Pore acceptance predictions of motorised Two-Wheelers during filtering at urban Mid-Block sections

References

• ACEM (Association des Constructeurs Européens de Motocycles). (2009). In-depth investigations of accidents involving powered two-wheelers (MAIDS). Retrieved from http://www.maids-study.eu/pdf/MAIDS2.pdf.
   Google Scholar
• Agarwal, A., & Lämmel, G. (2016). Modeling seepage behvaiour of smaller vehicles in mixed traffic conditions using an agent based simulation. Transportation in Developing Economies, 2(2), 1–12. doi:10.1007/s40890-016-0014-9
   Web of Science ®Google Scholar
• Albalate, D., & Fernández-Villadangos, L. (2010). Motorcycle injury severity in Barcelona: The role of vehicle type and congestion. Traffic Injury Prevention, 11(6), 623–631. doi:10.1080/15389588.2010.506932
   PubMed Web of Science ®Google Scholar
• AltendorfFlemisch, F. (2014). Prediction of driving behavior in cooperative guidance and control: A first game-theoretic approach. Madgeburg: CogSys.
   Google Scholar
• Ambarwati, L. A., Pel, J., & Verhaeghe, R. (2014). Empirical analysis of heterogeneous traffic flow and calibration of porous flow model. Transportation Research Part C: Emerging Technologies, 9, 418–436. doi:10.1016/j.trc.2014.09.017
   Google Scholar
• Amin, H. J., & Maurya, A. K. (2015). A review of critical gap estimation approaches at uncontrolled intersection in case of heterogeneous traffic conditions. Journal of Transport Literature, 9(3), 5–9. doi:10.1590/2238-1031.jtl.v9n3a1
   Google Scholar
• Ashalatha, R., & Chandra, S. (2011). Critical gap through clearing behavior of drivers at unsignalised intersections. KSCE Journal of Civil Engineering, 15(8), 1427–1434. doi:10.1007/s12205-011-1392-5
   Web of Science ®Google Scholar
• Brilon, W., Koenig, R., & Troutbeck, R. (1999). Useful estimation procedures for critical gaps. Transportation Research Part A: Policy and Practice, 33(3-4), 161–186. doi:10.1016/S0965-8564(98)00048-2
   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), 121–974.
   Web of Science ®Google Scholar
• Cafiso, S., La Cava, G., & Pappalardo, G. (2012). A logistic model for Powered Two-Wheelers crash in Italy. Procedia - Social and Behavioral Sciences, 53, 880–889. doi:10.1016/j.sbspro.2012.09.937
   Google Scholar
• Clarke, D. D., Ward, P., Bartle, C., et al. (2004). In-depth study of motorcycle accidents. London, UK: Department for Transport.
   Google Scholar
• Daganzo, C. F. (1981). Estimation of gap acceptance parameters within and across the population from direct roadside observation. Transportation Research Part B: Methodological, 15(1), 1–15. doi:10.1016/0191-2615(81)90042-4
   Web of Science ®Google Scholar
• Das, S., & Maurya, A. K. (2018). Modelling of motorised two-wheelers: A review of the literature. Transport Reviews, 38(2), 209–231. doi:10.1080/01441647.2017.1317049
   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. doi:10.1177/0361198119842818
   Web of Science ®Google Scholar
• FEMA. (2009). A European agenda for motorcycle safety: The motorcyclists’ point of view (Technical report). Federation of European Motorcyclists Associations.
   Google Scholar
• Fung, G. S., Yung, N. H., & Pang, GK. (2003). Camera calibration from road lane markings. Optical Engineering, 42(10), 2967–2977. doi:10.1117/1.1606458
   Web of Science ®Google Scholar
• Gattis, JL., & Low, ST. (1999). Gap acceptance at atypical stop-controlled intersections. Journal of Transportation Engineering, 125(3), 201–207. doi:10.1061/(ASCE)0733-947X(1999)125:3(201)
   Google Scholar
• Greenshields, B. D., Shapiro, D., & Ericksen, E. L. (1947). Traffic performance at urban street intersections (Technical Report No. 1.Bureau of Highway Traffic). Yale University.
   Google Scholar
• Gryllias, KC., & Antoniadis, IA. (2012). A Support vector machine approach based on physical model training for rolling element bearing fault detection in industrial environments. Engineering Applications of Artificial Intelligence, 25(2), 326–344. doi:10.1016/j.engappai.2011.09.010
   Web of Science ®Google Scholar
• Gunn, S. R. (1998). Support Vector Machines for Classification and Regression (Technical Report). Department of Electrical and Computer Science, University of Southampton.
   Google Scholar
• Hamed, M. M., Easa, SM., & Batayneh, RR. (1997). Disaggregate gap acceptance model for unsignalized T-Intersections. Journal of Transportation Engineering, 123(1), 36–42. doi:10.1061/(ASCE)0733-947X(1997)123:1(36)
   Google Scholar
• Hewitt, R. H. (1985). A comparison between some methods of measuring critical gap. Traffic Engineering & Control, 26(1), 13–22.
   Google Scholar
• Hublart, A., & Durand, J. –F. (2012). Risks for powered two-wheelers and filtering between lanes on urban motorways. Paper presented at European Transport Conference 2012, Glasgow, Scotland.
   Google Scholar
• Hurt, H. H., Ouellet, J. V., & Thom, D. R. (1981). Motorcycle accident cause factors and identification of countermeasures. DOT-HS-5-01160, Volume 1. National Highway Traffic Safety Administration, US Department of Transportation.
   Google Scholar
• Lee, T.-C. (2008). An Agent-Based Model to Simulate Motorcycle Behavior in Mixed Traffic Flow. United Kingdom: Imperial College London.
   Google Scholar
• Miller, A. J. (1972). Nine estimators of gap-acceptance parameters. In G. Newell, (Ed.) International Symposium on the Theory of Traffic Flow and Transportation (pp. 215–235). Amsterdam: Elsevier.
   Google Scholar
• Miller, A. J. (1974). A note on the analysis of gap-acceptance in traffic. Applied Statistics, 23(1), 66–73. doi:10.2307/2347055
   Google Scholar
• Mohan, M., & Chandra, S. (2016). Review and Assessment of Techniques for Estimating Critical Gap at Two-way Stop-controlled Intersections. European Transport-Trasporti Europei, 61(8), 1–18.
   Google Scholar
• Naranjo, J. E., Jiménez, F., Anaya, J. J., Talavera, E., & Gómez, O. (2017). Application of vehicle to another entity (V2X) communications for motorcycle crash avoidance. Journal of Intelligent Transportation Systems, 21(4), 285–295. doi:10.1080/15472450.2016.1247703
   Web of Science ®Google Scholar
• Nikias, V. A., Vlahogianni, E. I., Lee, T. C., et al. (2012). Determinants of powered two-wheelers virtual lane width in urban arterials. In Intelligent Transportation Systems (ITSC), 2012 15th International IEEE Conference, pp. 1205–1210, IEEE.
   Google Scholar
• Pawar, D. S., Patil, G R., Chandrasekharan, A., & Upadhyaya, S. (2015). Classification of gaps at uncontrolled intersections and midblock crossings using support vector machines. Transportation Research Record: Journal of the Transportation Research Board, 2515(1), 26–33. doi:10.3141/2515-04
   Web of Science ®Google Scholar
• Radwan, A. E., & Sinha, K. C. (1980). At Stop Controlled Intersections On Multi- Lane Divided Highways. ITE Journal, 3, 38–44.
   Google Scholar
• Raff, M. S., & Hart, J. W. (1950). A volume warrant for urban stop signs. Eno Foundation for Highway Traffic Control, Saugatack, Conn, USA.
   Google Scholar
• Rogé, J., Ferretti, J., & Devreux, G. (2010). Sensory Conspicuousness of Powered Two-wheelers during Filtering Manoeuvre, According to the Age of the Car Driver. Le Travail Humain, 73(1), 7–30. doi:10.3917/th.731.0007
   Google Scholar
• Sexton, B., Fletcher, J., & Hamilton, K. (2004). Motorcycle accidents and casualties in Scotland 1992–2002. Research Findings No. 194/2004. Transport Planning Group, Edinburgh, UK.
   Google Scholar
• Solberg, P. (1964). A Study of LAG and GAP Acceptances at Stop-Controlled Intersections. Joint Highway Research Project, Purdue University.
   Google Scholar
• Vapnik, V. N. (1998). Statistical Learning Theory (Vol. 2). New York, NY: John Wiley and Sons.
   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. doi:10.1016/j.trf.2014.04.004
   Web of Science ®Google Scholar
• Wilks, D. S. (2011). Statistical Methods in the Atmospheric Sciences (Vol. 100, 3rd ed.) Boston, MA: Academic Press.
   Google Scholar