Mobility Data Analytics

From Insights to Impact

As a defining feature of modern life, connectivity can significantly improve many aspects of mobility and transportation. This applies particularly to new settings like Mobility as a Service (MaaS): With passengers demanding easy access to convenient and fast transportation from A to B using a combination of available modes – be it a bus, a train, a ferry, a shared bike or one’s own feet – it is essential to keep a close eye on operational efficiency and service quality. While suitable data is increasingly available to address this challenge, the potential of data analytics remains largely untapped. However, it represents a rich source of information that can be utilized to enhance the passenger experience and drive operational excellence in the digital era.

From Big Data to Smart Data

Mobility apps and their users are an essential source of data. A mobility app, designed according to operators’ specific visuals and requirements, is the starting point for data insights. Each transaction made in a mobility app creates data, such as a HAFAS-based trip planner. Using this as a basis, data analytics is capable of understanding and determining patterns of mobility demand and their impact on the transportation network. These patterns can be used to make predictions on future passenger flows. Consequently, data analytics not only offers insights into current activity at particular locations, but also major commercial impact based on predictions on future mobility. As data protection is a sensitive issue, it is important to mention that many insights and results can already be gained by analyzing usage statistics. This can be done without collecting any person-specific data.

Read more about Big Data in our interview “Big Data Calling“.

Travelers’ willingness to share additional data can thus be rewarded with value adding services, such as more personalized and proactive  information. In addition to passenger apps, other sources like vehicle weight and occupancy, gate signals, road traffic information and weather data can be taken into consideration to gain further insights.