Economic progress can seem like a two-edged sword – we relish the opportunities for career and lifestyle choices offered by our expanding cities, but urban transportation woes can sometimes make us wonder if it’s all worth it.
The Association of Southeast Asian Nations (ASEAN) recently celebrated its 50th anniversary this year – and there is much to be proud of. In simple economic terms, ASEAN’s GDP growth over that period from $37.6 billion to $2.6 trillion has set the region firmly on the path to advanced standards of living. But this progress has undeniably come with a price.
Economic growth across Southeast Asia is driven by our cities, and the massive surge in urban population has propelled Jakarta, Bangkok and Manila into the ranks of global mega-cities – with all the transport problems that entails. Kuala Lumpur, Ho Chi Minh City and Yangon are not far behind, and even Singapore is seeing ever greater automobile ownership and use, despite the country’s clear commitment to a comprehensive public transport network.
The multiple ill effects of traffic congestion on the individual as well as on the national economy are well known, and sadly the cities of our region are major offenders.
Apart from Singapore and Malaysia, the 2016-2017 Global Competitiveness Report of the World Economic Forum (WEF) found that every nation of Southeast Asia scored badly when it came to efficient and developed transport infrastructure.
The reasons for this depressing situation are historic and complex, ranging from the political to the economic. Whatever the underlying causes, the key factor is the absence of government-level planning for comprehensive urban road and rail transit systems, covering both private and public use.
Now, the authorities in these countries have recognized that their transportation nightmares are a serious threat to the functioning of their cities, and over the past few years have been following Singapore’s lead in developing mass transit projects. However, private automobile traffic is not going away any time soon, and one challenge faced by city administrations is how to manage it and how to integrate it into a comprehensive urban transit system.
The poster boy for urban traffic management is definitely Singapore, which in 1998 pioneered the world’s first Electronic Road Pricing (ERP) system. This congestion pricing system automatically deducts the toll via a pre-paid in-vehicle unit, electronically triggered when the vehicle passes under a purpose-built gantry.
Singapore is now field-testing another world first – an ERP system based on satellite navigation technology instead of physical gantries. The system will have island-wide coverage, and will charge for actual distance travelled. It can also facilitate coupon-less street parking and will provide all road users with real-time traffic information through an intelligent onboard unit.
The benefits to road users and authorities alike from such an advanced system are clearly enormous, and the key to making it possible is our growing ability to gather and analyse massive quantities of data.
Transportation and traffic management make a perfect subject for Big Data analysis. The real promise of this burgeoning technology in the transportation sector is its potential to enable a truly comprehensive city-wide transit system, embracing and co-ordinating public and private, road and rail.
Elements of such applications are already underway. In addition to improving the flow and regulation of private automobile traffic, transport authorities around the world are using data analysis to manage and improve mass public transit systems, both bus and rail. Applications include everything from accurate ridership forecasts, to route planning and frequency, to cost-saving maintenance schedules.
The intimate understanding of customer behaviour and journey plans furnished by big data allows authorities to plan for additional services on the routes, such as conveniently located retail stores. It also lets the transit authority tailor communications with each individual rider to notify them of any service changes, upcoming events or weather issues that may impact service, or even provide targeted advertising.
The overall improvement in the commuter’s journey experience delivered by the insightful use of Big Data will lead to enhanced customer satisfaction and help increase train ridership, while providing authorities with new revenue sources.
A significant element of the costs of any mass transit system is maintenance. By leveraging Big Data, authorities can predict optimal maintenance requirements of the equipment – whether trains and their tracks or bus assets.
Data from the sensors installed on the equipment can be analyzed faster and at a more granular level. This helps predict upcoming faults at the individual component level such as brakes, a stretch of rails etc. Authorities can then schedule maintenance of the equipment at precisely the right time, optimising cost and minimising disruption.
One public rail transport provider in the US has successfully deployed Big Data to schedule its equipment maintenance with astonishing results – mean time to failure of the equipment has been reduced by almost 80-90% and equipment life increased by 200%. This has further improved customer safety and satisfaction, enhanced equipment utilization and reduced operating costs.
In another example, the Metro Transit of St Louis (MTL) had always taken maintenance seriously but lacking detailed data on how bus components were actually performing, maintained vehicles retroactively. It replaced parts after they failed, or simply bought new buses.
This approach assured passenger safety and service reliability, but the team suspected that it often replaced parts or discarded entire buses even when replacement wasn’t necessary.
MTL turned to Big Data analysis to better predict when a component on a particular bus will fail, allowing them to proactively service the bus prior to any component failures.
The results have been spectacular – the average time between bus failures has improved by a factor of five. MTL was able to run the buses for much longer, thereby increasing the return on investment in their fleet. Previously, buses were being retired after 35,000 miles per year at 12 years, but now MTL is able to continue using the buses up to 60,000 to 70,000 miles per year at 15 years. This is a 2x improvement on mileage and 30% increase in bus lifespan.
These improvements in vehicle maintenance have saved St Louis area taxpayers more than US$2.5 million per year.
If it is judiciously deployed, Big Data analysis has the potential to transform the transport systems of Southeast Asia’s megacities – delivering a positive impact on the environment, the economy and the quality of citizens’ lives.
Imagine a time when our cities can boast efficient, cost-effective mass transit buses and trains combined with well-managed highways and streets for private traffic! Given a combination of political will, financial resources and Big Data analysis, this scenario could be nearer than we think.