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Calibrating supply parameters of large-scale DTA models with surrogate-based optimisation

Calibrating supply parameters of large-scale DTA models with surrogate-based optimisation

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This study is among the early attempts to employ a surrogate-based optimisation (SBO) approach to solve the large-scale dynamic traffic assignment (DTA) calibration problem that is characterised by an expensive-to-evaluate and non-closed-form objective function. This paper formulates the calibration of the large-scale DTA model as a bi-level optimisation problem with a non-closed objective function such that it can only be evaluated through simulation. The Kriging surrogate model is adopted to construct the response surface between the objective value and the decision variables. The SBO approach first evaluates a number of initial samples, then fits the response surface and searches for the optima via an infill process. It reduces the number of large-scale DTA runs for evaluating the objective values and saves much computational time. For demonstrative purposes, a real-world large-scale DTA model in the state of MD is calibrated with the proposed SBO approach. After 400 initial points and 100 infill points, the SBO approach reduces the calibration matching gap from 29.68 to 21.90%. It is also presented that the proposed SBO is significantly faster than the genetic algorithm in searching for better solutions. The results demonstrate the feasibility and capability of SBO in DTA calibration problems.

References

    1. 1)
      • G. Gentile , L. Meschini , N. Papola .
        1. Gentile, G., Meschini, L., Papola, N.: ‘Spillback congestion in dynamic traffic assignment: a macroscopic flow model with time-varying bottlenecks’, Transp. Res. B, Methodol., 2007, 41, (10), pp. 11141138.
        . Transp. Res. B, Methodol. , 10 , 1114 - 1138
    2. 2)
      • X. Zhou , H.S. Mahmassani , K. Zhang .
        2. Zhou, X., Mahmassani, H.S., Zhang, K.: ‘Dynamic micro-assignment modeling approach for integrated multimodal urban corridor management’, Transp. Res. C, Emerg. Technol., 2008, 16, (2), pp. 167186.
        . Transp. Res. C, Emerg. Technol. , 2 , 167 - 186
    3. 3)
      • Q. Yang , H. Koutsopoulos , M. Ben-Akiva .
        3. Yang, Q., Koutsopoulos, H., Ben-Akiva, M.: ‘Simulation laboratory for evaluating dynamic traffic management systems’, Transp. Res. Rec., J. Transp. Res. Board, 2000, 1710, pp. 122130.
        . Transp. Res. Rec., J. Transp. Res. Board , 122 - 130
    4. 4)
      • Z. Zhu , C. Xiong , X. Chen .
        4. Zhu, Z., Xiong, C., Chen, X., et al: ‘Integrating dynamic traffic assignment and agent-based travel behavior models for cumulative land development impact analysis’. Presented at the Seventh Int. Symp. Travel Demand Management, Tucson, Arizona, USA, 2015.
        . Presented at the Seventh Int. Symp. Travel Demand Management
    5. 5)
      • N. Gartner , C. Stamatiadis .
        5. Gartner, N., Stamatiadis, C.: ‘Integration of dynamic traffic assignment with real-time traffic adaptive control system’, Transp. Res. Rec., J. Transp. Res. Board, 1998, 1644, pp. 150156.
        . Transp. Res. Rec., J. Transp. Res. Board , 150 - 156
    6. 6)
      • (2005)
        6. Caliper.: ‘TransCAD user's guide’ (Caliper Cooperation, USA, 2005).
        .
    7. 7)
      • 7. PTV. ‘VISUM 11 user manual’. Karlsruhe, Germany, 2009.
        .
    8. 8)
      • M. Ben-Akiva .
        8. Ben-Akiva, M.: ‘Development of dynamic traffic assignment system for planning purposes: DynaMIT user's guide’, 2002.
        .
    9. 9)
      • H.S. Mahmassani , H. Sbayti , X. Zhou . (2004)
        9. Mahmassani, H.S., Sbayti, H., Zhou, X.: DYNASMART-P version 1.0 user's guide.Maryland Transportation Initiative, College Park, MD, 2004, p. 137.
        .
    10. 10)
      • X. Zhou , J. Taylor .
        10. Zhou, X., Taylor, J.: ‘DTALite: a queue-based mesoscopic traffic simulator for fast model evaluation and calibration’, Cogent Eng., 2014, 1, (1), p. 961345.
        . Cogent Eng. , 1 , 961345
    11. 11)
      • 11. PTV.: ‘VISSIM user manual (version 4.51)’, 2008.
        .
    12. 12)
      • M. Behrisch , L. Bieker , J. Erdmann .
        12. Behrisch, M., Bieker, L., Erdmann, J., et al: ‘SUMO – simulation of urban mobility: an overview’. Third Int. Conf. Advances in System Simulation Proc. SIMUL 2011, ThinkMind, Barcelona, Spain, 2011.
        . Third Int. Conf. Advances in System Simulation Proc. SIMUL 2011, ThinkMind
    13. 13)
      • 13. Caliper.: ‘TransModeler traffic simulation software – version 2.5 user's guide’, 2009.
        .
    14. 14)
      • E. Cascetta , S. Nguyen .
        14. Cascetta, E., Nguyen, S.: ‘A unified framework for estimating or updating origin/destination matrices from traffic counts’, Transp. Res. B, Methodol., 1988, 22, (6), pp. 437455.
        . Transp. Res. B, Methodol. , 6 , 437 - 455
    15. 15)
      • E. Cascetta , D. Inaudi , G. Marquis .
        15. Cascetta, E., Inaudi, D., Marquis, G.: ‘Dynamic estimators of origin–destination matrices using traffic counts’, Transp. Sci., 1993, 27, (4), pp. 363373.
        . Transp. Sci. , 4 , 363 - 373
    16. 16)
      • H. Tavana .
        16. Tavana, H.: ‘Internally-consistent estimation of dynamic network origin–destination flows from intelligent transportation systems data using bi-level optimization’. Doctoral dissertation, the University of Texas at Austin, TX, 2001.
        .
    17. 17)
      • X. Zhou , X. Qin , H. Mahmassani .
        17. Zhou, X., Qin, X., Mahmassani, H.: ‘Dynamic origin–destination demand estimation with multiday link traffic counts for planning applications’, Transp. Res. Rec., J. Transp. Res. Board, 2003, 1831, pp. 3038.
        . Transp. Res. Rec., J. Transp. Res. Board , 30 - 38
    18. 18)
      • Y. Nie .
        18. Nie, Y.: ‘A variational inequality approach for inferring dynamic origin–destination travel demands’. Doctoral dissertation, University of California, Davis, CA, 2006.
        .
    19. 19)
      • H. Zhang , Y. Nie , Z. Qian .
        19. Zhang, H., Nie, Y., Qian, Z.: ‘Estimating time-dependent freeway origin–destination demands with different data coverage: sensitivity analysis’, Transp. Res. Rec., J. Transp. Res. Board, 2008, 2047, pp. 9199.
        . Transp. Res. Rec., J. Transp. Res. Board , 91 - 99
    20. 20)
      • H. Kim , S. Baek , Y. Lim .
        20. Kim, H., Baek, S., Lim, Y.: ‘Origin–destination matrices estimated with a genetic algorithm from link traffic counts’, Transp. Res. Rec., J. Transp. Res. Board, 2001, 1771, pp. 156163.
        . Transp. Res. Rec., J. Transp. Res. Board , 156 - 163
    21. 21)
      • L. Kattan , B. Abdulhai .
        21. Kattan, L., Abdulhai, B.: ‘Noniterative approach to dynamic traffic origin–destination estimation with parallel evolutionary algorithms’, Transp. Res. Rec., J. Transp. Res. Board, 2006, 1964, pp. 201210.
        . Transp. Res. Rec., J. Transp. Res. Board , 201 - 210
    22. 22)
      • A. Stathopoulos , T. Tsekeris .
        22. Stathopoulos, A., Tsekeris, T.: ‘Hybrid meta-heuristic algorithm for the simultaneous optimization of the O–D trip matrix estimation’, Comput.-Aided Civ. Infrastruct. Eng., 2004, 19, (6), pp. 421435.
        . Comput.-Aided Civ. Infrastruct. Eng. , 6 , 421 - 435
    23. 23)
      • E. Cipriani , M. Florian , M. Mahut .
        23. Cipriani, E., Florian, M., Mahut, M., et al: ‘A gradient approximation approach for adjusting temporal origin–destination matrices’, Transp. Res. C, Emerg. Technol., 2011, 19, (2), pp. 270282.
        . Transp. Res. C, Emerg. Technol. , 2 , 270 - 282
    24. 24)
      • X. He .
        24. He, X.: ‘Simulation-based optimization of transportation systems: theory, surrogate models, and applications’. Doctoral dissertation, University of Maryland, College Park, MD, 2014.
        .
    25. 25)
      • R. Omrani , L. Kattan .
        25. Omrani, R., Kattan, L.: ‘Demand and supply calibration of dynamic traffic assignment models: past efforts and future challenges’, Transp. Res. Rec., J. Transp. Res. Board, 2012, 2283, pp. 100112.
        . Transp. Res. Rec., J. Transp. Res. Board , 100 - 112
    26. 26)
      • İ Verbas , H. Mahmassani , K. Zhang .
        26. Verbas, İ, Mahmassani, H., Zhang, K.: ‘Time-dependent origin–destination demand estimation: challenges and methods for large-scale networks with multiple vehicle classes’, Transp. Res. Rec., J. Transp. Res. Board, 2011, 2263, pp. 4556.
        . Transp. Res. Rec., J. Transp. Res. Board , 45 - 56
    27. 27)
      • G. Cantelmo , E. Cipriani , A. Gemma .
        27. Cantelmo, G., Cipriani, E., Gemma, A., et al: ‘An adaptive bi-level gradient procedure for the estimation of dynamic traffic demand’, IEEE Trans. Intell. Transp. Syst., 2014, 15, (3), pp. 13481361.
        . IEEE Trans. Intell. Transp. Syst. , 3 , 1348 - 1361
    28. 28)
      • G. Cantelmo , F. Viti , E. Cipriani .
        28. Cantelmo, G., Viti, F., Cipriani, E., et al: ‘A utility-based dynamic demand estimation model that explicitly accounts for activity scheduling and duration’, Transp. Res. Procedia, 2017, 23, pp. 440459.
        . Transp. Res. Procedia , 440 - 459
    29. 29)
      • H. Tavana , H. Mahmassani .
        29. Tavana, H., Mahmassani, H.: ‘Estimation and application of dynamic speed-density relations by using transfer function models’, Transp. Res. Rec., J. Transp. Res. Board, 2000, 1710, pp. 4757.
        . Transp. Res. Rec., J. Transp. Res. Board , 47 - 57
    30. 30)
      • Y.C. Chiu , L. Zhou , H. Song .
        30. Chiu, Y.C., Zhou, L., Song, H.: ‘Development and calibration of the anisotropic mesoscopic simulation model for uninterrupted flow facilities’, Transp. Res. B, Methodol., 2010, 44, (1), pp. 152174.
        . Transp. Res. B, Methodol. , 1 , 152 - 174
    31. 31)
      • D.L. Doan , A. Ziliaskopoulos , H. Mahmassani .
        31. Doan, D.L., Ziliaskopoulos, A., Mahmassani, H.: ‘On-line monitoring system for real-time traffic management applications’, Transp. Res. Rec., J. Transp. Res. Board, 1998, 1678, pp. 142149.
        . Transp. Res. Rec., J. Transp. Res. Board , 142 - 149
    32. 32)
      • Y.E. Hawas .
        32. Hawas, Y.E.: ‘Calibrating simulation models for advanced traveler information systems/advanced traffic management systems applications’, J. Transp. Eng., 2002, 128, (1), pp. 8088.
        . J. Transp. Eng. , 1 , 80 - 88
    33. 33)
      • Y. Chen , H.J. Van Zuylen , R. Lee .
        33. Chen, Y., Van Zuylen, H.J., Lee, R.: ‘Developing a large-scale urban decision support system’, Control in Transp. Syst., 2006, 11, (1), pp. 216221.
        . Control in Transp. Syst. , 1 , 216 - 221
    34. 34)
      • R. Balakrishna , H.N. Koutsopoulos , M. Ben-Akiva .
        34. Balakrishna, R., Koutsopoulos, H.N., Ben-Akiva, M.: ‘Calibration and validation of dynamic traffic assignment systems. Transportation and traffic theory: flow, dynamics and human interaction’. Proc. 16th Int. Symp. Transportation and Traffic Theory, University of Maryland, College Park, 2005, pp. 407426.
        . Proc. 16th Int. Symp. Transportation and Traffic Theory , 407 - 426
    35. 35)
      • K.K. Kundé .
        35. Kundé, K.K.: ‘Calibration of mesoscopic traffic simulation models for dynamic traffic assignment’. Doctoral dissertation, Massachusetts Institute of Technology, 2002.
        .
    36. 36)
      • C. Antoniou , M. Ben-Akiva , H. Koutsopoulos .
        36. Antoniou, C., Ben-Akiva, M., Koutsopoulos, H.: ‘Online calibration of traffic prediction models’, Transp. Res. Rec., J. Transp. Res. Board, 2005, 1934, pp. 235245.
        . Transp. Res. Rec., J. Transp. Res. Board , 235 - 245
    37. 37)
      • R. Balakrishna , H.N. Koutsopoulos , M. Ben-Akiva .
        37. Balakrishna, R., Koutsopoulos, H.N., Ben-Akiva, M.: ‘Simultaneous off-line demand and supply calibration of dynamic traffic assignment systems’. Presented at 85th Annual Meeting of the Transportation Research Board, Washington, D.C, USA., 2006.
        . Presented at 85th Annual Meeting of the Transportation Research Board
    38. 38)
      • R. Balakrishna , M. Ben-Akiva , H. Koutsopoulos .
        38. Balakrishna, R., Ben-Akiva, M., Koutsopoulos, H.: ‘Offline calibration of dynamic traffic assignment: simultaneous demand-and-supply estimation’, Transp. Res. Rec., J. Transp. Res. Board, 2007, 2003, pp. 5058.
        . Transp. Res. Rec., J. Transp. Res. Board , 50 - 58
    39. 39)
      • V. Vaze , C. Antoniou , Y. Wen .
        39. Vaze, V., Antoniou, C., Wen, Y., et al: ‘Calibration of dynamic traffic assignment models with point-to-point traffic surveillance’, Transp. Res. Rec., J. Transp. Res. Board, 2009, 2090, pp. 19.
        . Transp. Res. Rec., J. Transp. Res. Board , 1 - 9
    40. 40)
      • X. Qin , H. Mahmassani .
        40. Qin, X., Mahmassani, H.: ‘Adaptive calibration of dynamic speed–density relations for online network traffic estimation and prediction applications’, Transp. Res. Rec., J. Transp. Res. Board, 2004, 1876, pp. 8289.
        . Transp. Res. Rec., J. Transp. Res. Board , 82 - 89
    41. 41)
      • Y. Wang , M. Papageorgiou .
        41. Wang, Y., Papageorgiou, M.: ‘Real-time freeway traffic state estimation based on extended Kalman filter: a general approach’, Transp. Res. B, Methodol., 2005, 39, (2), pp. 141167.
        . Transp. Res. B, Methodol. , 2 , 141 - 167
    42. 42)
      • K. Ashok , M. Ben-Akiva . (1993)
        42. Ashok, K., Ben-Akiva, M.: ‘Dynamic O–D matrix estimation and prediction for real-time traffic management systems’, in Daganzo, C. (Ed.), ‘Transportation and traffic theory’ (Elsevier Science Publishing, California, USA, 1993), pp. 465484.
        .
    43. 43)
      • K. Ashok .
        43. Ashok, K.: ‘Estimation and prediction of time-dependent origin–destination flows’. PhD dissertation, Massachusetts Institute of Technology, 1996.
        .
    44. 44)
      • K. Ashok , M. Ben-Akiva .
        44. Ashok, K., Ben-Akiva, M.: ‘Alternative approaches for real-time estimation and prediction of time-dependent origin–destination flows’, Transp. Sci., 2000, 34, (1), pp. 2136.
        . Transp. Sci. , 1 , 21 - 36
    45. 45)
      • X. Zhou , H.S. Mahmassani .
        45. Zhou, X., Mahmassani, H.S.: ‘Recursive approaches for online consistency checking and OD demand updating for real-time dynamic traffic assignment operation’, Transp. Res. Rec., 2005, 1923, pp. 218226.
        . Transp. Res. Rec. , 218 - 226
    46. 46)
      • C. Antoniou , M. Ben-Akiva , H.N. Koutsopoulos .
        46. Antoniou, C., Ben-Akiva, M., Koutsopoulos, H.N.: ‘Nonlinear Kalman filtering algorithms for on-line calibration of dynamic traffic assignment models’, IEEE Trans. Intell. Transp. Syst., 2007, 8, (4), pp. 661670.
        . IEEE Trans. Intell. Transp. Syst. , 4 , 661 - 670
    47. 47)
      • X. Zhou , H.S. Mahmassani .
        47. Zhou, X., Mahmassani, H.S.: ‘A structural state space model for real-time traffic origin–destination demand estimation and prediction in a day-to-day learning framework’, Transp. Res. B, Methodol., 2007, 41, (8), pp. 823840.
        . Transp. Res. B, Methodol. , 8 , 823 - 840
    48. 48)
      • H. Hashemi , K.F. Abdelghany , A.F. Abdelghany .
        48. Hashemi, H., Abdelghany, K.F., Abdelghany, A.F.: ‘A multi-agent learning approach for online calibration and consistency checking of real-time traffic network management systems’, Transportmetrica B, Transp. Dyn., 2017, 5, (3), pp. 369389.
        . Transportmetrica B, Transp. Dyn. , 3 , 369 - 389
    49. 49)
      • R.R. Barton , M. Meckesheimer .
        49. Barton, R.R., Meckesheimer, M.: ‘Metamodel-based simulation optimization’, Handb. Oper. Res. Manage. Sci., 2006, 13, pp. 535574.
        . Handb. Oper. Res. Manage. Sci. , 535 - 574
    50. 50)
      • X. Chen , L. Zhang , X. He .
        50. Chen, X., Zhang, L., He, X., et al: ‘Surrogate-based optimization of expensive-to-evaluate objective for optimal highway toll charges in transportation network’, Comput.-Aided Civ. Infrastruct. Eng., 2014, 29, (5), pp. 359381.
        . Comput.-Aided Civ. Infrastruct. Eng. , 5 , 359 - 381
    51. 51)
      • X. Chen , Z. Zhu , X. He .
        51. Chen, X., Zhu, Z., He, X., et al: ‘Surrogate-based optimization for solving mixed integer network design problem’, Transp. Res. Rec., J. Transp. Res. Board, 2015, 2497, pp. 124134.
        . Transp. Res. Rec., J. Transp. Res. Board , 124 - 134
    52. 52)
      • X. He , X. Chen , C. Xiong .
        52. He, X., Chen, X., Xiong, C., et al: Integrated optimization of transportation demand management and traffic operations using simulation: a bootstrapped support vector regression method considering the statistical distribution of simulation noise. Presented at the 94th Annual Meeting of Transportation Research Board, Washington, D.C., USA, 2015.
        . Presented at the 94th Annual Meeting of Transportation Research Board
    53. 53)
      • X. He .
        53. He, X.: ‘Simulation-based optimization of transportation systems: theory, surrogate models, and applications’. Doctoral dissertation, University of Maryland, College Park, 2014.
        .
    54. 54)
      • C. Zhang , C. Osorio , G. Flötteröd .
        54. Zhang, C., Osorio, C., Flötteröd, G.: ‘Efficient calibration techniques for large-scale traffic simulators’, Transp. Res. B, Methodol., 2017, 97, pp. 214239.
        . Transp. Res. B, Methodol. , 214 - 239
    55. 55)
      • Y.C. Chiu , B. Bustillos .
        55. Chiu, Y.C., Bustillos, B.: ‘A gap function vehicle-based solution procedure for consistent and robust simulation-based dynamic traffic assignment (no. 09-3721)’, 2009.
        .
    56. 56)
      • Y.C. Chiu , J. Bottom , M. Mahut .
        56. Chiu, Y.C., Bottom, J., Mahut, M., et al: (2011). ‘Dynamic traffic assignment: a primer’. Transportation Research E-Circular E-C153.
        .
    57. 57)
      • C.C. Lu , X. Zhou , K. Zhang .
        57. Lu, C.C., Zhou, X., Zhang, K.: ‘Dynamic origin–destination demand flow estimation under congested traffic conditions’, Transp. Res. C, Emerg. Technol., 2013, 34, pp. 1637.
        . Transp. Res. C, Emerg. Technol. , 16 - 37
    58. 58)
      • X. Hu , Y.C. Chiu , J.A. Villalobos .
        58. Hu, X., Chiu, Y.C., Villalobos, J.A., et al: ‘A sequential decomposition framework and method for calibrating dynamic origin–destination demand in a congested network’, IEEE Trans. Intell. Transp. Syst., 2017, 18, (10), pp. 27902797.
        . IEEE Trans. Intell. Transp. Syst. , 10 , 2790 - 2797
    59. 59)
      • M. Hussain , R. Barton , S. Joshi .
        59. Hussain, M., Barton, R., Joshi, S.: ‘Metamodeling: radial basis functions, versus polynomials’, Eur. J. Oper. Res., 2002, 138, (1), pp. 142154.
        . Eur. J. Oper. Res. , 1 , 142 - 154
    60. 60)
      • S. Jakobsson , M. Patriksson , J. Rudholm .
        60. Jakobsson, S., Patriksson, M., Rudholm, J., et al: ‘A method for simulation-based optimization using radial basis functions’, Optim. Eng., 2010, 11, (4), pp. 501532.
        . Optim. Eng. , 4 , 501 - 532
    61. 61)
      • J. Müller .
        61. Müller, J.: ‘MATSumoto: the MATLAB surrogate model toolbox for computationally expensive black-box global optimization problems’. 2014. arXiv preprint arXiv:1404.4261.
        .
    62. 62)
      • C. Xiong , Z. Zhu , X. He .
        62. Xiong, C., Zhu, Z., He, X., et al: ‘Developing a 24 h large-scale microscopic traffic simulation model for the before-and-after study of a new tolled freeway in the Washington, D.C.–Baltimore region’, J. Transp. Eng., 2015, 141, (6), p. 05015001.
        . J. Transp. Eng. , 6 , 05015001
    63. 63)
      • Z. Zhu , C. Xiong , X. Chen .
        63. Zhu, Z., Xiong, C., Chen, X., et al: ‘Agent-based microsimulation approach for design and evaluation of flexible work schedules’, Transp. Res. Rec., J. Transp. Res. Board, 2015, 2537, pp. 167176.
        . Transp. Res. Rec., J. Transp. Res. Board , 167 - 176
    64. 64)
      • K. Rasheed , H. Hirsh , A. Gelsey .
        64. Rasheed, K., Hirsh, H., Gelsey, A.: ‘A genetic algorithm for continuous design space search’, Artif. Intell. Eng., 1997, 11, (3), pp. 295305.
        . Artif. Intell. Eng. , 3 , 295 - 305
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