A fundamental challenge in constructing Big Models is the question of calibration. Big Models typically have a large number of parameters, which need to be inferred from data in a robust and theoretically justifiable way. Approximate Bayesian Computation is one promising approach to tackle this calibration problem for large complex models.
Zhang, L., Chew J.. S. C., & Gan H.. S.
(2018). Optimizing stop-skipping schedules for transit vehicles with equal passenger sizes. the 23rd international conference of Hong Kong Society for Transportation Studies.
Kutadinata, R., Moase W., Manzie C., Zhang L., & Garoni T. M.
(2016). Enhancing the performance of existing urban traffic light control through extremum-seeking. Transportation Research Part C: Emerging Technologies. 62, 1-20. doi: 10.1016/j.trc.2015.10.016
Rickard, C. M., Marsh N. M., Webster J., Gavin N. C., McGrail M. R., Larsen E., et al.
(2015). Intravascular device administration sets: replacement after standard versus prolonged use in hospitalised patients--a study protocol for a randomised controlled trial (The RSVP Trial). BMJ Open. 5(2), doi: 10.1136/bmjopen-2014-007257