By collaborating with Transport for Wales Rail, LNER, and Network Rail through the Alt Labs run TfW Labsand LNER FutureLabs programmes of 2020 and2022, Emu Analytics have evolved their Flo.w digital twin to analyse, visualise, and help predict where trespass incidents are likely to happen to enable strategic intervention strategies and more effective prevention of trespass on the railway


Flo.w allows rail operatorsand partners tocontinuously monitortrespass risk on the railwayand access insights toimplement more effectiveprevention strategies.


Flo.w consolidates a range ofdata to identify risk profilesfor target locations for earlyintervention strategies -helping to predict andprevent potential trespassincidents.


Centralised data collectionand processing madeaccessible and dynamicenables different types ofusers and rail organisationsto focus on their ownparticular use and insights.

The Challenge

Trespass on the railway is a growing concern for railway operators and society as a whole, leading to a large number of injuries and fatalities everyyear. In 2019, Network Rail reported 13,500 trespass incidents on the railway, costing train operators £55m in delay costs, with 500 train services per dayimpacted on average. In 2021, Network Rail reported the number of trespass incidents that year had increased to 19,000, showing a 50% increase oftrespass incidents in two years, proving the challenge is worsening.

Trespass on the railway not only leads to significant financial losses for train operators, but it also poses a severe risk to the safety of passengers and staff. As part of the FutureLabs Innovation programme of 2020 and 2022, EmuAnalytics worked closely with Transport for Wales Rai, LNER and Network Rail to address the challenge of trespass on the railway through innovative use of their geospatial analytics and visualisation software Flo.w.

The Solution

Emu Analytics sourced relevant geospatial data and a wide range of datasets that cover both rail and external factors such as social, economic, andenvironmental issues.

They utilised their innovative Digital Twin software, Flo.w, to seamlessly collate and analyse this data, enabling the solution to identify specific risk profiles and hot spots related to trespassing on railway tracks, as well as their proximity to potentially vulnerable locations, such as schools, pubs, mental health facilities, and more.

Together, these data sets build up a valuable picture of where future incidents are more likely to take place, empowering the relevant rail sector organisations to more effectively implement strategic interventions toavoid future incidents – in turn improving railway safety and minimising the impact of trespass incidents on rail services. By presenting this analysis through an accessible and dynamic interface, rail operators can better allocate resources, collaborate across organisations, and effortlessly gain insights on the effectiveness of intervention campaigns.

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