access icon free Calibration of Gipps’ car-following model for trucks and the impacts on fuel consumption estimation

© The Institution of Engineering and Technology
Received 26/01/2018, Accepted 24/09/2018, Revised 23/06/2018, Published 09/10/2018

Calibration of car-following models plays an important role not only in traffic simulation but also in the estimation of traffic-related energy consumption. However, the majority of calibration studies only focus on errors on position or speed, whereas these models are used to evaluate environmental parameters associated with road traffic (e.g. pollutant emissions, energy consumption). Then, this study focuses on the ability of Gipps’ car-following model calibrated on trajectory parameters to estimate properly the fuel consumption of a heavy vehicle. First, the shape of one of the most used Goodness-of-Fit function, Theil's inequality coefficient, is investigated. It will be demonstrated that optimal domains are flat and large, and so many combinations of parameters could accurately reproduce the vehicle trajectory. Then, the authors found that Gipps model, calibrated via a multi-objective particle swarm optimisation is relevant to simulate the trajectory of a heavy vehicle, but fuel consumption estimation resulting of these trajectories exhibits large discrepancies. To solve this issue, it is proposed to add the fuel consumption estimation directly in the calibration process as a further dimension. The results show an improvement in the value of energy consumption estimation without increasing too much the error on the trajectory.

Inspec keywords: automobiles; particle swarm optimisation; calibration; fuel economy; road traffic

Other keywords: fuel consumption estimation; environmental parameters; multiobjective particle swarm optimisation; traffic-related energy consumption estimation; trajectory parameters; Gipps car-following model calibration; Theil inequality coefficient; traffic simulation; pollutant emissions; goodness-of-fit function

Subjects: Systems theory applications in transportation; Optimisation techniques

References

    1. 1)
      • 5. Aghabayk,, K., Sarvi,, M., Young,, W.: ‘Understanding the dynamics of heavy vehicle interactions in car-following’, J. Transp. Eng., 2012, 138, (12), pp. 1468–1475.
    2. 2)
      • 18. Punzo,, V., Ciuffo,, B., Montanino,, M.: ‘May we trust results of car-following models calibration based on trajectory data?’. Proc. of the 91st Transportation Research Board Annual meeting, Transportation Research Board of the National Academies, Washington, DC, USA, 2012.
    3. 3)
      • 3. Aghabayk,, K., Sarvi,, M., Young,, W.: ‘Including heavy vehicles in car-following model: modeling, calibrating and validating’, J. Adv. Transp., 2016, 50, (7), pp. 1432–1446.
    4. 4)
      • 20. Punzo,, V., Montanino,, M.: ‘Speed or spacing? cumulative variables, and convolution of model errors and time in traffic flow models validation and calibration’, Transp. Res. B: Methodol., 2016, 91, pp. 21–33.
    5. 5)
      • 13. Wilson,, R.E.: ‘An analysis of Gipps’ car-following model of highway traffic’, IMA J. Appl. Math., 2001, 66, (5), pp. 509–537.
    6. 6)
      • 14. Ciuffo,, B., Punzo,, V., Montanino,, M.: ‘Thirty years of Gipps’ car-following model’, Transp. Res. Rec.: J. Transp. Res. Board, 2012, 2315, pp. 89–99.
    7. 7)
      • 15. Punzo,, V., Simonelli,, F.: ‘Analysis and comparison of microscopic traffic flow models with real traffic microscopic data’, Transp. Res. Rec.: J. Transp. Res. Board, 2005, 1934, pp. 53–63.
    8. 8)
      • 25. Coello Coello,, C.A., Lechuga,, M.S.: ‘MOPSO: a proposal for multiple objective particle swarm optimization’. IEEE. Evolutionary Computation, Honolulu, HI, USA, 2002, pp. 1051–1056.
    9. 9)
      • 29. Rakha,, H., Wang,, W.: ‘Procedure for calibration Gipps car-following model’, Transp. Res. Rec.: J. Transp. Res. Board, 2009, 2124, pp. 113–124.
    10. 10)
      • 22. Kim,, J., Mahmassani,, H.: ‘Correlated parameters in driving behaviour models: car-following example and its implication for traffic microsimulation’, Transp. Res. Rec.: J. Transp. Res. Board, 2011, 2249, pp. 62–77.
    11. 11)
      • 19. Treiber,, M., Kesting,, A.: ‘A microscopic calibration and validation of car-following models – a systematic approach’, Proc. Soc. Behav. Sci., 2013, 80, pp. 922–939.
    12. 12)
      • 16. Kesting,, A., Treiber,, M.: ‘Calibrating car-following models by using trajectory data: methodological study’, Transp. Res. Rec.: J. Transp. Res. Board, 2008, 2088, pp. 148–156.
    13. 13)
      • 24. Eberhart,, R., Kennedy,, J.: ‘A new optimizer using particle swarm theory’. IEEE. Micro Machine and Human Science, Nagoya, Japan, 1995, pp. 39–43.
    14. 14)
      • 28. Vieira da Rocha,, T., Leclercq,, L., Montanino,, M., et al: ‘Does traffic-related calibration of car-following models provide accurate estimations of vehicle emissions?’, Transp. Res. D: Transp. Environ., 2015, 34, pp. 267–280.
    15. 15)
      • 12. Gipps,, P.G.: ‘A behavioural car-following model for computer simulation’, Transp. Res. B: Methodol., 1981, 15, (2), pp. 105–111.
    16. 16)
      • 17. Ciuffo,, B., Punzo,, V.: ‘Verification of traffic micro-simulation model calibration procedures: analysis of goodness-of-fit measures’. Proc. of the 89th Annual Meeting of the Transportation Research Record, Washington, DC, USA, 2010.
    17. 17)
      • 21. Ossen,, S., Hoogendoorn,, S.: ‘Calibrating car-following models using microscopic trajectory data: a critical analysis of both microscopic trajectory data collection methods and calibration studies based on these data’, Delft Univ. Technol., 2008, vol. 21.
    18. 18)
      • 2. Nodine,, E., Lam,, A., Yanagisawa,, M, et al: ‘Naturalistic study of truck following behavior’, Transp. Res. Rec.: J. Transp. Res. Board, 2017, 2615, pp. 35–42.
    19. 19)
      • 10. Aghabayk,, K., Sarvi,, M., Forouzideh,, N., et al: ‘New car-following model considering impacts of multiple lead vehicle types’, Transp. Res. Rec.: J. Transp. Res. Board, 2013, 2390, pp. 131–137.
    20. 20)
      • 7. Yang,, D., Jin,, P., Pu,, Y., et al: ‘Stability analysis of the mixed traffic flow of cars and trucks using heterogeneous optimal velocity car-following model’, Physica A, 2014, 395, pp. 371–383.
    21. 21)
      • 11. Aghabayk,, K.: ‘Modelling heavy vehicle car-following in congested traffic conditions’. PhD thesis, Department of Civil Engineering, Institute of Transport Studies, Monash University, 2013.
    22. 22)
      • 26. Alvarez-Benitez,, J.E., Everson, R.M.: ‘A MOPSO algorithm based exclusively on pareto dominance concepts’. Third Int. Conf. on Evolutionary Multi-Criterion Optimization, Guanajuato, Mexico, 2005, pp. 459–473.
    23. 23)
      • 8. Liu,, L., Zhu,, L., Yang,, D.: ‘Modeling and simulation of the car-truck heterogeneous traffic flow based on a nonlinear car-following model’, Appl. Math. Comput., 2016, 273, pp. 706–717.
    24. 24)
      • 27. Eberhart,, R.C., Shi,, Y.: ‘Comparing inertia weights and constriction factors in particle swarm optimization’. Proc. of the 2000 Congress on Evolutionary Computation, La Jolla, CA, USA, 2000, vol. 1, pp. 84–88.
    25. 25)
      • 23. Kennedy,, J., Eberhart,, R.: ‘Particle swarm optimization’. IEEE. Conf. Neural Networks, Perth, Australia, Perth, Australia, 1995, vol. 4, pp. 1942–1948.
    26. 26)
      • 9. Chen,, D., Ahn,, S., Bang,, S., et al: ‘Car-following and lane-changing behavior involving heavy vehicles’, Transp. Res. Rec.: J. Transp. Res. Board, 2016, 2561, pp. 89–97.
    27. 27)
      • 6. Sarvi,, M.: ‘Heavy commercial vehicles-following behaviour and interactions with different vehicle classes’, J. Adv. Transp., 2011, 47, pp. 572–580.
    28. 28)
      • 1. Aghabayk,, K., Sarvi,, M., Young,, W.: ‘A state-of-the-art review of car-following models with particular considerations of heavy vehicles’, Transp. Rev., 2015, 35, (1), pp. 82–105.
    29. 29)
      • 4. Sarvi,, M., Ejtemai,, O.: ‘Exploring heavy vehicles car-following behaviour’. Proc. of the 34th Autralasian Transport Research Forum (ATRF), Adelaide, South Australia, Australia, 2011, pp. 1–11.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2018.5303
Loading

Related content

content/journals/10.1049/iet-its.2018.5303
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading