Data-Driven Transport Policy

International Transport Forum (2016)

data-driven-transport-policy-page-001What issues arise with the increased use of location data and require special attention regarding privacy, trust and security? Given that much of this data is produced by commercial actors and is often central to their business strategies, what new models for accessing and sharing data would allow collaboration between public and private sector in sourcing, accessing or co-creating data for better managing transport operations and improve the planning transport networks? Building on the 2015 study Big Data and Transport: Understanding and Assessing Options, this report presents the findings of an extensive exploration of these two broad themes at a workshop on “21st Century Public Interest Data Sharing” in Paris in November 2015, which involved a wide range of experts and stakeholders brought together under the auspices of the International Transport Forum’s Corporate Partnership Board.

Data are essential to the planning, delivery and management of transport services and infrastructure – whether data covering home and work locations, leisure destinations and demand for travel between these and others. Data are also necessary for ensuring the safe operation of traffic, to respond to incidents in real-time and to understand and address crash patterns and trends. Increasingly, vehicle and map data will become essential for supporting higher and higher levels of automated driving. Much of transport-related data has a geospatial component that allows for a more detailed understanding of where people are, where they are travelling, in what conditions and in some cases how and for what purpose. This data is being sensed in new ways, from a broadening array of sensing platforms and in a wide range of formats with several recognised advantages over traditional data-collection methods, notably scale (coverage of entire transport networks) and latency/frequency of data collection (24 hours a day; 365 days a year; in many cases in real-time).

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