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 and in online contexts. Notwithstanding numerous approaches proposed in the literature, there is still room for considerable improvements, also leveraging the unprecedented opportunity offered by information and communication technologies and big data. A key issue relates to the unobservability of OD flows in real networks-except from closed highway systems-thus leading to inherent difficulties in measuring performance of OD flows estimation/updating methods and algorithms. Starting from these premises, the paper proposes a common evaluation and benchmarking framework, providing a synthetic test bed, which enables implementation and comparison of OD estimation/updating algorithms and methodologies under "standardized" conditions. The framework, implemented in a platform available to interested parties upon request, has been flexibly designed and allows comparing a variety of approaches under various settings and conditions. Specifically, the structure and the key features of the framework are presented, along with a detailed experimental design for the application of different dynamic OD flow estimation algorithms. By way of example, applications to both offline/planning and on-line algorithms are presented, together with a demonstration of the extensibility of the presented framework to accommodate additional data sources. Keywords Traffic modelling, origin-destination (OD) estimation/updating, benchmarking platform. 1. Background and motivation Traffic congestion has been plaguing urban and interurban transportation systems everywhere for *Manuscript Click here to view linked References 1. TRUE CASE STUDY SETUP (FORWARD PROBLEM) 2. DESIGN OF EXPERIMENTAL SETUP

Time-Dependent Origin-Destination (OD) matrices are essential input for Dynamic Traffic Models such as microscopic and mesoscopic traffic simulators. Dynamic traffic models also support real-time traffic management decisions and they are traditionally used in the design and evaluation of Traffic Management and Information Systems (ATMS/ATIS). Timedependent OD estimations are typically based either on Kalman-Filtering or on bi-level mathematical programming, which can be considered in most cases as ad hoc heuristics. The advent of the new Information and Communication Technologies (ICT) provides new types of traffic data with higher quality and accuracy, which in turn allows new modeling hypotheses that lead to more computationally efficient algorithms. This paper presents ad hoc, Kalman Filtering procedures that explicitly exploit Bluetooth sensor traffic data , and it reports the numerical results from computational experiments performed at a network test site.

Origin-Destination (OD) trip matrices, which describe the patterns of traffic behavior across the network, are the primary data input used in principal traffic models and therefore, a critical requirement in all advanced systems that are supported by Dynamic Traffic Assignment models. However, because OD matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts which are provided by an existing layout of traffic counting stations. The availability of new traffic measurements provided by Information and Communication Technologies (ICT) applications allows more efficient algorithms, namely for the real-time estimation of OD matrices based on modified Kalman Filtering approaches exploiting the new data. The quality of the estimations depends on various factors, like the penetration of the ICT devices, the detection layout and the quality of the initial information. Concerning the feasibility of real-time applications, another key aspect is the computational performance of the proposed algorithms for urban networks of sensitive size. This paper presents the results of a set of computational experiments with a microscopic simulation of a network of the business district of Barcelona, which explore the sensitivity of the Kalman Filter estimates with respect to the values of the design factors, and its computational performance.