It all starts with maps, and continues with time tables of transport services (multi-modal...).
In other terms, geo-localised data, and time stamped data, can give raise to location and context dependent services, which are very valuable to users experiencing constraints:
- being a passenger, you are bound to a train carriage, bus, aircraft
- having to go from A to B, and reach B before time tB
- constrained by a budget, not being able to spend more than b
- well, being tired and hungry...
There is definitely a lot of potential in data for/with transport:
-data supporting a smoother transport experience
-data generated with/in transport.
This leads back to the economics of data, framed here in the particular context of transport where value may become more easily salient to suppliers of service of/in transport, and users of transport who become consumers of service in transport.
The expected output data of a transport big data system may include:
-for the Traveller, Quality of Experience and Safety
-for the Transport Operator
- Volume, cost, efficiency targets, in particular MTBF (Mean Time Between Failure), avoidance of delay due to “signal failure” = poor preventive maintenance
The expected usable input of a big data for transport system includes, for a better Operation:
(Supply-side, including trade unions intentions to strike)