Using locational data as part of the intelligence planning process enables better decision making across the project lifecycle, says Craig Hayes.
The transport infrastructure world is an increasingly complicated one. Modern transport networks are more interconnected than ever, digital transformation wave is continuing to gather pace, and the public is demanding innovation at a rapid rate. Consequently, transport planners, operators and organisations responsible for driving regional economic development are facing a huge amount of complexity.
The government is also under pressure to improve services in a cost-efficient, sustainable manner and it’s clear a new level of intelligence is needed to plan and execute transport infrastructure projects.
Across the public-sector organisations, transport operators and both central and local government also have considered wider political and socio-economic factors, which further adds to the complexity. This must be done with a limited budget, whilst facing competing demands for the funds received or generated.
Public sector organisations that plan and implement transport initiatives cannot afford to adopt a siloed mindset. They must be prepared to collaborate internally, with partner organisations, to analyse where investments will have the greatest impact across all modes of transport that serve the population and to drive economic growth.
There’s a focus on laying down the foundations for future economic development by giving people access to better jobs. Here, intelligent planning comes into play.
Getting from A to B
The intricate nature of modern transport systems means data analytics is now a central aspect of any planning process. The Greater London Authority, Transport for the West Midlands, West Yorkshire Combined Authority and Transport for London (TfL) are using geographic information systems (GIS) to gain insights into the wider impact of projects geographically.
This spatial approach to data analysis means prioritising the right investments and identifying clashes between projects, gaps in resources or collaboration opportunities, can quickly be visualised, increasing the likelihood of projects delivering the greatest returns on the investments made.
TfL’s city planning tool plans to support the mayor’s transport strategy regarding easing congestion; reducing pollution and improving both safety and health by encouraging citizens to use public transport, walk or cycle. TFL’s business plan generates significant amounts of data, all at varying levels of scale and complexity, creating challenges when it comes to accessing, visualising and making sense of data to inform decisions.
So, the city planning tool allows planners to analyse data regarding pollution, journey times and traffic patterns, providing a single source of information. Users can vary the significance of different types of data (e.g. bus travel patterns over cycling patterns) to highlight potential benefits of a given project.
And this practice isn’t limited to London. Various organisations in the north of England, including the West Yorkshire Combined Authority and Transport for West Midlands, are looking at how they can use data to improve transportation infrastructure to develop the economy. They’re taking a regional approach to identify the biggest problems and where new schemes will show the biggest returns on investment.
With so many directions available for transportation infrastructure projects in the UK, it’s hard for organisations to identify where they’ll see the biggest financial returns. Projects could focus on increasing rail and bus efficiencies and service provision, reducing congestion or improving access to jobs and better lifestyles. They could be designed to underpin economic development through regeneration or attract inward investment from businesses to towns or cities where a reliable, cost-effective transport system is key.
The spatial approach using GIS is important in optimising ROI. Up-to-date data for stakeholders, is saving organisations time and money, and brings an end to the current situation where data is disparate and stored in multiple places.
Using locational data as part of the intelligence planning process enables better decision-making across the entire project lifecycle. Hundreds of different data streams from multiple sources, can be layered on top of each other, providing meaningful information that may otherwise go unnoticed and a level of insight that simply wouldn’t otherwise be available.
A strategy that optimises intelligent planning in this manner will directly impact its bottom line, but also the wider UK economy and, ultimately, give people better access to jobs and improved lifestyles.
Craig Hayes is head of transport at GIS software suppliers Esri UK.