Summary

Automated data sources are increasingly being used in almost all fields of contemporary transport science. Empirical observations and quantitative findings derived from data can support decisions of various actors of the urban society, including policy makers, road and public transport operators, private service providers and travellers. The cost of data collection is rapidly decreasing, as most of the new data sources are necessary side products of automation and digitisation of the transport industry. Also, new technologies and telecommunications generate tremendous amount of information on transport users’ daily habits and travel preferences. Thus, planning and policy making can be facilitated with efficient data utilisation on both the demand and supply side of the mobility market. Despite the inevitable availability of automated data sources, making good use of them requires an investment of additional knowledge and analytical efforts. As the amount of data grows significantly faster than our learning and processing capabilities, much of the information remains hidden in untouched databases, or even get lost after a while due to the increasing cost of data storage. The paper highlights the key success factors determining the efficiency of data management interventions, and the benefits that the successful implementation of such policies could deliver for local communities and their surrounding economies.

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