The study evaluates the added value generated by estimating dynamic demand matrices by
information gathered from Floating Car Data (FCD). Firstly, adopting a large dataset of FCD …

Simultaneous perturbation stochastic approximation (SPSA) is an efficient and well
established optimization method that approximates gradients from successive objective …

The fundamental challenge of the origin-destination (OD) matrix estimation problem is that it
is severely under-determined. In this paper we propose a new data driven OD estimation …

Abstract Estimation/updating of Origin–Destination (OD) flows and other traffic state
parameters is a classical, widely adopted procedure in transport engineering, both in off-line …

Nowadays, new mobility information can be derived from advanced traffic surveillance
systems that collect updated traffic measurements, both in fixed locations and over specific …

The development and calibration of complex traffic models demands parsimonious
techniques, because such models often involve hundreds of thousands of unknown …

Transportation networks are unprecedentedly complex with heterogeneous vehicular flow.
Conventionally, vehicles are classified by size, the number of axles or engine types, eg …

Calibrating dynamic traffic demand for stochastic traffic simulators is one of the big
challenges due to computational burden. This paper proposes a novel framework to …

Traffic assignment is of great importance in real life from foot traffic assignment of a building
to vehicle traffic assignment of a city. With the rapid increase of the number of agents and the …

This paper considers high-dimensional offline calibration problems for large-scale
simulation-based network models. We propose a metamodel simulation-based optimization …