Manuel Bullejos

A Kalman filter approach for exploiting bluetooth traffic data when estimating time-dependent OD matrices

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 advanced traffic traffic management and information systems (ATMS/ATIS). Time-dependent OD estimations are typically based either on Kalman filtering or on bilevel 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 article 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.

Robustness and Computational Efficiency of Kalman Filter Estimator of Time-Dependent Origin-Destination Matrices

Origin–destination (O-D) trip matrices that describe the patterns of traffic behavior across a network are the primary data input used in principal traffic models and, therefore, a critical requirement in all advanced systems supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The availability of new traffic measurements provided by information and communication technologies (ICT) allows more efficient algorithms, namely for real-time estimation of O-D matrices that are based on modified Kalman filtering approaches to exploit the new data. The quality of the estimations depends on various factors such as the penetration of the ICT devices, the detection layout, and the quality of the initial information. The feasibility of real-time applications depends on 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 the network of Barcelonas central business district that explore the sensitivity of the Kalman filter estimates in relation to design factor values.

Robustness and Computational Efficiency of Kalman Filter Estimator of Time-Dependent Origin–Destination Matrices: Exploiting Traffic Measurements from Information and Communications Technologies

Origin–destination (O-D) trip matrices that describe the patterns of traffic behavior across a network are the primary data input used in principal traffic models and, therefore, a critical requirement in all advanced systems supported by dynamic traffic assignment models. However, because O-D matrices are not directly observable, the current practice consists of adjusting an initial or seed matrix from link flow counts that are provided by an existing layout of traffic-counting stations. The availability of new traffic measurements provided by information and communication technologies (ICT) allows more efficient algorithms, namely for real-time estimation of O-D matrices that are based on modified Kalman filtering approaches to exploit the new data. The quality of the estimations depends on various factors such as the penetration of the ICT devices, the detection layout, and the quality of the initial information. The feasibility of real-time applications depends on 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 the network of Barcelonas central business district that explore the sensitivity of the Kalman filter estimates in relation to design factor values.