access icon free Modelling the driving behaviour at a signalised intersection with the information of remaining green time

Signal lights are essential for maintaining the operational efficiency and safety in urban road networks. Operational efficiency and safety at intersection have been two important topics in transportation science. In this study, the authors propose a car-following model to investigate the impacts of signal light on driving behaviour, fuel consumption and emissions during the whole process that each vehicle runs across the intersection. In particular, the proposed model has explicitly considered the behaviours at an intersection with countdown device that provides instantaneous information to drivers. The proposed model is tested by numerical analysis and the results indicate that the model can enhance the operational efficiency and the traffic safety near the intersection, and also reduce the average fuel consumption of the vehicles. Sensitivity analysis indicates that the vehicles’ initial time headway at the road origin may have major influences on the flow capacity and the total fuel consumption.

Inspec keywords: road traffic; behavioural sciences; energy consumption; fuel economy; road safety; sensitivity analysis; numerical analysis

Other keywords: transportation science; vehicle initial time headway; traffic safety; sensitivity analysis; numerical analysis; car-following model; signal lights; driving behaviour modelling; operational efficiency; signalised intersection; fuel consumption; green time information; urban road network safety; urban road network efficiency

Subjects: Systems theory applications in social science and politics; Other numerical methods; Systems theory applications in transportation

References

    1. 1)
      • 19. Yu, S.W., Shi, Z.K.: ‘Analysis of car-following behaviors considering the green signal countdown device’, Nonlinear Dyn., 2015, 82, pp. 731740.
    2. 2)
      • 18. Yu, S.W., Shi, Z.K.: ‘An extended car-following model at signalized intersections’, Physica A, 2014, 407, pp. 152159.
    3. 3)
      • 3. Tiaprasert, K., Zhang, Y.L., Wang, X.B., et al: ‘Queue length estimation using connected vehicle technology for adaptive signal control’, IEEE Trans. Intell. Transp. Syst., 2015, 16, (4), pp. 21292140.
    4. 4)
      • 15. Nagatani, T., Hino, Y.: ‘Driving behavior and control in traffic system with two kinds of signals’, Physica A, 2014, 403, pp. 110119.
    5. 5)
      • 24. Li, Z.P., Gong, X.B., Liu, Y.C.: ‘An improved car-following model for multiphase vehicular traffic flow and numerical tests’, Commun. Theor. Phys., 2006, 46, (2), pp. 367373.
    6. 6)
      • 17. Tang, T.Q., Huang, H.J., Wong, S.C., et al: ‘A new car-following model with consideration of the traffic interruption probability’, Chin. Phys. B, 2009, 18, (3), pp. 975983.
    7. 7)
      • 31. Rakha, H., Ahn, K., Trani, A.: ‘Comparison of MOBILE5a, MOBILE6, VT-MICRO, and CMEM models for estimating hot-stabilized light-duty gasoline vehicle emissions’, Can. J. Civ. Eng., 2003, 30, (6), pp. 10101021.
    8. 8)
      • 5. Zohdy, I.H., Rakha, H.A.: ‘Intersection management via vehicle connectivity: the intersection cooperative adaptive cruise system concept’, J. Intell. Transp. Syst., 2016, 20, (1), pp. 1732.
    9. 9)
      • 9. Wang, H., Zhang, G.H., Zhang, Z.S., et al: ‘Estimating control delays at signalised intersections using low-resolution transit bus-based global positioning system data’, IET Intell. Transp. Syst., 2016, 10, (2), pp. 7378.
    10. 10)
      • 22. Helbing, D., Tilch, B.: ‘Generalized force model of traffic dynamics’, Phys. Rev. E, 1998, 58, (1), pp. 133138.
    11. 11)
      • 1. Cai, C., Wang, Y., Geers, G.: ‘Vehicle-to-infrastructure communication-based adaptive traffic signal control’, IET Intell. Transp. Syst., 2013, 7, (3), pp. 351360.
    12. 12)
      • 30. Ahn, K., Rakha, H., Trani, A., et al: ‘Estimating vehicle fuel consumption and emissions based on instantaneous speed and acceleration levels’, J. Transp. Eng., 2002, 128, (2), pp. 182190.
    13. 13)
      • 4. Yu, S.W., Shi, Z.K.: ‘Dynamics of connected cruise control systems considering velocity changes with memory feedback’, Measurement, 2015, 64, pp. 3448.
    14. 14)
      • 25. Zheng, L., He, Z.B.: ‘A new car following model from the perspective of visual imaging’, Int. J. Mod. Phys. C, 2015, 26, (8), p. 1550090.
    15. 15)
      • 23. Jiang, R., Wu, Q.S., Zhu, Z.J.: ‘Full velocity difference model for a car-following theory’, Phys. Rev. E, 2001, 64, (1), pp. 367373.
    16. 16)
      • 14. Kikuchi, S., Perincherry, V., Chakroborty, P., et al: ‘Modeling of driver anxiety during signal change intervals’, Transp. Res. Rec., 1993, 1399, pp. 2735.
    17. 17)
      • 33. Silva, C.M., Farias, T.L., Frey, H.C., et al: ‘Evaluation of numerical models for simulation of real-world hot-stabilized fuel consumption and emissions of gasoline light-duty vehicle’, Transp. Res. D, 2006, 11, (5), pp. 377385.
    18. 18)
      • 21. Bando, M., Hasebe, K., Nakayama, A., et al: ‘Dynamical model of traffic congestion and numerical simulation’, Phys. Rev. E, 1995, 51, (2), pp. 10351042.
    19. 19)
      • 10. Ma, D.F., Luo, X.Q., Li, W.J., et al: ‘Traffic demand estimation for lane groups at signal-controlled intersections using travel times from video-imaging detectors’, IET Intell. Transp. Syst., 2017, 11, (4), pp. 222229.
    20. 20)
      • 32. Rakha, H., Ahn, K., Trani, A.: ‘Development of VT-Micro model for estimating hot stabilized light duty vehicle and truck emissions’, Transp. Res. D, 2004, 9, (1), pp. 4974.
    21. 21)
      • 7. Le Vine, S., Liu, X.B., Zheng, F.F., et al: ‘Automated cars: queue discharge at signalized intersections with ‘assured-clear-distance-ahead’ driving strategies’, Transp. Res. C, 2016, 62, pp. 3554.
    22. 22)
      • 2. Li, Z.F., Eleteriadou, L., Ranka, S.: ‘Signal control optimization for automated vehicles at isolated signalized intersections’, Transp. Res. C, 2014, 49, pp. 118.
    23. 23)
      • 6. De Nunzio, G., de Wit, C.C., Moulin, P., et al: ‘Eco-driving in urban traffic networks using traffic signal information’, Int. J. Robust Nonlinear Control, 2016, 26, (6), pp. 13071324.
    24. 24)
      • 20. Gipps, P.G.: ‘A behavioural car-following model for computer simulation’, Transp. Res. B, 1981, 15, (2), pp. 105111.
    25. 25)
      • 27. Peng, G.H., He, H.D., Lu, W.Z.: ‘A new car-following model with the consideration of incorporating timid and aggressive driving behaviors’, Physica A, 2016, 442, pp. 197202.
    26. 26)
      • 11. Wong, S.C., Sze, N.N., Li, Y.C.: ‘Contributory factors to traffic crashes at signalized intersections in Hong Kong’, Accident Anal. Prevent., 2007, 39, (6), pp. 11071113.
    27. 27)
      • 12. Bella, F., Silvestri, M.: ‘Driver's braking behavior approaching pedestrian crossings: a parametric duration model of the speed reduction times’, J. Adv. Transp., 2016, 50, (4), pp. 630646.
    28. 28)
      • 29. An, F., Barth, H., Norbeck, J., et al: ‘Development of comprehensive modal emissions model: operating under hot-stabilized conditions’, Transp. Res. Rec., 1997, 1587, (1), pp. 5262.
    29. 29)
      • 16. Berndt, H., Wender, S., Dietmayer, K.: ‘Driver braking behavior during intersection approaches and implications for warning strategies for driver assistant systems’. IEEE Intelligent Vehicles Symp., Istanbul, Turkey, 2007, pp. 245251.
    30. 30)
      • 26. Yu, S.W., Shi, Z.K.: ‘An extended car-following model considering vehicular gap fluctuation’, Measurement, 2015, 70, pp. 137147.
    31. 31)
      • 28. Beusen, B., Broekx, S., Denys, T., et al: ‘Using on-board logging devices to study the longer-term impact of an eco-driving course’, Transp. Res. D, 2009, 14, (7), pp. 514520.
    32. 32)
      • 8. Islam, M.R., Hurwitz, D.S., Macuga, K.L.: ‘Improved driver responses at intersections with red signal countdown timers’, Transp. Res. C, 2016, 63, pp. 207221.
    33. 33)
      • 13. Zhao, J., Li, P.: ‘An extended car-following model with consideration of speed guidance at intersections’, Physica A, 2016, 461, pp. 18.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2017.0191
Loading

Related content

content/journals/10.1049/iet-its.2017.0191
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading