City Science was commissioned by England’s Economic Heartland to develop a flexible Working from Home propensity model to evaluate the capacity impacts of demand and mode share changes across the region expected to occur as a result of the COVID-19 pandemic. Figure 1: The EEH Region
Overview
New transport trends introduced by the Covid-19 pandemic offer the opportunity to explore alternatives to traditional investments aimed at mitigating peak-hour congestion. Within this study, City Science was tasked with identifying where such reprioritisation might be beneficial, as it is critical to be able to predict both changes in propensity for home working and how these translate into changes in transport infrastructure capacity.
England ‘s Economic Heartland (EEH) is a Sub-National Transport Body responsible for the region spanning Swindon to Cambridgeshire: advising government policy on transport infrastructure and services with the objective of realising the region’s economic potential all while supporting the journey to net zero.
Their ambitious transport strategy makes clear that enabling growth in a way that improves the environment requires a fundamental switch in the way the region’s transport system is planned and delivered. Through partnerships such as this, transport bodies, councils and local authorities can champion the use of digital technologies to make transport smarter and create net zero possibilities.
City Science Response
Based on scenarios of low, medium and high home working uptake, we were able to leverage existing data and model assets to:
- Evaluate the impact of “Working from Home” as a mode in its own right.
- Identify which trips could be most impacted by homeworking.
- Identify where and when capacity could be released on the transport network.
- Identify reduced requirements and demands for new high cost / high carbon infrastructure.
- Identify areas with strong potential for home working but limited digital infrastructure.
Our approach was broken down into the following 3 stages:
Stage 1: Consolidating Data for EEH Region. This first stage of the project was important in ensuring the quality of the results produced by propensity models produced in later stages, and that any data-derived analysis would be as accurate as possible. As such, we took steps to ensure this quality included the cleansing, cataloguing, and validation of all data sources. This included:
- Consolidating geo-demographic/employment data to develop zonal statistics
- Conducting link analysis to join observation data to MSOA-level geographies
- Integrating mapping of digital assets and coverage (e.g. fibre-optics)
Stage 2: Developing a Propensity Model. Through demographically-linked continuous survey data captured through the COVID-19 pandemic, our team was able to combine infrastructure-level predictors – such as broadband coverage – to train, test, and optimise the WfH Propensity Model. The data was linked geospatially to a comprehensive set of predictor variables including extensive demographic and persona-type predictors. All predictors were analysed using a using a mixed modelling technique with potential fixed predictors. The model was then applied geospatially to forecast WfH impacts, and behaviour change propensity at a highly localised level.
Figure 2: Model Predictors of Working from Home Propensity
Stage 3: Application & Analysis. Utilising the WfH Propensity Model developed in the previous stage, we then implemented the outputs in possible future WfH demand scenarios. Outlined below are the specific steps taken to conduct analysis of the findings.
- Develop an application approach to enable flexible mode-share impact modelling
- Create possible scenarios for future home working levels
- Apply specific scenarios through the propensity model and observe subsequent network capacity impacts
- Analyse and map scenario outputs through in-house software to produce clear visualisations
Figure 3: EEH WFH Propensity Model, Example Capacity Release Predictions
Outcome
Having developed an England-wide model that can predict the propensity for working from home, based on a number of factors like occupation income, household size and digital connectivity, we were able to enhance and apply the model to the EEH area and use it to produce valuable outputs that can feed into the EEH corridor studies. Our work was used to support and inform investment strategies and travel demand management activities to encourage positive behaviour change across the region.
By better understanding the effects of the shift in transport patterns due to greater WfH demand, EEH can more appropriately target reductions in high cost, high carbon infrastructure and identify key areas which would benefit from greater expenditure in digital infrastructure.