The technological advances in the transportation and automotive industry led to the use of new types of sensing systems more cost-effective and adapted to large-scale dense deployment. Those sensing techniques allow continuously gathering traffic measurements times series in different geospatial locations. The accuracy of the obtained raw measurements is often hindered by different factors related to the sensing environment and the sensing process itself and thus fail to capture the short-term traffic variations crucial for real-time traffic monitoring. In this paper, we propose the DGC-LSTM model for area-wide traffic estimation from error-prone measurements time series. The backbone of the DGC-LSTM model is a graph convolutional Long Short Term Memory model with a dynamic adjacency matrix. The adjacency matrix is learned and optimized during the model training. The adjacency matrix values are estimated from the set of contextual features that impact the dynamicity of the dependencies in both the spatial and temporal dimensions. Experiments on a realistic synthetic labelled Bluetooth counts dataset is used for model evaluation. Lastly, we highlight the importance of transfer learning methods to improve the model applicability by ensuring model adaptation to the new deployment site while avoiding the extensive data-labelling effort.

The technological advances in the transportation and automotive industry led to the use of new types of sensing systems more cost-effective and adapted to large-scale dense deployment. Those sensing techniques allow continuously gathering traffic measurements times series in different geospatial locations. The accuracy of the obtained raw measurements is often hindered by different factors related to the sensing environment and the sensing process itself and thus fail to capture the short-term traffic variations crucial for real-time traffic monitoring. In this paper, we propose the DGC-LSTM model for area-wide traffic estimation from error-prone measurements time series. The backbone of the DGC-LSTM model is a graph convolutional Long Short Term Memory model with a dynamic adjacency matrix. The adjacency matrix is learned and optimized during the model training. The adjacency matrix values are estimated from the set of contextual features that impact the dynamicity of the dependencies in both the spatial and temporal dimensions. Experiments on a realistic synthetic labelled Bluetooth counts dataset is used for model evaluation. Lastly, we highlight the importance of transfer learning methods to improve the model applicability by ensuring model adaptation to the new deployment site while avoiding the extensive data-labelling effort.

We propose a simulation model of Bluetooth (BT) passive scanning for vehicular traffic monitoring. The model is designed to simulate packet detection in the physical and Medium Access Control (MAC) layers for fixed BT sensors. At the physical layer, we consider the radio propagation effects of path loss, small- and large-scale fading. At the MAC layer, we model channel hopping and packet collisions. Associated to a vehicular traffic simulator, our proposal provides a cost-efficient way to generate large-scale BT traffic measurement datasets including both sensor and ground-truth data, avoiding deployment costs. We validate the model by comparing the simulation output to experimental data and we show a simulation use-case of vehicular traffic monitoring in a complex urban context.

During the last decade, Bluetooth has become a widespread feature in the automobile industry, meaning that its signal activity can be correlated with road traffic. Using this technology as a means for assessing traffic is very cost-effective and has a low impact on the infrastructure. Nevertheless, unlike other techniques, it does not provide direct sensing of vehicles only. Statistical analysis methods need to be applied to the collected data. In this paper, we first propose a machine learning method for traffic flow estimation using Bluetooth sensors. We also propose a method for estimating the mean travel speed between two sensors. The performance of the proposed methods is evaluated through eight weeks of experimentation. Finally, we envision a potential solution for building real-time Origin- Destination matrices.