A track circuit is an electrical system that detects the presence of a train on the tracks by passing a current through the rails, which acts as a conductor. In its initial form, track circuits consisted of a battery and a relay with adjustable resistors to set the transmitted signal gain and receiver operating point. Sections of track are electrically isolated by insulated joints in each rail. The transmitted signal travels through a single rail, through the relay at the opposite end, then returning to the transmitter through the other rail. Track circuits follow the closed loop principle, which means that any failure results in the safest state (track occupied) as shown in Figure 1. Because of this, track circuits also provide detection of broken rails.Figure 1: Track circuit behaviour schema Nowadays, there are many types of track circuits. The last state of the art ones provide enhanced performance, integrating sophisticated signalling systems to improve operation and safety. Track-circuit failures have an important impact as they imply a stop of operations and an economic impact for both the railway operator and its customers (1).

Researchers have developed arrays of AI-controlled cameras and microphones to identify animals and birds and to monitor their movements in the wild – technology, they say, that should help tackle Britain's growing biodiversity problem. The robot monitors have been tested at three sites and have captured sounds and images from which computers were able to identify specific species and map their locations. Dozens of different birds were recognised from their songs while foxes, deer, hedgehogs and bats were pinpointed and identified by AI analysis. No human observers are involved. "The crucial point is the scale of the operation," said Anthony Dancer, a conservation specialist at the Zoological Society of London (ZSL).

The latest demonstrator from startup hyperTunnel was built at the firm's R&D facility in the North Hampshire Downs. The approach is claimed to be friendlier to the environment and will use sustainable materials such as low-carbon concrete. It could also drastically improve safety in the tunnelling sector because no humans need to enter the structure during construction. A fleet of'hyperBot' robots enters the ground via an arch of high-density plastic pipes and, once inside, can 3D print the tunnel shell by deploying construction material directly into the ground. The 6m-long, 2m-high and 2m-wide Peak XV'pedestrian-scale' tunnel has been delivered as part of a project for Network Rail.

The project, led by high-tech SME, OpenSpace Group Ltd, is part of HS2 Ltd's Innovation programme and brings together the company delivering Britain's new high speed rail network with Network Rail; intercity train operator, Avanti West Coast; and the University of Birmingham. The six month proof-of-concept project will use Artificial Intelligence (AI) and LiDAR technology to digitise passenger flow data and inform the design of future HS2 stations. Traditionally stations have been designed, built and run in a way that focused on smooth running of trains for the benefit of passengers. However, Future Stations Living Lab will put passengers at the heart of design by creating a real-time replica of the existing Euston station in London. Central to the project is the combination of Artificial Intelligence (AI) systems with cutting-edge LiDAR sensors that will capture highly accurate people movement data at Euston's forecourt, concourse and ticket gates.

Artificial intelligence (AI) trials have shown that lineside vegetation may be monitored securely, inexpensively, rapidly, and at scale by identifying species of trees and other plants from images obtained by on-train cameras. Due to safety considerations, the size of Britain's 20,000-mile rail network, and the number of specialist surveyors required, monitoring flora and fauna on the side of a railway track to promote improved management of lineside ecosystems is exceedingly challenging. However, Network Rail has been collaborating with the UK Centre for Ecology and Hydrology (UKCEH) and technology firm Keen AI to create creative ways to remotely monitor biodiversity. Researchers have shown that AI can recognize invading species by their tracks, as well as native trees that may be threatened by diseases like ash dieback. As part of Network Rail's aim to achieve biodiversity net gain on its property by 2035, this information would enable railway staff to take necessary action to better manage lineside vegetation.

Cost savings of up to £30M were identified on Network Rail's Great Western project through use of machine learning and wider construction industry benefits are set to be unlocked by a new investment from a Google venture business. Network Rail leveraged the savings by using construction risk forecasting platform nPlan, which has just been boosted by a £13.5M investment from GV (formerly Google Ventures). According to nPlan, wider use of machine learning that this investment will support could save the global construction sector almost £730bn each year through spotting delays and recommending improvements with an accuracy and scale previously not possible. The firm has said that such data-led insights would effectively reduce the volatility of and increase investor confidence in construction projects. By using some of the most powerful machine learning capabilities in the world to analyse what worked and what didn't in past projects, we can help our customers work out what's going to derail their own initiatives, and stop problems happening before they even appear." Speaking about the due diligence undertaken by GV ahead of the investment, GV general partner Tom Hulme said that he was "blown away" by the benefits organisations such as Network Rail saw in applying modern machine learning techniques to such a difficult analogue problem".

The company's proprietary AI algorithms have analysed nearly $1 trillion worth of global construction projects and deploys this learning to spot delays and recommend improvements with an accuracy and scale previously not possible. In doing so, nPlan's data-led insights effectively reduce the volatility of and increase investor confidence in construction projects. There continues to be a significant global appetite for major infrastructure projects, both in the immediate boost they provide to jobs and construction companies' revenues, and the ongoing economic impact they can have on regions and even whole countries. According to recent data from the Royal Institution of Chartered Surveyors, 'In the last quarter of 2020, before the latest lockdowns, construction industry workloads increased for the first time since 2019. Further to this, 2020 saw a seven month period of increased activity and expansion within the UK construction sector.

Tech Intelligence Bulletin (HG Insights) – Cognizant has been selected by Network Rail, Britain's principal rail infrastructure owner, to lead a new consortium tasked with delivering data-driven operations as part of a comprehensive Intelligent Infrastructure transformation program. Cognizant, in partnership with rail specialists Amey Consulting and Arup, has been awarded a five-year contract to design, build and operate digital capabilities based on artificial intelligence, internet of things, data analytics, and mobility technologies that will help improve Network Rail's asset management and overall performance. Cognizant will lead the consortium in developing and applying enhanced data collection and analytics to Network Rail's more than 12,000 connected assets, such as track circuits, signal power supplies, and switches. The new capabilities will equip Network Rail with real-time condition monitoring and data-driven insights, aided by artificial intelligence, to improve decision-making and asset management. Network Rail expects to lower costs and enhance safety by predicting and preventing maintenance issues, prioritizing work streams and minimizing the time rail workers spend on the tracks.

Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants. The artificial intelligence studies footage of plants near the line taken from trains and attempts to spot when leaves change colour, indicating that they might fall. It can also warn of fallen trees and when vegetation growth might soon obstruct the path of trains and lead to delays. The project is one of 24 high-tech schemes that have today been funded a total of £7.8 million ($9.9 million) by the UK government to improve the nation's railways. Trains delayed by'leaves on the line' might soon be a thing of the past as an AI system is trialled to predict build ups on the line and warn of encroaching plants (stock image) Slippery rails -- commonly referred to as'leaves on the line' -- result when build ups on the track led to trains not being able to grip the rails properly.

Similarly, an Indian travel start-up, RailYatri, has created an Estimated Arrival Time prediction algorithm using Machine Learning and statistical modelling techniques to predict the arrival time of trains. The system, trained on historical data, can provide customers with realistic estimated times for the arrival of their trains. According to Kapil Raizada, Cofounder of RailYatri, the method to predict the arrival time of trains in India had not changed over decades and was typically based on a distance by speed ratio for trains with some buffer time. RailYatri's Machine Learning algorithm takes into considerations other parameters ("ground realities") such as increasing traffic, rush, seasonality, etc, and adapts as it learns from subsequent inputs, making the predictions better with time. It uses clustering techniques to organise historical train runs into thousands of patterns where time series data attributes are similar.