Robust travel time predictions are of prime importance in managing any transportation infrastructure, and particularly in rail networks where they have major impacts both on traffic regulation and passenger satisfaction. We aim at predicting the travel time of trains on rail sections at the scale of an entire rail network in real-time, by estimating trains' delays relative to a theoretical circulation plan. Predicting the evolution of a given train's delay is a uniquely hard problem, distinct from mainstream road traffic forecasting problems, since it involves several hard-to-model phenomena: train spacing, station congestion and heterogeneous rolling stock among others. We first offer empirical evidence of the previously unexplored phenomenon of delay propagation at the scale of a railway network, leading to delays being amplified by interactions between trains and the network's physical limitations. We then contribute a novel technique using the transformer architecture and pre-trained embeddings to make real-time massively parallel predictions for train delays at the scale of the whole rail network (over 3000 trains at peak hours, making predictions at an average horizon of 70 minutes). Our approach yields very positive results on real-world data when compared to currently-used and experimental prediction techniques.

Or maybe you're wondering why we should even care about trains and how they operate--what is this, the 1800s?--so let's back up a bit. If you think America is solely dependent on trucks to move freight, you might be suffering from tunnel vision: Trains account for a third of the ton-miles--that is, a ton of weight carried a mile--that freight travels in the U.S. every year. That's almost as much as is carried by trucks. The U.S. has the most extensive rail network of any country on earth by miles of track--yes, even bigger than China's--and it's currently facing some of the same snarls and congestion as seemingly every other part of the country's supply chains, on account of unprecedented activity at ports and record demand at some rail hubs. Trains might seem like a mature technology with little room for improvement or expansion, since adding new rail lines is prohibitively expensive, as battles over the cost of the expansion of Amtrak service have shown.

Over the past 20 years, the number of passengers traveling on British train networks has almost doubled to 1.7 billion annually. With numbers like that it's clear how much people rely on rail service in Great Britain, and how many disgruntled patrons there would be when delays occur. A recent study used real British Railway data and an artificial intelligence model to improve the ability to predict delays in railway networks. "We wanted to explore this problem using our experience with graph neural networks," said Huy Tran, an aerospace engineering faculty member at the University of Illinois Urbana-Champaign. "These are a specific class of artificial intelligence models that focus on data modeled as a graph, where a set of nodes are connected by edges."

Our Innovation Analysts recently looked into emerging technologies and up-and-coming startups working on solutions for the railway industry. As there is a large number of startups working on a wide variety of solutions, we decided to share our insights with you. This time, we are taking a look at 4 promising artificial intelligence startups. For our 4 top picks, we used a data-driven startup scouting approach to identify the most relevant solutions globally. The Global Startup Heat Map below highlights 4 interesting examples out of 21 relevant solutions.

One of the biggest advances in transportation has been self-driving technology, which has facilitated breakthroughs in not just self-driven cars, but even driverless trains, self-flying planes and self-navigating ships. Rio Tinto, an Australian mining corporation, has unveiled the first operational driverless train in Western Australia, even before China, which has its own similar automated train in the works. The train completed its first run of 100 kilometers (62 miles) in the Pilbara region of Western Australia, without anyone manning the train. "This successful pilot run puts us firmly on track to meet our goal of operating the world's first fully-autonomous heavy haul, long distance rail network, which will unlock significant safety and productivity benefits for the business," Rio Tinto Iron Ore chief executive Chris Salisbury stated in the press release issued Monday. "New roles are being created to manage our future operations and we are preparing our current workforce for new ways of working to ensure they remain part of our industry."

Back in 2014 an Accenture report predicted that investment in the Industrial Internet of Things would reach $500 billion by 2020. A combination of cheap sensors, powerful data processing and machine learning has enabled companies to make their industrial processes significantly smarter and more efficient. A good example of this in action comes in the rail industry, where a number of fascinating use cases have emerged in the past year. For instance, Finnish company Sharper Shape have been using drones to map utility networks. They use machine learning to identify trees that are at risk of falling onto power lines.

This'Digital meets Transport' networking session will open doors to larger brands that are looking to the digital community to implement agile innovation. The session will showcase companies developing disruptive data capture technologies, innovative data-driven applications, artificial intelligence algorithms, cyber-security solutions and open-source payment systems. The event will take place from 14:00-17:00 on Thursday 8th December at the Digital Engineering Test Centre in Queen Elizabeth Olympic Park, London. Up to 16 of the most innovative companies to apply to attend will be invited to meet with established industry brands, for an afternoon of facilitated networking. Nissan: The global automotive business and world-class manufacturer are looking for innovative solutions to gain improvements in cost, quality & delivery performance in a very lean, high volume manufacturing environment.

This paper describes how AI is used to plan, schedule, and optimize nightly engineering works for both the commuter and rapid transit lines in Hong Kong. The MTR Corporation Limited operates and manages all the rail lines in Hong Kong. Its “Engineering Works and Traffic Information Management System” (ETMS) is a mission critical system that manages all information related to engineering works and their related track possessions and engineering train movements. The AI Engine described in this paper is a component of this ETMS. In Hong Kong, the maintenance, inspection, repair, or installation works along the rail lines are done during the very short non-traffic hours (NTH) of roughly 4 to 5 hours each night. These engineering works can be along the running tracks, track-side, tunnel, freight yards, sub-depots, depot maintenance tracks, etc. The proper scheduling of necessary engineering works is crucial to maintaining a reliable and safe train service during normal hours. The AI Engine optimizes resource allocation to maximize the number of engineering works that can be performed, while ensuring all safety, environment, and operational rules and constraints are met. The work described is part of a project to redesign and replace the existing ETMS, deployed in 2004, with an updated technology platform and modern IT architecture, to provide a more robust and scalable system that potentially can be deployed to other cities around the world.