Traffic density is one of the important variables to identify traffic states. Traffic management and traffic control require real-time estimation of traffic density as an input for large spatial and temporal coverage of the road network. Statistics offices responsible for the production and publishing of official traffic statistics for the benefit of policy makers often do not consider the use of this rich information source for a variety of reasons. The objective of this study is to theoretically link current attempts to exploit real-time monitoring information from the domain of dynamic traffic management with the usual practice of producing traffic statistics by statistics offices. Specifically, the study proposes a theoretical model for lane-level traffic density estimation for official statistics based on the use of data from the Global Positioning System (GPS) and loop detectors. Currently, the adoption of GPS data is seriously hampered because few vehicles are equipped with GPS transponders. By combining loop detector and GPS data, one can benefit from the relative quality of both data sources. In the current model, the definition of traffic density is applied on the basis of the cumulative traffic counts from loop detectors during the travel time of GPS vehicles that pass by the loop detectors. Furthermore, estimators are presented of the mean traffic density in the population by upscaling to the relevant parts of the road network and certain time periods. Statistical weighting strategy is applied for both temporal upscaling and spatial upscaling.

² Freight origin-destination (OD) information is increasingly important for understanding the influence of transportation on network congestion. Traditional OD estimation methods based on a single data source, usually loop detectors, are not easily transferred to freight OD estimation. However, alternative data capture technologies are nowadays available to gather traffic information. Examples are automatic number plate recognition (ANPR), Bluetooth scanners, and Weigh-in-Motion systems. This paper aims to develop feasible approaches based on Entropy Maximization and Bayesian Networks to estimate freight OD matrix using multiple sources of captured data. In the case of the A15 motorway in the Netherlands, we illustrate how the captured data is informative about transport behavior in the area and how the proposed methods lead to an estimation of the freight OD matrix.

1Predicting traffic demand becomes essential, either to understand the traffic state in the future or 2 to take necessary measures for alleviating the congestion in the next time period. Usually, an 3 origin destination matrix (OD) is used to represent traffic demand between two zones in 4 transportation planning. Vehicles are assumed to be homogenous and the trips of each vehicle are 5 examined separately. In fact, this traditional OD-matrix lacks of a behavioral basis and trip based 6 model structure. There is additionally another research stream of travel activity-based research 7 which digs into the individual travel behaviors. This stream really takes care of the trip chain for 8 travelers. But their research scope is on the attributes of the trips, ignoring the road network. In 9 order to link these two fields and to better predict traffic demand, we propose the concept of 10 Origin Destination Tuple (ODT), a sequence of dependent OD pairs. With the help of advanced 11 monitoring systems to identify and track vehicles in the road network, the additional uncertainties 12 from ODTs can be mitigated, reducing the under-specification more specifically. We propose the 13 Hierarchical Bayesian Networks mechanism in Gaussian Space with multi-process to get the 14 posterior of uncertain parameters. The model includes level and trend components to make 15 prediction of future traffic volumes. A case study demonstrates that the proposed method is 16 feasible to predict the demand and the path flow from cameras can reduce the uncertainty in the 17 estimation and prediction process, especially for the OD-tuples. 18 19 20

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