Managing sensor traffic data and forecasting unusual behaviour propagation

Abstract

Sensor data on traffic events have prompted a wide range of research issues, related with the so-called ITS (Intelligent Transportation Systems). Data are delivered for both static (fixed) and mobile (embedded) sensors, generating large and complex spatio-temporal series. This scenario presents several research challenges, in spatio-temporal data management and data analysis. Management issues involve, for instance, data cleaning and data fusion to support queries at distinct spatial and temporal granularities. Analysis issues include the characterization of traffic behavior for given space and/or time windows, and detection of anomalous behavior (either due to sensor malfunction, or to traffic events). This paper contributes to the solution of some of these issues through a new kind of framework to manage static sensor data. Our work is based on combining research on analytical methods to process sensor data, and data management strategies to query these data. The first aspect is geared towards supporting pattern matching. This leads to a model to study and predict unusual traffic behavior along an urban road network. The second aspect deals with spatio-temporal database issues, taking into account information 280 Geoinformatica (2010) 14:279-305 produced by the model. This allows distinct granularities and modalities of analysis of sensor data in space and time. This work was conducted within a project that uses real data, with tests conducted on 1,000 sensors, during 3 years, in a large French city.

Information Visualization to Enhance Sensitivity and Selectivity in Biosensing

“…In a number of cases, the data are generated by a wide range of sensing devices, obtained by an equally large variety of sensor types. These may include electrical, electrochemical or optical sensors, satellite images, traffic (see for instance Medeiros et al [25]) and spectroscopic techniques. In problems that generate large amounts of correlated data, as in the measurements in multiple brain areas obtained over time with electrode arrays, it is essential to employ sophisticated data-analysis methods.…”

“…These problems may be addressed with feature selection methods [28] coupled with data cleaning and fusion. For traffic events in a major French city, Medeiros et al [25] combined analytical methods with data management strategies to handle spatio-temporal data. Feature selection is essential in many data analysis problems, including biosensor optimization.…”