Combination of conventional survey and automated-processed-data
Digitally generated data coming from sensors and mobile phone apps on the other hand, have significant advantages in terms of fineness, coverage, efficiency, reliability and speed. ICT that collects real-time data offers the opportunity to improve conventional survey and traffic counting methods, enabling the comparison and sharing of data, decreasing sampling bias, reducing respondent burden and increasing quality. Thus, when accurately collected and processed, data represent a great source of information, indicators and user-experience valuable both for the Urban Authority and the citizens. For instance, processed data can help anticipate passenger flows and therefore, in the longer term, optimize transport-planning investments as it the case in Alberstlund. In Szeged, new sensors installed at Public Transport (PT) services contribute identifying patterns and optimizing PT capacities to adapt public transport services, routes and corridors. For the city of Ghent, the most important data source is a 3-years survey organized with the inhabitants. This survey allows the municipality, amongst others, to make modal split estimations, as well as estimations of vehicle ownership.
Shaping performance indicators with a user-centred approach
As travel experience is a decisive factor for citizens to opt for a given transport mode, it is crucial to develop New Key Performance Indicators (KPIs) that could reflect citizens’ satisfaction levels. As qualitative data collection can be made easier with sensors and smart phone applications, new KPI indicators could include personal travel experience and overall qualitative satisfaction levels, based on digitally generated data.
However, managing such diversified and massive amounts of automated data is challenging especially when data is created and collected with neither pre-defined nor specific purpose. Indeed, municipal processes are not always in place for cities to work with digital data and identifying their biases and blind spots. Therefore, municipalities need to develop data governance strategies.