Resumen de Contribucions per a sistemes intel·ligents de transport: anàlisi de big data per obtenir dades fiables amb valor
Transportation industry has entered the era of big data. Part of the data disseminated by connected vehicles and infrastructure is being exploited by Intelligent Transport Systems (ITS), advanced applications in which information and communication technologies are applied in the field of road transport traffic management. In the upcoming future, all road vehicles are likely to communicate with one another and the surrounding infrastructure, for example, to warn others about traffic incidents or poor road conditions. But, the connectivity and data analytics requirements for the envisaged use cases are far from covered.
Dedicated Short Range Communication (DSRC) is a higher layer standard based on the evolution of IEEE 802.11p Wi-Fi, one of the main technologies that support the first generation of vehicle-to-everything (V2X) communication. The first part of this dissertation addresses the improvement of IEEE 802.11p direct vehicular-to-infrastructure communication in the ITS data acquisition layer, which suffers from a well-known scalability problem. The analysis carried out concludes that the data dissemination of standardized protocols is not reliable enough to support safety applications that depend on ITS roadside units located in intersection areas. To solve this, novel infrastructure-oriented criteria is proposed to adapt the communication parameters and an intersection assistance protocol is designed in compliance with the standards to increase the reliability of the data acquisition layer up to the point where safety applications can be implemented.
As ITS data acquisition layer produces massive amounts of data, it requires data aggregation and processing in the data analytics and application layer to enable more advanced use cases, mission-critical applications that have the potential impact to reduce problems such as road safety, pollution, traffic congestion and transportation costs. The second part of the dissertation proposes a generative deep learning model that can be used in an unsupervised manner to solve multiple ITS challenges. Big data collected by ITS is exploited and transformed to an asset for safety applications and decision-making, without the need for additional knowledge nor labeled data. The model allows to efficiently compress traffic data and forecast, impute missing values, select the best data and models for a specific problem and detect anomalous traffic data at the same time.
The last part of the dissertation is motivated by the growing concern generated by the efficiency of ITS solutions and the large amount of data expected to be processed. The presented algorithm allows to automatically and efficiently derive the minimum expression architecture of the model that provides maximal compressed representations that inform about the original traffic data. In this way, the performance of the subsequent ITS traffic forecasting system is not adversely affected, but benefits from data being represented with fewer dimensions, which is vitally important in the age of big data. The basis of the algorithm is taken from theoretical concepts of Information Theory applied to neural networks, going a step beyond the current available methods that are based on trial and error.
