Emu Analytics are proud to share that we were “highly commended” by the judges of the Transport for Wales Rail (TfW Rail) Innovation Lab Demo Day. Within a 12 week accelerator programme, our team proved how our software solution Flo.w offers an essential tool in tackling challenges across railway networks with immediate results and great potential to expand the project. We are looking forward to working together in the future with Transport for Wales.
In our presentation, we showcased how the use of real-time and non-traditional data across the rail network can produce a much clearer understanding of causes and impacts of delays at unstaffed stations across the TfW network. Flo.w’s high performant capacity and inclusive user design to both handle and communicate enormous amounts of data in an easy-to-understand way revealed the power of geospatial analytics for the rail industry.
Thanks to Flo.w, pressing questions - such as where, why and when patterns of incidents from trespassing & vandalism through to legitimate requests for passenger assistance take place - can be answered with enabling TfW Rail to introduce mitigating strategies.
Boost Insight across Rail Network with Flo.w
Throughout the innovation process Flo.w’s application demonstrated how the mixing and analysing of data from the rail sector with external data is key to uncover new correlations between incidents and their surroundings. TfW Rail not only received a new and broader picture of an issue but also could develop strategies based on an effective data-driven approach that is accessible for everyone across the organisation. For instance, Flo.w made apparent when trespassing incidents occur at certain times of the day or whether there were correlations to the surrounding geography.
The Power of Real-Time Analytics
Such analysis was particularly helpful to determine the impact of these incidents on both train services and passengers - with a specific focus on the unstaffed stations. By using real-time train position data from Network Rail, Flo.w shows not only moving trains on a map but continuously produces calculations of prolonged stopping time of train services at those stations. Through combining schedule data and determining where the dwell time at a station is greater than the schedule states, Flo.w can flag both delays and their origins, identify knock-on effect as well as highlight broader patterns of behaviour.
Flo.w’s Versatile Applications
Emu also demonstrated the value of further application such as integrated passenger counts, derived from Wifi devices at stations (the data provider was WiFi Spark). This delivered a real-time insight in how many passengers are potentially impacted by the service delays, permitting TfW Rail to improve customer experience during such circumstances. Flo.w also has no problem of incorporating real-time data of weather conditions such as flood-alters from Natural Resources Wales and the Environmental Agency, which similarly affect the performance of train services. For the context of Covid-19, we included the data derived from video analytics by our lab peers, Route Konnect, who’s technology identifies and measures who wears face masks on the platform on who does not.
During the accelerator, Flo.w revealed the importance of unlocking the value of data by synthesising, analysing and visualising a broad spectrum of data sets to ensure that a complex system such as the rail networks runs as smoothly and efficiently as possible.
If you are interested in finding out more about our software Flo.w and how it could be help your organisation, don’t hesitate to email our Business Development Executive Martine Skaret via martine.skaret@emu-analytics.com