Jun 24, 2016
Without substantial investment, by 2035 the UK will experience severe congestion across its transport infrastructure. Innovate UK commissioned this study to explore how emerging technologies might be able to address the problem of congestion by using capacity more efficiently, supporting different mode choices, and managing or reducing travel demand.
This study included three broad phases. First, we identified six key technologies that a range of experts think may influence the effectiveness and efficiency of the transport network: autonomous vehicles, next-generation ICT connectivity, advanced manufacturing (such as 3D printing), user apps / Big Data and intelligent processing, the Internet of Things, and novel materials and embedded sensors in infrastructure. Next we developed future scenarios to examine the influence of these technologies on travel. Three scenarios were developed, based around activities that contribute to travel demand: work/business, health, retail, long-distance business and leisure travel, and freight movement. The scenarios are: Driving Ahead, based on higher-than-forecast economic growth and widespread use of autonomous vehicles; Live Local, with moderate growth and use of travel substitution due to advances in communications technology; and Digital Divide, in which technologies advance but are not evenly distributed because of rising income inequality. In the third phase of the study, we interviewed experts and policymakers about the key policy implications of the scenarios, what innovation investments would be robust across scenarios, and what are the barriers and enablers for each technology. Using these responses, we developed a strategic roadmap to inform future UK policies and investments.
Using future transport scenarios to identify future policy and investment needs
Key technologies that could influence transport efficiency in 2035
Three future transport scenarios
What do these scenarios mean for policy and innovation investment?
Methodology for shortlisting key technologies