A common question posed by transport leaders tasked with overseeing smart transport initiatives is how to generate greater returns from existing data reservoirs.
By taking a creative approach to advanced location-based analytics, there is a significant opportunity for transport planning strategists to drive more valuable insights from data to establish effective transport analytics programs. Here’s how.
1. Devising effective public transport strategies from travel card data
There is an incredible amount of valuable information currently collected via public transport travel cards that could be used to understand how and why people travel – think patronage data such as traveller numbers, the number of trips taken per week, the most common routes taken, peak travel periods and mode of transport.
But how can this data be effectively used to inform smart transport strategies? The Big Data sourced from travel cards often has limited value in its raw form and can leave analysts scratching their heads as to how best to take advantage of it
Location-based analytics technology can bring order to this data, by literally mapping undetected trends to present valuable, new insights. By presenting travel patronage data in the powerful visual format of a map, you gain a new perspective on information and clearly identify where there is scope in the network to initiate positive change.
For example, this approach could help us predict the value of new routes and infrastructure, or explore options like prioritising or incentivising certain mode choices on particular routes at different times of day to reduce congestion and improve service delivery and reliability.
2. Forecasting better transport network solutions from road data
The predictive capabilities of location-based analytics are equally as effective at the micro level: looking at a specific street, intersection or interchange. For example, by analysing information after an unplanned road or tunnel closure – such as which roads were closed, what alternate routes were taken, and what the impact was on public transport – analysts can determine how to manage future events more optimally.
Plans can be put in place to proactively change traffic signals, maintain roads, alert media, and relay data to operations teams if a similar incident occurs.
3. Integrating inter-organisational data for smarter maintenance scheduling programs
Beyond providing insights into commuter behaviour and traffic network flow, location-based analytics is enabling government and non-government agencies to share data and collaborate on projects, particularly in terms aligning road maintenance schedules.
New South Wales’ Land Planning Infrastructure (LPI) is one example of an agency that has seen major benefits from using location analytics to coordinate its transport infrastructure maintenance programs – boosting safety and efficiencies, and slashing roadwork disruptions and infrastructure costs.
The Smarter Scheduling NSW (SSN) system displays planned infrastructure across the state up to a decade in advance, alerting stakeholders – including government agencies, utilities and councils – to opportunities to coordinate in the schedule, and ensure maintenance programs are facilitated with maximum efficiency.
SSN reduces the need for multiple digs, construction and repairs – standing as a benchmark for streamlined infrastructure coordination capability. Stakeholders do not have to actively monitor the system – they simply subscribe to projects and are notified when there are opportunities to perform their own works or coordinate with others.
Opportunities are displayed on a user-friendly, interactive map interface that is viewable and searchable by all authorised users.
While there are statutory obligations on utility providers to consult road authorities about any planned road openings, there are no requirements to communicate these plans to other utility providers – and it’s not uncommon for the same stretch of road to be dug up a number of times by different utility providers. Location-based analytics encourages a more collaborative approach to infrastructure works. The result?
Reduced waste in works arising from duplicated infrastructure activity and minimised disruption to the public and business sectors.
The next steps for transport analytics technologies
The good news for Australia’s transport agencies is the technology and the data required to move forward with this approach already exists. In many cases, the technology required to enable location-based analytics – known as Geographic Information System (GIS) GIS technology – is already a foundation platform within most government transport departments.
The real opportunity lies in expanding the use of the technology to drive greater value from new and existing data sources to: forecast future challenges and solutions impacting the road network; inform the development of intuitive and fit-for-purpose services; and streamline costs associated with network maintenance.