Capacity of a road in an urban network is defined as the maximum number of vehicles which could pass this road in a unit time. Since travel delays occur when the travel demand exceeds the capacity, performance of congestion estimation strongly depends on capacity values. In order to obtain correct values of capacities in a network, one needs to implement calibration, which is a process to modify parameters to make the model outputs match observed data. A calibration problem is also known as an inverse problem in mathematics.

In this thesis, a new calibration method for road capacities in urban networks is presented. The method relies on Partial Least Squares (PLS) regression, which combines calibration and dimensionality reduction capabilities. A sampling strategy in determining which training data should be used is implemented to further im-prove the calibration efficiency and accuracy. Moreover, influence of different parameters such as wiggling amplitude in sensitivity analysis and number of loading vectors on calibration results are investigated. This method is demonstrated to be feasible and efficient in an urban road network (Stockholm, Sweden).

Besides, this method does not require any other constraints (such as non-negativity) in the optimization part and no additional terms need to be added to guarantee the closeness between initial guess of capacities and estimated values of those, which can be regarded as an indicator that this calibration method does not strongly rely on initial guess of input variables. It is a promising method since it can not only be used in capacity calibration but also has a potential on origin-destination (O-D) calibration problems which share a very similar structure: more unknown variables than measurements and similar data structures in input and output spaces. Even more generally, this method has the potential to be applied on most of high-dimensional inverse problems.