4 MIN READ

With Australia’s population set to soar to 30 million by 2031, the pressure on authorities and commercial enterprises to deliver intelligent transport systems that are reliable, efficient, safe, sustainable and accessible has never been greater.

Beyond the rapid growth of our major cities, the challenges that lie ahead for the country’s transport networks are complex and have far-reaching ramifications. For example, traffic congestion – and its impact on local business – is forecast to cost $20b a year by 2020. Road freight alone is projected to increase by 80 per cent by 2030. And CO2 emissions from road transport will account for 14 per cent of Australia’s total greenhouse emissions.

The answer to addressing these challenges – and mitigating the associated risks – doesn’t lie in traditional responses.

Rather, if you’re the CEO or COO of one of Australia’s transport agencies, the key to improving the performance of existing infrastructure networks and optimising new intelligent transport strategies lies buried deep within your department’s Big Data reservoirs.

Looking beyond real-time and into the future

Utilising data sources to provide insights into a city’s transport networks isn’t new; in fact most traffic departments have been using the approach for decades. But the truth is we have barely scratched the surface in exploiting Big Data to improve transport networks and planning. While day-to-day, operational tasks – such as optimising traffic signal timing and responding to accidents – are incredibly important to the smooth flow of traffic, even greater value lies in being able to predict what the networks of the future could look like.

New developments in advanced location-based analytics are now providing us with the ability to move beyond the historical views of traffic patterns and real-time situational awareness that is our current focus.

By combining advanced analytics with the wide range of Big Data sources that we can now consume, we are better positioned to understand future commuter behaviour and plan networks that match and direct movement more efficiently than ever before.

Unlocking consumer-driven insights to inform service delivery

Central to this approach is supplementing authoritative, government-sourced data with “non-authoritative”, external data to create a more comprehensive and holistic view of our transport networks.

Australian transport agencies now have the ability to mine data collected through smartphones, as well as satellites and GPS devices, and combine this with information from services and apps we use on a daily basis, such as Uber and Twitter. And with global telecommunications networks forecast to connect with more than 50 billion sensors by the year 2020, the amount of accessible Big Data will increase exponentially.

Advanced location-based analytics platforms – such as Geographic Information System (GIS) technology  – can rapidly tame these vast volumes of data, translating raw information into meaningful insights.

For example, planners looking to relieve road congestion by encouraging alternative transport could use location-based analytics to leverage the information collected by cycling apps such as Strava. Strava reveals routes taken by bike riders as well as commentary about why these are used: valuable information that could support decision-making when designing new bike paths for cyclists.

With GIS technology, data from these disparate sources can be integrated and analysed with ease to enable us to move beyond simply identifying the ‘where’ and ‘when’ of transport, to start answering crucial questions about ‘how’ and ‘why’ people are travelling. Why are commuters selecting one mode of transport over another? How can promoting the use of a particular route over another during peak-hour ease congestion?

Armed with this knowledge, we can understand how the different components of our transport network – encompassing infrastructure, vehicles and mobility services – could be planned based on the needs of commuters, cost constraints and the capacity of the network as a whole.

Singapore’s Land Transport Authority (LTA) is just one example of an agency that has successfully leveraged this approach. Their location-based analytics system – called Planning for Land Transport Network (PLANET) – enables planners to understand the geographic aspects of commuter trends and patterns so they can fine-tune their transport policies (you can read more about the LTA’s approach in this case study).

To find out more about how big data analytics can inform smart transport planning, call 1800 870 750 or send us an email.

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