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Acceleration models for two-wheelers and cars in mixed traffic: effect of unique vehicle-following interactions and driving regimes


Affiliations
1 Department of Civil Engineering, TKM College of Engineering, Kollam 691 005, India, India
2 Transportation Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India, India
 

Driving behaviour in mixed traffic conditions is chara­cterized by vehicle heterogeneity and lane-less movement. In such traffic conditions, the following response of a vehicle may be discontinuous and gets triggered when certain thresholds on relative speed and spacing with the leaders are crossed. In this context, the present study segments vehicular response into driving regimes using vehicle trajectory data based on relative speed and position. Acceleration models are formulated by featuring driving regimes and their interactions with mixed traffic attributes. These models are used to study the differences in the following behaviour of two-wheelers and cars. The proposed models capture the asymmetric behaviour and account for differences across driving regimes, resulting in a signi­ficantly better fit and realistic representation of mixed traffic.

Keywords

Acceleration Models, Driving Regimes, Mixed Traffic Attributes, Local Area Concentration, Vehicle Trajectory Extraction
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  • Raju, N., Kumar, P., Arkatkar, S. S. and Joshi, G., Application of trajectory data for investigating vehicle behavior in mixed traffic environment. Transp. Res. Rec., 2018, 2672(43), 122–133.

  • Kan, X., Ramezani, H. and Benekohal, R., Calibration of VISSIM for freeway work zones with time varying capacity. Presented at the 93rd Transportation Research Board Annual Meeting, Washington, DC, USA, 2014.

  • Menneni, S., Sun, C. and Vortisch, P., An integrated microscopic and macroscopic calibration for psychophysical car following models. Presented at the 88th Transportation Research Board Annual Meeting, Washington, DC, USA, 2009.

  • Sarvi, M. and Ejtemai, O., Exploring heavy vehicles’ car-following behaviour. In Proceedings of the 34th Australasian Transport Research Forum, Adelaide, Australia, 2011.

  • Ossen, S. and Hoogendoorn, S. P., Heterogeneity in car-following behavior: theory and empirics. Transp. Res. Part C, 2011, 19(2), 182–195.

  • Manjunatha, P., Vortisch, P. and Mathew, T., Methodology for the calibration of VISSIM in mixed traffic. Presented at the 92nd Transportation Research Board Annual Meeting, Washington, DC, USA, 2013.

  • Pipes, L., An operational analysis of traffic dynamics. J. Appl. Phys., 1953, 24, 274–287.

  • Herman, R. and Weiss, G., Comments on the highway-crossing problem. Oper. Res., 1961, 9(6), 828–840.

  • Forbes, T. W., Human factor considerations in traffic flow theory. Highway Research Record, 1963, 15, 60–66.

  • Gazis, D. C., Herman, R. and Potts, R. B., Car-following theory of steady-state traffic flow. Oper. Res., 1959, 7(4), 499–505.

  • Miller, A. J., Nine estimators of gap-acceptance parameters. In Proceedings of the 5th International Symposium on the Theory of Traffic Flow and Transportation, New York, USA, 1972.

  • Gipps, P. G., A behavioural car following model for computer simulation. Transp. Res. Part B, 1981, 15, 101–115.

  • Gipps, P., A model for the structure of lane-changing decisions. Transp. Res. Part B, 1986, 20(5), 403–414.

  • Mahmassani, H. and Sheffi, Y., Using gap sequences to estimate gap acceptance functions. Transp. Res. Part B, 1981, 15(3), 143– 148.

  • Wiedemann, R. and Reiter, U., Microscopic traffic simulation: the simulation system mission, background, and actual state. Technical report, CEC project ICARUS (V1052) final report, no. 2, Brussels, Belgium, 1992.

  • Kita, H., Effects of merging lane length on the merging behavior at expressway on-ramps. In 12th International Symposium on the Theory of Traffic Flow and Transportation. Berkeley, California, USA, 1993.

  • Nagel, K., Wolf, D. E., Wagner, P. and Simon, P., Two-lane traffic rules for cellular automata: a systematic approach. Phys. Rev. E, 1997, 58(2), 1425–1437.

  • Choudhury, C. F., Ramanujam, V. and Ben-Akiva, M. E., Modeling acceleration decisions for freeway merges. Transp. Res. Rec., 2009, 2124(01), 45–57.

  • Toledo, T., Driving behaviour: models and challenges. Transp. Rev., 2007, 27(1), 65–84.

  • Brackstone, M. and McDonald, M., Car-following: a historical review. Transp. Res. Part F, 1999, 2(4), 181–196.

  • Madhu, K., Srinivasan, K. K. and Sivanandan, R., Following behavior in mixed traffic: effects of vehicular interactions, local area concentration and driving regimes. Int. J. Eng. Res. Technol., 2020, 13(6), 1353–1368.

  • Michaels, R., Perceptual factors in car following. In Proceedings of the Second International Symposium on the Theory of Road Traffic Flow, Organisation for Economic Co-operation and Development, Paris, 1963.

  • Lee, D., A theory of visual control of braking based on information about time to collision. Perception, 1976, 5(4), 437–459.

  • Aghabayk, K., Sarvi, M., Young, W. and Kautzsch, L., A novel methodology for evolutionary calibration of VISSIM by multithreading. In Australasian Transport Research Forum Proceedings, Brisbane, Queensland, Australia, 2013.

  • Jie, L., Zuylen, H., Chen, Y., Viti, F. and Wilmink, I., Calibration of a microscopic simulation model for emission calculation. Transp. Res. Part C, 2013, 31, 172–184.

  • Zheng, L., Jin, P. J., Huang, H., Gao, M. and Ran, B., A vehicle type-dependent visual imaging model for analysing the heterogeneous car-following dynamics. Transp. B, 2016, 4(1), 68–85.

  • He, Z., Zheng, L. and Guan, W., A simple nonparametric carfollowing model driven by field data. Transp. Res. Part B, 2015, 80, 185–201.

  • Fan, P., Guo, J., Zhao, H., Wijnands, J. S. and Wang, Y., Carfollowing modeling incorporating driving memory based on autoencoder and long short-term memory neural networks. Sustainability, 2019, 11(6755), 2–15.

  • Gunay, B., Car following theory with lateral discomfort. Transp. Res. Part B, 2007, 41(7), 722–735.

  • Arasan, V. and Dhivya, G., Methodology for determination of concentration of heterogeneous traffic. J. Transp. Syst. Eng. Inf. Technol., 2010, 10(4), 50–61.

  • Kanagaraj, V., Asaithambi, G., Srinivasan K. K. and Sivanandan, R., Vehicle class wise analysis of time gaps and headways under heterogeneous traffic. Presented at the 90th Transportation Research Board Annual Meeting, Washington, DC, USA, 2011.

  • Ravishankar, K. V. R. and Mathew, T. V., Vehicle-type dependent car-following model for heterogeneous traffic conditions. J. Transp. Eng., 2011 137(11), 775–781.

  • Metkari, M., Budhkar, A. and Maurya, A. K., Development of simulation model for heterogeneous traffic with no lane discipline. Procedia – Soc. Behav. Sci., 2013, 104, 360–369.

  • Chandra, S., Capacity estimation procedure for two-lane roads under mixed traffic conditions. J. Indian Roads Congress, 2004, 498, 139–167.

  • Jin, S., Huang, Z. Y., Tao, P. F. and Wang, D. H., Car-following theory of steady-state traffic flow using time-to-collision. J. Zhejiang Univ. Sci. A, 2011, 12(8), 645–654.

  • Mathew, T. V., Munigety, C. R. and Bajpai, A., Strip-based approach for the simulation of mixed traffic conditions. J. Comput. Civ. Eng., 2015, 29(5), 1–9.

  • Budhkar, A. and Maurya, A., Characteristics of lateral vehicular interactions in heterogeneous traffic with weak lane discipline. J. Mod. Transp., 2017, 25, 74–89.

  • Papathanasopoulou, V. and Antoniou, C., Flexible car-following models for mixed traffic and weak lane-discipline conditions. Eur. Transp. Res. Rev., 2018, 10(2), 2–14.

  • Kiran, M. S. and Verma, A., Review of studies on mixed traffic flow: perspective of developing economies. Transp. Dev. Econ., 2016, 2(5), 2–16.

  • Madhu, K., Sivanandan, R. and Srinivasan, K. K., Identification of different vehicle-following manoeuvres for heterogeneous weaklane disciplined traffic condition from vehicle trajectory data. IOP Conf. Ser.: Earth Environ. Sci., 2020, 491, 012052.

  • Gujarati, D. N., Basic Econometrics Fourth Edition, McGraw-Hill Companies, Boston, 2004.

  • Fisher, F. M., Tests of equality between sets of coefficients in two linear regressions. An expository note. Econometrica, 2006, 38(2), 361–371.


Abstract Views: 283

PDF Views: 122




  • Acceleration models for two-wheelers and cars in mixed traffic: effect of unique vehicle-following interactions and driving regimes

Abstract Views: 283  |  PDF Views: 122

Authors

Kavitha Madhu
Department of Civil Engineering, TKM College of Engineering, Kollam 691 005, India, India
Karthik K. Srinivasan
Transportation Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India, India
R. Sivanandan
Transportation Engineering Division, Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India, India

Abstract


Driving behaviour in mixed traffic conditions is chara­cterized by vehicle heterogeneity and lane-less movement. In such traffic conditions, the following response of a vehicle may be discontinuous and gets triggered when certain thresholds on relative speed and spacing with the leaders are crossed. In this context, the present study segments vehicular response into driving regimes using vehicle trajectory data based on relative speed and position. Acceleration models are formulated by featuring driving regimes and their interactions with mixed traffic attributes. These models are used to study the differences in the following behaviour of two-wheelers and cars. The proposed models capture the asymmetric behaviour and account for differences across driving regimes, resulting in a signi­ficantly better fit and realistic representation of mixed traffic.

Keywords


Acceleration Models, Driving Regimes, Mixed Traffic Attributes, Local Area Concentration, Vehicle Trajectory Extraction

References





DOI: https://doi.org/10.18520/cs%2Fv122%2Fi12%2F1441-1450