Transforming Transport (Parallel Session 1.3)
- Overview of the TT project and main challenges/Impact in the transport sector
- – What is this and why is it important – Adrián Irala (Indra)
- Introduction of the importance of Big Data in the logistics domain
- The TRIUMPH project – Matthias Prandtstetter (AIT)
- TT Ports as Intelligent Logistics Hubs- Mr Daniel Sáez Domingo (ITI)
- The CogLO Project – Kostas Kalaboukas (SingularLogic SA) & Giacomo Morabito (CNIT)
- Big-Data driven insights in the Supply Chain domain: The case of SELIS Project – Towards a Shared European Logistics Intelligent Information Space – Dr Ioannis Konstantinou (ICCS-CSLAB, NTUA)
- Introduction to the importance of Big Data in the Aviation domain:
- ICARUS project – Fenareti Lampathaki (Suite5)
- TT project Smart Turnaround and Passenger Flow pilot – David Scarlatti (David Scarlatti (Boeing R&T – Europe)
- Introduction of the importance of Big Data in the Rail domain:
- The CONNECTIVE project – Big Data in the Shift2Rail JU Programme Connecting and Analysing the Digital Transport Ecosystem – Javier Saralegui (Indra)
- AI Opportunities in Mobility & Transport – Andreas Metzger (Paluno)
- Discussion on Q&A provided by the speakers and audience with the panel
- Moderator: Mrs Vivian Kiousi (INTRASOFT International SA & BDVA Mobility and Logistics Subgroup leader) briefing also on intros of every domain depicted in the agenda
- Rodrigo Castiñeira – R&D Program Manager (Indra’s Transport Business Unit) & Big Data and Big Data Lighthouse Coordinator (EU Project TransformingTransport)
- Vivian Kiousi TT – Impact Manager (INTRASOFT INTL)
- Andreas Metzger – Senior Business Development Manager & TT Scientific and Technical Coordinator (UDE)
From rising congestion to increased demand for public and commercial transit, the travel behaviour and transportation preferences of dwellers and companies are changing fast. At the heart of each of the current domain, challenges include the need for data and its capabilities and constraints as well as the need for good data. since currently in all modes of transport, there is a considerable quantity and diversity of data made available by policy changes to operators. Their use can improve performance, efficiency, service provision, safety and security. Data also enables operators to manage demand conflicts, customer service, environmental impacts and innovation.
Big Data plays a significant role in working towards the resolution of the abovementioned challenges inclusive on how smart cities and companies obtain their transportation targets and use and deploy ICT to enhance their transportation networks. Thanks to modern technology, data collection tools and analytics don’t have to slow transportation professionals down anymore in fact, these tools can be some of planners’ most valuable assets. To discuss on all these challenges TT invites willing contributors to a dedicated session that can showcase sustainable and efficient Big Data solutions in the domains of logistics, aviation and rail.
TransformingTransport project, in a continuous, seek for collaboration with other relevant projects and domain experts attempts to facilitate the discussion of making transport more sustainable and efficient. Accordingly, and in addition to presenting the key topics in relation to Big Data and highlighted case studies the various slots will rely on examining various questions by surveying their relevance after the various presentations. Hence, invite the audience to respond to showcased questions below and beyond.
The session aims to provide an overview of the below topics and more particularly:
- Short description overview of the TT project and main challenges /Impact in the transport sector (Adrián Irala, INDRA) (5 min)
- Introduction of the importance of Big Data in the logistics domain (5min) total session length (25 min)
- (Case Study TRIUMPH project) (5 min) (Matthias Prandtstetter, AIT).
In the TRIUMPH project we proofed with a container terminal simulation the benefit of accurate estimated time of arrival predictions of incoming containers. These ETA predictions were based on a large dataset of travel time observations for road transportation as well as inland navigation. The outcome of the simulation was that on the one hand the energy consumption of the container terminal for reshuffling the containers could be significantly reduced while on the other hand the flow of goods could be improved since terminal operations could be optimized.
- Case study CogLO project (5 min) (Kostas Kalaboukas SINGULAR, Giacomo Morabito CNIT)
COG-LO aims at providing innovative ICT solutions and business models for the Logistics Operation stakeholders to overcome the above-mentioned challenges. The main goal of the project is to create the necessary framework and tools that will enable future logistics processes to become more cognitive and collaborative-interoperable by: a) adding cognitive behaviour to all involved Logistics
- Case study TT Ports as Intelligent Logistics Hubs (5min) (Daniel Saez Domingo, ITI)).
The two project pilots under this domain (Valencia Sea Port and Duisport Inland Port) are developing algorithms using Big Data to improve logistics operations in ports. This involves using predictive maintenance technology to identify failures in a spreader, the device used for lifting containers and cargo. Efforts concentrate on the development of a decision-making cockpit to support logistics planning
- Case study Selis project: Big-Data driven insights in the Supply Chain domain: The case of SELIS Project – Towards a Shared European Logistics Intelligent Information Space (5min) (Dr. Ioannis Konstantinou, ICCS-CSLAB NTUA)
The SELIS Big Data Stack offers a suite of available descriptive and predictive (i.e., machine learning) distributed algorithms (deep neural networks, support vector machines, etc.) over collected data in the form of recipes to solve visibility, prediction and classification tasks. SELIS is offered either as a service through a cloud deployment of choice, or as a package that can be downloaded and installed inside the company’s premises. SELIS recipes are being developed for 8 different Living Labs and 10 use cases. We showcase how they solve a series of problems across the supply chain, indicatively the stock optimization problem for a large retailer and the route optimization / ETA prediction problem for a large railway undertaker.
- Introduction of the importance of Big Data in the Aviation domain (5min) total session length (15min)
- Case Study: ICARUS project (5 min) (Fenareti Lampathaki)
ICARUS will address critical barriers for the adoption of Big Data in the aviation industry (e.g. data fragmentation, data provenance, data licensing and ownership, data veracity), and will enable aviation-related Big Data scenarios for EU-based companies, organizations and scientists, through a multi-sided platform that will allow exploration, curation, integration and deep analysis of original, synthesized and derivative data characterized by different velocity, variety and volume in a trusted and fair manner
- TT project Smart Turnaround and Passenger Flow pilot (5 min) (David Scarlatti, Boeing R&T – Europe)
Jeppesen is working together with Boeing and SEA in this replication. The goal of the pilot is to improve aircraft turnaround processes at airports. As one of the key factors for increased operational efficiency is reduced down time of an aircraft, which means less time on ground, the replication pilot focuses on ETA (estimated time of arrival) prediction and airport turnaround.
- Introduction of the importance of Big Data in the Rail domain (5 min) total session length (10 min)
- The CONNECTIVE project: Big Data in the Shift2Rail JU Programme Connecting and Analysing the Digital Transport Ecosystem (Javier Saralegui, Indra) (5 min).
- AI Opportunities in Mobility & Transport (A. Metzger, paluno) (5 min)
- Discussion points (30 min discussion)
- Which new services can we implement Existing tools and technologies used across other sectors
- Can we understand the user in a better way to set-up better services with a better customer experience?
- Which technological, business, and policy-related challenges we have to overcome?
- Which solutions are possible?
- Can we make the services more cost-efficient for the operators?
The session will further reach out to the audience during and after the various presentations to gather valuable feedback that aims in the formation of a common policy roadmap relating to the topics identified for Big Data in transport. Ultimately, the results of the session, as fed by experts (including attendees of EBDVF 2018), will enable the identification of industry-led recommendations in relation to Big Data, with a focus on the transport sector to challenge the identified opportunities, barriers and limitation to exploit Big Data to achieve efficiency.