Topic 2 Data analysis


With the digital revolution, the new tools available (smartphones, wearables, tablets, etc.), online services (e.g. E-commerce, etc.), social media, applications, Internet of Things, have contributed to generating a large amount of data. The ability to collect, process and interpret this data can translate into greater value for organizations, following the generation of new knowledge that can be significant in decision making process.

Big Data Analysis is the analysis that is carried out on a complex and varied set of data (big data) aimed at extrapolating, through various techniques, information that can help the organization to have the right tools to complete the evaluation process.

This also applies to more traditional sectors, such as public transport.

  1. Descriptive analysis (“what happened”?) to better interpret and understand a phenomenon of interest;
  2. Diagnostic analysis (“why did it happen”?) aimed at identifying the causes
  3. Predictive analysis (“what might happen in the future”?) allows to extract useful information in order to determine future trends;
  4. Prescriptive analysis (“what should we do about it”?) allows to study any corrective actions to be taken.


Subtopic 2.1 Big Data in Public Transport

To become a data-enabled company it is necessary to have a clear awareness of the know-how, of the strategies to be implemented for the collection, processing, interpretation of data, of the value of the data and an in-depth knowledge of the rules on privacy and use of data (UITP, 2021).

  1. Customer Data ( personal data, travel behaviour data, customer journey data and CRM data)
  2. Operational Data (Operations monitoring and control, disruption management, ticket sales, journey reliability and real-time information, management information, staff information, security data, predictive maintenance of infrastructure, asset and fleet)
  3. Mobility Data (Network description, timetable information, car traffic and other mobility modes data, parking data and accessibility data)
  4. Exogenous Data (weather, disruptions or scheduling (big events, schools, etc).


 ⃰ CRM: Customer Relationship Management