Big Data Analytics for vehicle multisensory anomalies detection

Autonomous driving is assisted by different sensors, each providing information about certain parameters. What we are looking for is an integrated perspective of all these parameters to drive us into better decisions. To achieve this goal, a system that can handle these Big Data issues regarding volume, velocity and variety is needed. This paper aims to design and develop a real-time Big Data Warehouse repository, integrating the data generated by the multiple sensors developed in the context of IVS (In-Vehicle Sensing) systems; the data to be stored in this repository should be merged, which will imply its processing, consolidation and preparation for the analytical mechanisms that will be required. This multisensory fusion is important because it allows the integration of different perspectives in terms of sensor data, since they complement each other. Therefore, it can enrich the entire analysis process at the decision-making level, for instance, understanding what is going on inside the cockpit.This work has been supported by FCT – Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020 and by the European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project nº 039334; Funding Reference: POCI-01-0247-FEDER-039334]

BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform

Novel Big Data-supported dynamic toll charging system: Impact assessment on Portugal’s shadow-toll highways

Traffic congestion is a huge problem in many countries. It affects not only the inner workings of cities but also the quality of life of the people that endure it. In Portugal, traffic congestion happens mainly on national/urban roads, and this phenomenon has increased since the introduction of the so called shadow-toll systems in highways that were free to use. This work proposes a toll charging system that relies on a novel dynamic congestion charging scheme, supported by state of the art Big Data technologies, in order to shift traffic from national/urban roads to tolled highways, taking into account not only the Quality of Service of the highways and national roads, but also the competitiveness of toll prices for users. This Intelligent Transportation System was tested and validated in a real-world scenario with one of the biggest freight logistics companies in Portugal and with the Portuguese public road infrastructure operator.This work was performed under the scope of the OPTIMUM Project - Multi-source Big Data Fusion Driven Proactivity for Intelligent Mobility, grant agreement number 636160-2, funded by the European Union's Horizon 2020 research and innovation programme.Accepted versio