The Anonymous Widower

NextGen Data-Driven Timetable Performance Optimisation Tool

This project was one of the winners in the First Of A Kind 2022 competition run by Innovate UK.

In this document, this is said about the project.

Project No: 10037862

Project title: NextGen Data-Driven Timetable Performance Optimisation Tool
Lead organisation: ARTONEZERO LIMITED
Project grant: ££157,826

Public description:

During the pandemic, the on-time reliability of services significantly increased due to the reduction
in the number of services and passengers.

However, as passengers have returned to the railway performance has once again deteriorated.

This has an even greater impact on the industry post-pandemic as passengers’ expectations for services that are reliable and run on-time is even higher. Increased delays and passenger dissatisfaction therefore leads to an even greater decreased revenue from ticket sales.

Poor performance is in large part due to a poorly planned timetable that is often operationally unachievable or cannot handle minor perturbations. This is due to the timetable usually being planned with simulations and the method does not in how trains performing in reality at junction or stations.

Through years of working closely with performance, planning and operational teams, we’ve identified that by using granular train movement data and machine learning techniques, the actual performance of the existing timetable could be accurately calculated. This would enable planners with accurate information to make faster and better planning decisions that are based on real-world evidence.

Our Timetable Analysis tool will deliver automatically updated insights and recommendations to planners that is highly aligned to the planning process. Utilising both on-track (track circuit) and onfleet (GPS and OTMR) data, the tool will provide an integrated view to both Network Rail and TOC teams.

Fundamentally this tool will result in a step change in the speed and quality of timetable planning, moving away from the use of limited simulations and anecdotal experience to a fully evidenced-based approach.

November 18, 2022 - Posted by AnonW | Computing, Transport/Travel | First Of A Kind 2022, Innovate UK