Can Humans and Machines Get Along to Drive Transport Forward

Daren Wood, Vision Director, Resonate

AI has historically received some negative and melodramatic publicity surrounding its ability to displace people from their jobs and quite literally assume a life of its own. In some areas of industry though, there is significant potential for AI to support and enhance human capabilities by working harmoniously. Keeping transport networks moving, combining big and messy decision making with big and messy data sets, is a great opportunity for collaboration between human and artificial intelligences.

Over the next decade, we’ll see an increasing amount of instrumentation across transport networks, providing a comprehensive digital portrait of the assets that make up a complex delivery system. This will enable the capability for powerful predictions about the status and availability of all the components, from the condition of vehicles and infrastructure to the build-up of crowds at stations. All of this will enable greater reliability of the network’s components, and provide earlier opportunities to take preventative action when necessary.

Figure 1 Resonate's technology applied to solving network operations in rail

Operating a transport network is about keeping on top of a large number of moving parts, while continually responding and adjusting the plan to ensure people and goods flow efficiently. For anybody working in the operations centers of a busy transport network, one of the biggest challenges is finding the opportunity to learn. Most working time is spent dealing with current and present issues, with little time available to review, practice and share experiences with colleagues. It’s like a football team that only ever plays competitive matches, with no time for training or access to the analysts collecting data on every move and touch of the ball. 

What if we could bring a twelfth player on during game time, whose only role was to watch, learn and speak up when warning signs were present. Here’s where the AI transport operator bot can extend the capabilities of the human team. The advantages held by AI are based around having a long, high resolution and perfect memory, able to see all movements across wide geographic networks; a memory that is based not just on personal experiences, but across all shift patterns and staff rosters. It can analyse objectively, detect patterns and process large amounts of data in real time without ever getting tired, ill or distracted. And if it is doing OK, replicate and repeat.

However powerful the machine, the decision maker has a number of challenges that remain hard for technology to reach (see the box for a flavour of these). Humans are good at negotiating, working through compromises and taking decisions in the moment where there’s an incomplete or imperfect picture. Side by side, the human operator is greatly enhanced by AI, providing the longer term memory, objective analysis and early warning of trouble emerging from the patterns in complex data sets. 
Predicting the evolution of artificial intelligence is hard and can fall anywhere between cautioning against false hope and visualising the end of human kind. In the meantime, there’s a great opportunity to align human and machine intelligence learning and development and to organise all the moving parts of the system to deliver more reliable, value for money, transport networks.

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