Evaluating overtaking and filtering maneuver of motorcyclists and car drivers using advanced trajectory data analysis

References

• Agarwal, A., & Lämmel, G. (2016). Modeling seepage behavior of smaller vehicles in mixed traffic conditions using an agent based simulation. Transportation in Developing Economies, 2(2), 8. https://doi.org/10.1007/s40890-016-0014-9
   Web of Science ®Google Scholar
• Ambarwati, L., Pel, A. J., Verhaeghe, R., & van Arem, B. (2014). Empirical analysis of heterogeneous traffic flow and calibration of porous flow model. Transportation Research Part C: Emerging Technologies, 48, 418–436. https://doi.org/10.1016/j.trc.2014.09.017
   Web of Science ®Google Scholar
• Barmpounakis, E. N., Vlahogianni, E. I., & Golias, J. C. (2016a). Modeling cooperation and powered-two wheelers short-term strategic decisions during overtaking in urban arterials. International Journal of Transportation Science and Technology, 5(4), 227–238. https://doi.org/10.1016/j.ijtst.2016.11.001
   Google Scholar
• Barmpounakis, E. N., Vlahogianni, E. I., & Golias, J. C. (2016b). Vision-based multivariate statistical modeling for powered two-wheelers maneuverability during overtaking in urban arterials. Transportation Letters, 8(3), 1–10. https://doi.org/10.1179/1942787515Y.0000000020
   Web of Science ®Google Scholar
• Beanland, V., Pammer, K., Sledziowska, M., & Stone, A. (2015). Drivers’ attitudes and knowledge regarding motorcycle lane filtering practices. Proceedings of the 2015 Australasian Road Safety Conference, 1–11.
   Google Scholar
• Broughton, P. S., Fuller, R., Stradling, S., Gormley, M., Kinnear, N., O’dolan, C., & Hannigan, B. (2009). Conditions for speeding behaviour: A comparison of car drivers and powered two wheeled riders. Transportation Research Part F: Traffic Psychology and Behaviour, 12(5), 417–427. https://doi.org/10.1016/j.trf.2009.07.001
   Web of Science ®Google Scholar
• Burges, C. J. C. (1998). A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. https://doi.org/10.1023/A:1009715923555
   Web of Science ®Google Scholar
• Cakmakyapan, S., & Goktas, A. (2013). A comparison of binary logit and probit models with a simulation study. Journal of Social and Economic Statistics, 2(1), 1–17.
   Google Scholar
• Clabaux, N., Fournier, J.-Y., & Michel, J.-E. (2014). Powered two-wheeler drivers’ risk of hitting a pedestrian in towns. Journal of Safety Research, 51, 1–5. https://doi.org/10.1016/j.jsr.2014.07.002
   PubMed Web of Science ®Google Scholar
• Clabaux, N., Fournier, J.-Y., & Michel, J.-E. (2017). Powered two-wheeler riders’ risk of crashes associated with filtering on urban roads. Traffic Injury Prevention, 18(2), 182–187. https://doi.org/10.1080/15389588.2016.1225298
   PubMed Web of Science ®Google Scholar
• Das, S., & Maurya, A. K. (2019). Modeling maneuverability of motorized two-wheelers during filtering in urban roads. Transportation Research Record: Journal of the Transportation Research Board, 2673(5), 637–647. https://doi.org/10.1177/0361198119842818
   Web of Science ®Google Scholar
• Das, S., & Maurya, A. K. (2020). Pore acceptance predictions of motorised Two-Wheelers during filtering at urban Mid-Block sections. Journal of Intelligent Transportation Systems, 24(4), 352–364. https://doi.org/10.1080/15472450.2020.1735385
   Web of Science ®Google Scholar
• Dehwko, C. K. (2019). Kinovea. http://www.kinovea.org
   Google Scholar
• DfT, D. for T. (2022). The Highway Code. https://www.gov.uk/guidance/the-highway-code/using-the-road-159-to-203
   Google Scholar
• George Oketch, T. (2000). New modeling approach for mixed-traffic streams with nonmotorized vehicles. Transportation Research Record: Journal of the Transportation Research Board, 1705(1), 61–69. https://doi.org/10.3141/1705-10
   Google Scholar
• Hernández-Lobato, J. M. (2010). Balancing flexibility and robustness in machine learning: Semi-parametric methods and sparse linear models. Doctoral Dissertation, Department of Computer Science, University of Autonoma, Madrid, Spain.
   Google Scholar
• Hublart, A., & Durand, J.-F. (2012). Risks for powered two-wheelers and filtering between lanes on urban motorways. In European Transport Conference 2012Association for European Transport (AET). Transportation Research Board.
   Google Scholar
• Huth, V., Füssl, E., & Risser, R. (2014). Motorcycle riders’ perceptions, attitudes and strategies: Findings from a focus group study. Transportation Research Part F: Traffic Psychology and Behaviour, 25, 74–85. https://doi.org/10.1016/j.trf.2014.05.004
   Web of Science ®Google Scholar
• Karlaftis, M. G., & Vlahogianni, E. I. (2011). Statistical methods versus neural networks in transportation research: Differences, similarities and some insights. Transportation Research Part C: Emerging Technologies, 19(3), 387–399. https://doi.org/10.1016/j.trc.2010.10.004
   Web of Science ®Google Scholar
• Lee, T.-C., Polak, J. W., & Bell, M. G. H. (2008). Trajectory Extractor user manual version 1.0. In Centre for Transport Studies, Imperial College London, United Kingdom. www. cts. cv. ic. ac. uk/documents/publications/iccts01155. pdf. Accessed July (Vol. 1).
   Google Scholar
• Lin, M.-R., & Kraus, J. F. (2009). A review of risk factors and patterns of motorcycle injuries. Accident; Analysis and Prevention, 41(4), 710–722. https://doi.org/10.1016/j.aap.2009.03.010
   PubMed Web of Science ®Google Scholar
• Mannering, F. L., & Grodsky, L. L. (1995). Statistical analysis of motorcyclists’ perceived accident risk. Accident; Analysis and Prevention, 27(1), 21–31. https://doi.org/10.1016/0001-4575(94)00041-j
   PubMed Web of Science ®Google Scholar
• Mikhail, E. M., Bethel, J. S., & McGlone, J. C. (2001). Mathematics for photogrammetry. In Introduction to modern photogrammetry (pp. 351–386). John Wiley & Sons, Inc.
   Google Scholar
• MoRTH. (2017). Motor vehicle driving regulation. https://morth.nic.in/sites/default/files/Motor-Vehicle-Driving-Regulation-2017.pdf
   Google Scholar
• MoRTH. (2021). Road accidents in India. Transportation Research Wing. Ministry of Road Transport and Highways, Government of India.
   Google Scholar
• Munigety, C. R., & Mathew, T. V. (2016). Towards behavioral modeling of drivers in mixed traffic conditions. Transportation in Developing Economies, 2(1), 6. https://doi.org/10.1007/s40890-016-0012-y
   Web of Science ®Google Scholar
• Nguyen, L., Hanaoka, S., & Kawasaki, T. (2012). Describing non-lane-based motorcycle movements in motorcycle-only traffic flow. Transportation Research Record: Journal of the Transportation Research Board, 2281(1), 76–82. https://doi.org/10.3141/2281-10
   Web of Science ®Google Scholar
• Nikias, V. A., Vlahogianni, E. I., Lee, T. C., & Golias, J. C. (2012). Determinants of powered two-wheelers virtual lane width in urban arterials. IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 4943(0), 1205–1210. https://doi.org/10.1109/ITSC.2012.6338619
   Google Scholar
• Pradhan, A. (2011). Network traffic classification using support vector machine and artificial neural network. International Journal of Computer Applications, 8, 8–12.
   Google Scholar
• Qader, K., Adda, M., & Al-Kasassbeh, M. (2017). Comparative analysis of clustering techniques in network traffic faults classification. International Journal of Innovative Research in Computer and Communication Engineering, 5(4), 6551–6563.
   Google Scholar
• Rice, T., Troszak, L., & Erhardt, T. (2015). Motorcycle lane-splitting and safety in California. University of California.
   Google Scholar
• Saini, H. K., & Biswas, S. (2021). Estimating lateral placement and lane indiscipline of urban mixed traffic of a developing country: An ANN-assisted approach. Canadian Journal of Civil Engineering, 48(11), 1571–1581. https://doi.org/10.1139/cjce-2020-0250
   Web of Science ®Google Scholar
• Soni, R., Vasudevan, V., & Dutta, B. (2020). Analysis of overtaking patterns of Indian drivers with data collected using a LiDAR. Transportation Research Part F: Traffic Psychology and Behaviour, 74, 139–150. https://doi.org/10.1016/j.trf.2020.08.016
   Web of Science ®Google Scholar
• Sugandhi, U., & Chunchu, M. (2017). Analysis and modeling of two-wheeler-overtaking-LMV maneuver in heterogeneous traffic stream. Transportation in Developing Economies, 3(2), 1–7. https://doi.org/10.1007/s40890-017-0046-9
   Google Scholar
• Theofilatos, A., & Yannis, G. (2015). A review of powered-two-wheeler behaviour and safety. International Journal of Injury Control and Safety Promotion, 22(4), 284–307. https://doi.org/10.1080/17457300.2014.908224
   PubMed Web of Science ®Google Scholar
• Tohka, J., & van Gils, M. (2021). Evaluation of machine learning algorithms for Health and Wellness applications: A tutorial. Computers in Biology and Medicine, 132, 104324. https://doi.org/10.1016/j.compbiomed.2021.104324
   PubMed Web of Science ®Google Scholar
• Troutbeck, R. J. (1981). Overtaking behaviour on Australian two-lane rural highways. Australian Road Research Board, Special Report SR20.
   Google Scholar
• Tu, J. V. (1996). Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology, 49(11), 1225–1231. https://doi.org/10.1016/s0895-4356(96)00002-9
   PubMed Web of Science ®Google Scholar
• Vanajakshi, L., & Rilett, L. R. (2004). A comparison of the performance of artificial neural networks and support vector machines for the prediction of traffic speed. IEEE Intelligent Vehicles Symposium, 2004, 194–199. https://doi.org/10.1109/IVS.2004.1336380
   Google Scholar
• Vlahogianni, E. I. (2014). Powered-Two-Wheelers kinematic characteristics and interactions during filtering and overtaking in urban arterials. Transportation Research Part F: Traffic Psychology and Behaviour, 24, 133–145. https://doi.org/10.1016/j.trf.2014.04.004
   Web of Science ®Google Scholar
• Vlahogianni, E. I., Karlaftis, M. G., & Orfanou, F. P. (2012). Modeling the effects of weather and traffic on the risk of secondary incidents. Journal of Intelligent Transportation Systems, 16(3), 109–117. https://doi.org/10.1080/15472450.2012.688384
   Web of Science ®Google Scholar
• Vlahogianni, E. I., Yannis, G., & Golias, J. C. (2013). Critical power two wheeler driving patterns at the emergence of an incident. Accident; Analysis and Prevention, 58, 340–345. https://doi.org/10.1016/j.aap.2012.12.026
   PubMed Web of Science ®Google Scholar
• WHO. (2018). Global status report on road safety 2018. World Health Organization.
   Google Scholar
• Wilks, D. S. (2011). Statistical methods in the atmospheric sciences (Vol. 100). Academic press.
   Google Scholar
• Woldemariam, W. (2022). A framework for transportation infrastructure cost prediction: A support vector regression approach. Transportation Letters, 14(9), 997–1003. https://doi.org/10.1080/19427867.2021.1985895
   Web of Science ®Google Scholar
• Yu, B., Wang, Y. T., Yao, J. B., & Wang, J. Y. (2016). A comparison of the performance of ANN and SVM for the prediction of traffic accident duration. Neural Network World, 26(3), 271–287. https://doi.org/10.14311/NNW.2016.26.015
   Web of Science ®Google Scholar