Big Data enables better urban transport networks | 5/13/2019 | Staff
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The SIADE SaaS (Spatial Decision Support System for Transportation Planning) project essentially marks a change in the positioning of Spanish SME Terrain Technologies. From a consultancy service built around an algorithm to infer passengers' destinations, the company requested support from Horizon 2020 to become a software supplier.

Now, cities across Europe can benefit from a solution enriching Big Data with a spatial component, enabling complex analysis of travellers' behaviour to improve public transport networks.

María - J - Arguelles - Project - Coordinator

María J. Arguelles, project coordinator, tells us more about the company's solutions and accomplishments so far.

How can Big Data help provide a better public transport experience in Europe?

María - J - Arguelles - Smartcard - Systems

María J. Arguelles: Smartcard ticketing systems currently available in many cities' public transport systems enable a vast amount of data. These datasets reflect how people behave, which in turn means it is possible to assess their transport needs and provide an accurate picture of their habits, either as groups (based on fare types, such as student, elderly, etc.) or at an individual level. Thanks to Big Data analytics, we can adapt public transport to these needs, plan new services, minimise walking time, etc.

What have been the shortcomings of attempts to use this data so far, and how does your software stand out in this regard?

Big - Data - Complexity - Something - '5

It is important to clarify that Big Data brings great complexity to transport because of something inherent to it: what is commonly defined as the '5 Vs' (volume, velocity, veracity, variety and value).

For example, a large amount of data volume implies large storage capacity. We can't forget that cities like Madrid, for example, generate almost 500 million trips per year, and around 1.2 billion for the whole of the greater Madrid area which is almost as many as in Istanbul.

Variety - Dimension - Datasets - Sources - Bus

The variety dimension involves datasets generated from different sources such as bus validators or mobile phones, while veracity...
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