The novel ELASTIC software architecture, designed to satisfy the performance requirements of extreme-scale analytics through a novel elasticity concept that distributes workloads across the compute continuum, will be tested in a smart mobility use case deployed in the metropolitan area of Florence (Italy).

Specifically, ELASTIC will provide the required scalable computing infrastructure to the Florence tramway network, enhancing the tramway public transportation services as well as its interaction with the private vehicle transportation. The new elasticity concept will enable the efficient processing, through extreme-scale analytics, of multiple and heterogeneous streams of data collected from an extensive deployment of Internet of Things (IoT) sensors, located on board the tram vehicles, along the tramway lines, as well as on specific urban spots around the tram stations (e.g., traffic lights). At the same time, the proposed architecture will guarantee additional properties, known as non-functional requirements, which are inherited by the tramway system. In this specific use case, the desired non-functional requirements refer to the system operation with real-time guarantees, enhanced energy efficiency, high communication quality and security.


The ELASTIC smart city use case


The output of the real-time, secure and energy efficient extreme-scale analytics solutions enabled by ELASTIC will be, in turn, used to improve the overall Florence transportation network performance, thus enhancing the quality of life of citizens in terms of safe mobility and service availability. Furthermore, the fulfilment of the non-functional properties will be a significant step towards fully autonomous, highly reliable and efficient public transportation systems.


Screenshot of smart sensor video used in ELASTIC


As the project aims at developing the challenging software architecture needed to implement the next-generation urban mobility applications supporting extreme-scale analytics, three specific applications have been carefully identified to assess and highlight the benefits of ELASTIC technology for newly conceived tramway solutions:

Next Generation Autonomous Positioning (NGAP) and Advanced Driving Assistant System (ADAS)

The first use case application is a combination of two complementary functionalities, namely the NGAP and the ADAS, aiming to provide accurate information on the tram position and assist drivers in critical situations by informing them on the presence of obstacles in real-time, thus enhancing the passengers’ safety.

The NGAP will enable the accurate and real-time detection of the tram position through data collected from on-board inertial measurement units (IMU), satellite positioning information (GNSS) and other sensors. The positioning information will be estimated on-board the tram, and sent through a reliable connection to the tram operation control system on the ground.

This information will also enable the development of ADAS, implementing obstacle detection and collision avoidance functionalities based on an innovative data fusion algorithm combining the output of multiple sensors including radars, cameras and light detection and ranging (LIDAR) detectors. Data from additional sources, such as fixed sensors placed at strategic positions (e.g., road crossings), will also be integrated to increase the reliability of the system.

Predictive maintenance

Defective assets on the rail track represent a significant cause of hold ups on most rail and tram networks, causing one third of delays and sometimes rail trams’ suspensions, thus impacting citizens’ expectations and the normal operation of cities. Therefore, the early detection of symptoms associated with possible rail track wear is fundamental in order to resolve such problems as soon as possible, and even completely prevent them if possible.

The predictive maintenance application will monitor the rail track status and profile in real-time, enabling the identification of changes in equipment behavior that foreshadow failure. Furthermore, through offline analytics, potential correlations between unexpected detected obstacles (obtained through the NGAP/ADAS application) and rail track damages will be examined. The application will also provide recommendations, enabling maintenance teams to carry out remedial work before the asset starts to fail. Finally, the power consumption profile will also be monitored in real-time, in order to potentially minimize consumption and have an environmentally positive impact.

Interaction between the public and private transport in the City of Florence

In urban areas, public and private transport are continuously interacting, and high capacity public transport is typically granted a higher priority. However, such policies do not take into account the specific traffic dynamics of different scenarios. The increasing deployment of IoT solutions is providing cities with a dense network of sensors collecting different types of data that can be used to monitor performance of mobility services and their impact (traffic flows, travel times, air quality, etc.). ELASTIC will use this information to enhance the quality of the city traffic management through the application of extreme data analytics, providing valuable outputs for both users and operators that will enable them to:

(1) identify critical situations (e.g., vehicles crossing the intersection with the tram line despite having a red traffic light),

(2) optimize the local traffic regulation strategies (e.g., reduce the waiting time of cars at tram crossings through improved light priority management, or slow down trams to reduce a queue of waiting vehicles under traffic congestion, etc.)

The ELASTIC framework for enhancing the interaction between different transportation networks (i.e., public and private) through real-time analytics will be used as a reference for the Florence tramway system for the implementation of new features and mobility services in the future.