Cooperative driving modelling in the vicinity of traffic signals based on intelligent driver model

Cooperative driving modelling in the vicinity of traffic signals based on intelligent driver model

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The vicinity of traffic signals is one of the most special and critical areas in the whole road system. Considering that the driver can obtain traffic signals information through two approaches: vision and the vehicle-to-vehicle (V2X) communication equipment in the vehicle, this study proposes two vehicle cooperative driving models that apply to the vicinity of traffic signals: the intelligent driver model (IDM) in the vicinity of traffic signals (IDM-VT) and IDM in the vicinity of traffic signals under V2X environment (IDM-VT-V2X). These two models are both based on the intelligent driver model. In accordance with different situations of vehicles in the vicinity of traffic signals, such as distance from the traffic lights, whether there was another vehicle in front and different states of traffic lights, the pertinent analysis was conducted, and the cooperative driving strategy that satisfied the complicated situations of vehicle was proposed. These two models were verified and compared in the simulation experiment. The results of simulation show that when comparing with the IDM-VT, the IDM-VT-V2X can reduce the average travel time and the average stop delay time by 12.98 and 98.32%, respectively, and it can also reduce 22.53% fuel consumption.


    1. 1)
      • 1. Liu, H., Sun, D., Zhao, M.: ‘A model prediction control based framework for optimization of signaled intersection: a cyber-physical perspective’, Optik, 2016, 127, (20), pp. 1006810075.
    2. 2)
      • 2. National highway traffic safety administration’. Available at
    3. 3)
      • 3. Gazis, D., Herman, R., Maradudin, A.: ‘The problem of the amber signal light in traffic flow’, Oper. Res., 1960, 8, pp. 112132.
    4. 4)
      • 4. Mirchandani, P., Head, L.: ‘A real-time traffic signal control system: architecture, algorithms, and analysis’, Trans. Res. C, 2001, 9, (6), pp. 415432.
    5. 5)
      • 5. Li, C., Shimamoto, S.: ‘An open traffic light control model for reducing vehicles’ CO2 emissions based on ETC vehicles’, IEEE Trans. Veh. Technol., 2012, 61, (1), pp. 97110.
    6. 6)
      • 6. Zhao, J., Li, W., Wang, J.: ‘Dynamic traffic signal timing optimization strategy incorporating various vehicle fuel consumption characteristics’, IEEE Trans. Veh. Technol., 2016, 65, (6), pp. 38743887.
    7. 7)
      • 7. Aziz, H.M., Ukkusuri, S.V.: ‘Network traffic control in cyber-transportation systems accounting for user-level fairness’, J. Intell. Trans. Syst., 2016, 20, (1), pp. 416.
    8. 8)
      • 8. Hunt, P.B., Robertson, D.I., Bretherton, R.D.: ‘The SCOOT on-line traffic signal optimisation technique’, Traffic Eng. Control, 1982, 23, (4), pp. 190192.
    9. 9)
      • 9. Milanes, V., Perez, J., Onieva, E.: ‘Controller for urban intersections based on wireless communications and fuzzy logic’, IEEE Trans. Intell. Transp. Syst., 2010, 11, (1), pp. 243248.
    10. 10)
      • 10. Alsabaan, M., Naik, K., Khalifa, T.: ‘Optimization of fuel cost and emissions using V2V communications’, IEEE Trans. Intell. Transp. Syst., 2013, 14, (3), pp. 14491461.
    11. 11)
      • 11. Nunzio, G.D., Wit, C.C.D., Moulin, P., et al: ‘Eco-driving in urban traffic networks using traffic signal information’, Int. J. Robust Nonlinear, 2014, 26, pp. 13071324.
    12. 12)
      • 12. Iglesias, I., Isasi, L., Larburu, M.: ‘I2V communication driving assistance system: on-board traffic light assistant’. IEEE 68th Vehicular Technology Conf. (VTC 2008-Fall), 2008, pp. 15.
    13. 13)
      • 13. Liu, B., Kamel, A.E.: ‘V2X-Based decentralized cooperative adaptive cruise control in the vicinity of intersections’, IEEE Trans. Intell. Transp., 2016, 17, pp. 644658.
    14. 14)
      • 14. Lee, J., Park, B.: ‘Development and evaluation of a cooperative vehicle intersection control algorithm under the connected vehicles environment’, IEEE Trans. Intell. Transp., 2012, 13, pp. 8190.
    15. 15)
      • 15. Treiber, M., Hennecke, A., Helbing, D.: ‘Congested traffic states in empirical observations and microscopic simulations’, Phys. Rev. E, 2000, 62, p. 1805.
    16. 16)
      • 16. Treiber, M., Helbing, D.: ‘Memory effects in microscopic traffic models and wide scattering in flow-density data’, Phys. Rev. E, 2003, 68, p. 046119.
    17. 17)
      • 17. Treiber, M., Kesting, A., Helbing, D.: ‘Delays, inaccuracies and anticipation in microscopic traffic models’, Physics A, 2006, 360, pp. 7188.
    18. 18)
      • 18. Kesting, A., Treiber, M.: ‘Calibrating car-following models using trajectory data: methodological study’. Trans. Res. Rec., 2008, 2088, pp. 148156.
    19. 19)
      • 19. Weiß, C.: ‘V2X communication in Europe – from research projects towards standardization and field testing of vehicle communication technology’, Comput. Netw., 2011, 55, pp. 31033119.
    20. 20)
      • 20. Ngoduy, D.: ‘Analytical studies on the instabilities of heterogeneous intelligent traffic flow’, Commun. Nonlinear Sci., 2013, 18, pp. 26992706.
    21. 21)
      • 21. Li, Z., Li, W., Xu, S., et al: ‘Stability analysis of an extended intelligent driver model and its simulations under open boundary condition’, Physics A, 2015, 419, pp. 526536.
    22. 22)
      • 22. Frey, H.C., Unal, A., Rouphail, N.M., et al: ‘n-road measurement of vehicle tailpipe emissions using a portable instrument’, J. Air Waste Manage., 2003, 53, pp. 9921002.
    23. 23)
      • 23. Akçelik, R., Biggs, D.C.: ‘An energy-related model of instantaneous fuel consumption’, Traffic Eng. Control, 1986, 27, pp. 320325.

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