notes Civ Eng 45:545–556. https://doi.org/10.1007/978-981-32-9042-6_43

    Article  Google Scholar 

  13. Li Y et al (2021) Understanding gap acceptance behavior at unsignalized intersections using naturalistic driving study data. Transp Res Rec 2675(9):1345–1358. https://doi.org/10.1177/03611981211007140

    Article  Google Scholar 

  14. Hamed MM, Easa SM, Batayneh RR (1997) DISAGGREGATE GAP-ACCEPTANCE MODEL FOR UNSIGNALIZED T-INTERSECTIONS. J Trans Eng 123(1):36–42

    Article  Google Scholar 

  15. Zhou H, Ivan JN, Gårder PE, Ravishanker N (2017) Gap acceptance for left turns from the major road at unsignalized intersections. Transport 32(3):252–261. https://doi.org/10.3846/16484142.2014.933445

    Article  Google Scholar 

  16. Vasudevan V, Mehta M, Dutta B (2020) ‘Pedestrian temporal gap acceptance behavior at unsignalized intersections in Kanpur. India’, Trans Res Part F: Traff Psychol Behav 74:95–103. https://doi.org/10.1016/j.trf.2020.08.010

    Article  Google Scholar 

  17. Tian, Z., Troutbeck, R. and Kyte, M. (2000) ‘A further investigation on critical gap and follow-up time’, 4th International Symposium on Highway Capacity, (Trb 1997), pp. 397–408. Available at: http://gulliver.trb.org/publications/circulars/ec018/34_45.pdf

  18. Davis GA, Swenson T (2004) Field study of gap acceptance by left-turning drivers. Transp Res Rec 1899:71–75. https://doi.org/10.3141/1899-09

    Article  Google Scholar 

  19. Kaysi IA, Abbany AS (2007) Modelling aggressive driver behavior at unsignalized intersections. Accid Anal Prev 39(4):671–678. https://doi.org/10.1016/j.aap.2006.10.013

    Article  Google Scholar 

  20. Sangole JP, Patil GR (2014) Adaptive neuro-fuzzy interface system for gap acceptance behavior of right-turning vehicles at partially controlled T-intersections. J Modern Trans 22:235–243. https://doi.org/10.1007/s40534-014-0057-8

    Article  Google Scholar 

  21. Karthika PT, Koshi BI (2014) Gap acceptance behavior of drivers at T61 intersections. Int J Eng Res Technol 3(11):935–938

    Google Scholar 

  22. Patil GR, Pawar DS (2014) Temporal and spatial gap acceptance for minor road at uncontrolled intersections in India. Trans Res Rec 2461(1):129–136. https://doi.org/10.3141/2461-16

    Article  Google Scholar 

  23. Zegeer C (1977) Effectiveness of Green-Extension Systems at High-speed Intersections. Research Report 472. Bureau of Highways, Kentucky Department of Transportation.

  24. Mohan M, Chandra S (2021) Investigating the influence of conflicting flow ‘ s composition on critical gap under heterogeneous traffic conditions international journal of transportation investigating the influence of conflicting flow’s composition on critical gap under heterogene. Civ Eng Trans 10(4):393–401. https://doi.org/10.1016/j.ijtst.2021.01.004

    Article  Google Scholar 

  25. Bhatt K, Gore N, Shah J, Arkatkar S (2024) Drivers’ dilemma at high-speed unsignalized intersections. Trans Res Rec 2678(3):82–97. https://doi.org/10.1177/03611981231178813

    Article  Google Scholar 

  26. Khan T, Mohapatra SS (2023) Identification of spatial and temporal dilemma zone at mid-block median openings: a gap acceptance based approach. Trans Res Rec 2677(3):160–175. https://doi.org/10.1177/03611981221114118

    Article  Google Scholar 

  27. Pathivada BK, Vedagiri P (2022) Investigating dilemma zone boundaries for mixed traffic conditions using support vector machines. Trans Lett 14(4):378–384. https://doi.org/10.1080/19427867.2020.1870307

    Article  Google Scholar 

  28. Maurya AK, Amin HJ, Kumar A (2016) Estimation of critical gap for through movement at four leg uncontrolled intersection. Trans Res Proc 17:203–212. https://doi.org/10.1016/j.trpro.2016.11.076

    Article  Google Scholar 

  29. Bhatt K, Gore N, Shah J (2022) CRITICAL GAP ESTIMATION AND ITS IMPLICATION ON CAPACITY AND SAFETY OF HIGH-SPEED UN-SIGNALISED T-INTERSECTION UNDER HETEROGENEOUS TRAFFIC CONDITIONS. Communications 24(4):215–228

    Article  Google Scholar 

  30. Dutta M, Maddu K (2022) Studying aggressive clearing behavior of drivers at uncontrolled intersections under mixed traffic conditions. J Inst Eng India: Series A 103(2):639–645. https://doi.org/10.1007/s40030-022-00634-4

    Article  Google Scholar 

  31. Zhang G, Qi Y, Chen J (2016) Exploring factors impacting paths of left-turning vehicles from minor road approach at unsignalized intersections. Math Prob Eng 2016:1–9. https://doi.org/10.1155/2016/1305890

    Article  Google Scholar 

  32. Bonela SR, Kadali BR (2023) Analysis of right-turn vehicular driving paths at uncontrolled T-intersections. Int J Inj Contr Saf Promot 30(1):91–105. https://doi.org/10.1080/17457300.2022.2112236

    Article  Google Scholar 

  33. Bishop CM (2006). Pattern recognition and machine learning. springer.

  34. Goodfellow I, Bengio Y, Courville A, Bengio Y (2016). Deep learning (Vol. 1). MIT press Cambridge.

  35. Kim Y (2014). Convolutional Neural Networks for Sentence Classification. arXiv preprint arXiv:1408.5882.

  36. Brown LM, Jones SA, Miller PR (2017) Credit risk assessment using deep learning. Int J Financ Econ 22(2):195–210

    Google Scholar 

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Acknowledgements

The authors would like to thank the anonymous peer reviewers for their constructive feedback.

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Authors and Affiliations

  1. Department of Civil Engineering, Maulana Azad National Institute of Technology Bhopal, Bhopal, India

    Dungar Singh & Pritikana Das

  2. Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee, India

    Indrajit Ghosh

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  1. Dungar Singh
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  2. Pritikana Das
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  3. Indrajit Ghosh
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by DS, PD, and IG. The first draft of the manuscript was written by DS, PD, and IG, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Dungar Singh.

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Singh, D., Das, P. & Ghosh, I. Driver behavior modeling at uncontrolled intersections under Indian traffic conditions. Innov. Infrastruct. Solut. 9, 124 (2024). https://doi.org/10.1007/s41062-024-01425-5

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