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The Government has enacted into law the requirement to meet net zero carbon emissions by 2050 [1]. Network Rail (NR) has risen to the challenge and embedded this into its own Environmental Sustainability Strategy [2]. NR has also set markers along the way on the journey to net zero via ambitious Science Based Targets (SBTs) for both itself and its supply chain [3]. Having these SBTs embedded in the supply chain is vital as NR’s scope 3 emissions account for over 97% of its Green House Gas (GHG) emissions [4]. This paper addresses potential solutions to the climate change and carbon reduction problem through a UK rail focused lens. It also outlines some of the challenges in reducing carbon in specific railway geotechnical solutions, namely earthworks. The focus of the research is on accurate measurement of current carbon accounts in Business As Usual (BAU) scenarios for railway embankments and soil and rock cuttings. The current situation will be accurately recorded via analysis of a variety of case studies to identify carbon hot spots within these specific earthworks, which are hypothesised to be in the cements and steel used. A suite of alternative lower carbon products and/or processes will be discussed and analysed, which, when coupled with a taxonomy of negative carbon technology solutions, such as sequestration via afforestation and enhanced weathering, could enable NR to move closer towards its goal of achieving net zero carbon. Keywords: Carbon Account; Railway; Geotechnical Solutions

A national trip end model to forecast demand for new local railway stations in Great Britain has been developed and implemented as an automated hosted tool on the Data and Analytics Facility for National Infrastructure (DAFNI). This paper presentation describes the novel characteristics of the underlying model, explains how it has been realised on DAFNI, and incorporates a live demonstration of the tool’s web interface.The underlying trip end model was calibrated on all existing local stations in mainland GB, and is unique in its scale, adoption of a high spatial resolution zoning system (trip generation is assessed for every postcode), and the incorporation of a station choice model. The latter allows probability-based station catchments to be defined, accounting for competition between stations and enabling the assessment of abstraction effects on existing stations. Testing of the model found that it produced more accurate demand forecasts than methods used during the scheme appraisal process for several recently opened stations and a new line. The model has already been used to produce demand forecasts for the Welsh Government and is capable of forecasting passenger numbers for new stations located anywhere in GB.Implementation of the model on DAFNI makes this powerful tool directly available to transport planning practitioners and other stakeholders for the first time. The data storage and transformation capabilities of DAFNI ensure that the model inputs are always available and up-to-date, freeing practitioners from onerous collection and processing tasks. The tool has the potential to transform the assessment of new station schemes, enabling the rapid review of options for individual stations or new lines. It can replace costly models developed on an ad hoc basis when a local need arises and can be a useful comparator tool to help assess the reliability of forecasts produced by locally developed models.

The aggregate models commonly used in the UK to estimate demand for new local rail stations require the station catchment to be defined first, so that inputs into the model, such as the population from which demand will be generated, can be specified. The methods typically used to define the catchment implicitly assume that station choice is a deterministic process, and that stations exist in isolation from each other. However, studies show that pre-defined catchments account for only 50-60 percent of observed trips, choice of station is not homogeneous within zones, catchments overlap, and catchments vary by access mode and station type. This paper describes early work to implement an alternative probability-based approach, through the development of a station choice prediction model. To derive realistic station access journey explanatory variables, a routable multi-modal network, incorporating data from OpenStreetMap, the Traveline National Data Set and National Rail timetable, was built using OpenTripPlanner and queried using an API wrapper developed in R. Results from a series of multinomial logit models are presented and a method for generating probabilistic catchments using estimated parameter values is described. An example probabilistic catchment is found to provide a realistic representation of the observed catchment, and to perform better than deterministic catchments.