Deliverable 1.3 Big Data Methodologies, Tools and Infrastructures

Big Data opens up new opportunities to define “Intelligent” mobility and transportation solutions. The transportation industry is a leader in creating the so-called Internet of Everything. Each day vast volumes of data are generated through sensors in passenger counting and vehicle locator systems and ticketing and fare collection systems, just to name a few.

The goal is to create value out of this amount of data, by providing a comprehensive picture of what’s happening, using business analytics, leveraging big data tools and predictive analytics, to help transportation agencies improve operations, reduce costs and hopefully better serve travelers.

The technical challenge is that much of this Big Data is non-standard data (e.g., social, geospatial or sensor-generated data that does not an easy fit into traditional, structured, relational data warehouses or databases).

An additional challenge is that with such an amount of real-time structured and unstructured data captured from a variety of sources, it is difficult to determine which data is most valuable. Terabytes of data are collected and result in an added complexity to the underlying IT infrastructures.

These terabytes of data require immense amounts of storage in silo after silo of transportation operator data centers. In order to analyze Big Data, an appropriate Data Infrastructure needs to be in place to:

  1. store and maintain data
  2. analyze data
  3. present results in a clear visual way

Several Big Data platforms have been proposed recently, open source and proprietary. In order to tackle the demands and challenges in the transportation domain, an optimal stack of Big Data technologies needs to be selected and designed based on the application requirements.

This is not an easy task.

This report, which is a follow up of Deliverable 1.1, offers an in-depth introduction to relevant technologies for Big Data Analytics and Big Data Management. It also looks at how these technologies are applied to build a Big Data Platform suitable for the transport sector. We present in detail how application-specific benchmarking can be used in order to evaluate which Big Data technologies are most suited for the domain. We conclude the report with an applied example of using data analytics for urban mobility.

This document offers the reader a technical insight into existing Big Data technologies at various levels: software management, data platform, and application. In order to evaluate which specific software components in the Big Data stack are more suitable for transport applications, with high volume and high-velocity requirements, a benchmarking approach is presented.

The future of data analytics in transportation has many applications and opportunities.

The main challenge is using significantly improved technologies and methods to gather and understand the data in order for business decisions to be informed by better insights

LeMO, 3rd project meeting in Sogndal, Norway 19-21 June 2018

The third LeMO project meeting was held in Sogndal, Norway on the 19th to 21st of June 2018. All partners gathered to discuss the progress of ongoing work packages − Setting the stage on big data in transport (WP1), Institutional and governmental issues and barriers (WP2) and Creating shared value (WP5) −and to elaborate on the plan for the forthcoming work package on case studies (WP3). The impact manager of TransformingTransport project, Akrivi Kiousi, also attended the meeting and presented the TransformingTransport project. The attendees also discussed opportunities for cooperation with our sister project NOESIS and TransformingTransport project. 

On 20th June LeMO consortium and the Advisory Reference Group (ARG) met for the first time to discuss ongoing work including including how can policies accelerate or hinder the use of big data in transport?. The ARG members Daniel Jacques (European Commission), Norbert Schaffitzel (Deutsche Bahn Systel GmbH), and Guro Marie Dyngen (Statens vegvesen) provided valuable input on big data policy aspects in transport sector.  The intense discussions showed how much the project has progressed and how much more we know about the institutional, political & technological back-drop to bigdata in transport.

The last day of LeMO meeting focused on seven case studies (WP3). The objective of this discussion was to brainstorm on seven case study topics, and distribution and planning of case studies work.

The meeting was hosted by the project coordinator, Western Norway Research Institute.

The first H2020 LeMO Newsletter is out now

The first H2020 LeMO Newsletter is published on the project website with relevant project news. The newsletter introduces you to project’s latest developments. 

In the current Issue of our Newsletter you can read our project’s news from its start until June 2018. Also, have a look at upcoming activities and publications that are related to the thematic of the project!