At the 5th International Railway Summit in Kuala Lumpur last month, Malaysia’s rapid development of land public transportation (LPT) was in the spotlight as Prasarana Malaysia and the Land Public Transportation Commission (SPAD) shared the latest developments.
What catches the eye during the two-day conference, organised in association with Huawei Technologies and the International Union of Railways, are the government’s efforts via Prasarana, the nation’s largest public transport operator, to increase rail transport capacity.
Global Shift To Public Transport
Following global trends in favour of sustainable urban mobility, more commuters are choosing LPT over personal vehicles. Similarly, Malaysia has aimed to provide easy access to LPT (less than 400 metres’ distance) to 80% of its urban population by 2030 with 40% modal share.
That push is now driving a massive development of the LPT network in the Klang Valley. But it’s far from an easy undertaking.
The Klang Valley alone is forecast to have 9.4 million people by 2030, according to official estimates, a sizeable portion of some 80% of Malaysians who are expected to live in urban areas by that time.
Achieving Prasarana’s target would mean providing easy LPT access to 7.5 million Malaysians in the Klang Valley in 2030 and having 40% of commuters use public transportation services. As at May 2017, the modal share was at 25%.
However, simply increasing rail transport capacity by building new rail lines is only part of the picture. First-mile and last-mile connectivity services also need strengthening to ensure that people who newly shift to LPT are not turned off by potential inconveniences of getting to train stations.
Quick Ridership Surges Looming
Experience shows that while new rail systems are designed with specific expectations on how many riders they may eventually need to handle, getting to the maximum capacity is not always a smooth, gradual trend line.
Rather, the growth may come in large, quick bursts as newly completed rail connectivity draw new businesses, new housing projects and ultimately a population boom, which grows the catchment for public transport ridership.
For example, the mass rapid transit (MRT) at Kwasa Damansara is not serving a significant population catchment at present. However, the connectivity has spurred a 20-year development of a new township there with an expected population of 150,000.
Similarly, projects currently underway such as the financial district Tun Razak Exchange (TRX) and other transit-oriented developments will eventually cause spikes in LPT ridership when completed.
To illustrate the point, the light rail transit (LRT) ridership for the Kelana Jaya Line hit 271,250 for the first eight months of 2017, a 26% increase compared to the full-year figure in 2016. Similarly, the Ampang Line LRT ridership grew 14% when the January-August 2017 figure is compared to the full-year 2016 figure.
Carbon And Congestion Taxes
Additionally, there is also a growing trend of carbon and congestion taxes imposed around the world to discourage single-passenger occupied vehicles on the road. It is inevitable that Malaysia, particularly in the Klang Valley where road congestion is a serious issue, would head in this direction eventually.
One nearby example is Singapore, which declared 2018 to be its Year of Climate Action. Coupled with its coming implementation of carbon tax beginning 2019, the impact on transportation is clear: even more Singaporeans will be incentivised to choose land public transportation (LPT) over personal vehicles.
Of course, personal car ownership is a more entrenched culture in Malaysia and any tax-driven migration from personal vehicles to LPT, particularly trains, would not occur as fast as what is seen elsewhere globally.
However, the trend remains and it is not unthinkable for local authorities to promote public transport by adopting congestion taxes. For example, some areas in Selangor already provide free bus shuttles around town to encourage fewer cars on the road.
The coming migration from personal cars to LPT also needs to be considered as it would add ridership to the LPT network, impacting rail transport capacity.
In most cases, Increasing Capacity Takes Time
In comparison, traditional options to increase rail network capacity to match growing ridership are a considerably slower process. These include:
- Increasing train frequencies and number of rolling stocks via more precise train management using communications-based train control. For example, the Ampang LRT extension to double its capacity to 400,000 was made possible by Huawei’s communications-based train control system which upgraded the obsolete signalling system used previously to allow automated train operations.
- Replacing rolling stocks with bigger, longer units to ferry more riders in the same number of trips.
- Putting in more stations and lines to disperse riders and reduce congestions at hotspot areas.
- Adding parallel lines to ease the volume strain on busy existing rail lines.
- Using alternative LPT modes such as bus rapid transit (BRT) systems and electric trams to takeover some ridership volume from rail lines.
In a nutshell, these measures involve time-consuming procedures such as budgeting, calling for tenders and physical construction works.
That means that on their own, these measures could be ineffective since ridership surges may happen more quickly over a shorter time span.
As a result, riders may face congestion issues that mean longer waiting time as well as first-mile and last-mile connectivity issues due to capacity-volume mismatch.
For example, a passenger may be forced to wait longer if the shuttle bus to and from a train station is consistently full. Such inconveniences may deter commuters who are teetering on the edges from leaving their cars at home in favour of LPT.
What We Can Expect From Prasarana
In the Klang Valley’s case, the migration pace is unlikely to accelerate too much too soon compared to other global urban areas. However, Prasarana is already taking initiatives to improve connectivity such as placing oBike bicycle-sharing services at train stations.
Moving forward, what Malaysians can expect from Prasarana in respect of further complementing its various LPT modes, namely BRT and Rapid Bus services, could be gauged from what is happening in other markets.
One such possibility is e-hailing shuttle services. A good example is Via, a US start-up which will launch a dynamic on-demand ride-sharing next month in Arlington, Texas and West Sacramento, California.
While similar to Uber and Grab, in simple terms Via will be offering an algorithm-driven carpool-on-demand service.
Prasarana may also be considering tie-ups with taxi-hailing services to provide cheaper first-mile and last-mile transportation like what Mumbai did last year.
Other possible examples to emulate could be Bangalore’s move to provide taxi kiosks at its train stations for ease of booking and even possible subsidies for ride-sharing services like what the town of Innisfil, Canada is doing in partnership with Uber.
With Singapore already planning to roll out driverless buses in three towns by 2022, it is easy to imagine Prasarana looking into the same — if they haven’t already. To do this, big data analytics will be essential.
In theory, big data alongside artificial intelligence (AI) can manage driverless bus capacity and deployment by monitoring changes in ridership demand and identifying hotspots.
The AI can then estimate the number of passengers per hour going in a particular direction towards an identified train station from nearby locations. It would be able to respond by re-deploying LPT capacity such as driverless buses or driverless cars to the required areas to ensure there is no bottleneck that causes congestion.
Furthermore, with machine learning the system would continuously improve its efficiency by identifying congestion trends and pre-empting ridership surges with standby capacity.
Even prime minister Datuk Seri Najib Tun Razak has alluded to the possibility of driverless vehicles as a mode of public transportations for Malaysians in a recent blog post.
“Driverless vehicles, predictive maintenance for trains, electric cars- who knows what else the future will unveil? What is clear is that people and cities are on the cusp of changing rapidly and dramatically as advancements in mobility technology converge in the marketplace,” Najib wrote.
Big Data Analytics
Looking further forward, it is likely that big data analytics will play a crucial role in Prasarana’s strategic plans.
Among others, big data could help it spot transportation trends and potential congestion hotspots. It could then immediately react accordingly to ensure ample connectivity, including by increasing bus frequencies as needed, arranging for transit and carpool services or working with e-hailing service providers to ensure sufficient presence in specific areas.
Logically, these measures would be done by converging data from various parties, such as e-hailing players Uber and Grab and even from bicycle-sharing firms oBike and Mobike, on top of strategic collaboration to ensure all stakeholders are moving in lockstep.
Already corporates are recognising the value in the data trove such services are sitting on — on Nov 20, big data specialist Fusionex won a contract from an unnamed “leading ride-hailing company” to help the ride-hailing service tap its big data for better marketing intelligence and insights.
It would be crucial for various stakeholders, including ride-hailing service providers and other transportation-based startups, to work closely with Prasarana to complete the national LPT puzzle both in the short-term and in the long-term.