Software fights Underground leaf problem

One of London's most heavily used Underground lines can run more quickly during autumn, thanks to a software program that predicts the impact of an unlikely enemy: falling leaves.

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Long blamed for train delays in Europe, autumnal leaves pose a danger on the tracks by forming a slippery coating that makes acceleration and braking more difficult. The leaves also interrupt the electrical conductivity between the train and track needed for the signalling system to work properly.

The London Underground's Central Line is particularly vulnerable, as around 70% of the 46 mile line is above ground, parts of which go through wooded areas.

Software engineers have created a program, in use for the last five years, to help predict when the leaf fall will be the heaviest. The software helps train drivers determine whether they can use a computer controlled system, Automatic Train Operation (ATO), which drives trains harder and faster.

ATO maximises acceleration and braking, which means more Central Line trains can run than if under manual control by drivers, said Gilbert Rowe, project engineer for the Central Line's Leaf Fall Mitigation programme, part of the Metronet Rail Group that runs much of the Tube.

The track must be clean for the trains to run in ATO, Rowe said. The software program, called the Adhesion Controller's Condition Assessment Tool (ACCAT), compiles a variety of data to determine track conditions.

ACCAT downloads regular weather reports, including rainfall and wind speed, along with data from moisture sensors on the tracks. Train drivers can also report conditions. ACCAT also incorporates information from an annual vegetation survey, which pinpoints what species of trees are located next to specific portions of track, Rowe said.

That information is important since different kinds of trees shed their leaves at different times during the fall. For example, oak trees line the tracks between the stops of Epping and Woodford on the north-eastern part of the Central Line, which means heavy leaf fall in early December, Rowe said.

The data and trends are incorporated into the software's logic, which uses algorithms to make a recommendation on risk.

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