Public Transport Planning and the Technological Revolution

ETHOS Issue 19, Jul 2018

Public transportation has not undergone a technological revolution for more than a hundred years. The main modes by which we move within cities today have been around since the end of the 19th century. The first electric subway system started in London in 1890. The first motorised public bus service, with a capacity for six passengers, was launched in Siegerland, Germany in 1895. Taxis with distance meters were introduced in Germany in 1897. Over the years, these modes have proliferated and improved substantially—becoming more fuel efficient, more comfortable and less pollutive. However, the underlying technology—and importantly, the ways each of these modes are provided to and used by commuters—has remained largely unchanged. Consequently, the fundamental paradigms in transport planning have also remained largely intact.

The public transportation system is on the cusp of another technological revolution, enabled by the confluence of various digital technologies. Mobile connectivity now allows real-time information flow between commuters and transport providers in both directions. Smart algorithms can process this information rapidly and generate schedules and routes of bus and train services dynamically to provide better and more targeted services, at less cost. Artificial intelligence reduces the need for human intervention, allowing public transport services to be provided more flexibly and efficiently. This article discusses some of the ways by which these technologies can free up traditional constraints and open up new possibilities in transport planning, along with some of the potential challenges that may arise.

Freeing the Routes and Schedule

Today, most public trains and buses run on fixed routes, schedules and capacities.Transport planners, typically through surveys, try to determine where and when commuters generally travel to or from. They then design routes and schedules so their limited transport resources can meet most of these needs. Once implemented, these routes and schedules are relatively rigid. Commuters will need to plan their day and journeys around them. However, in so doing, the rigidity of a public transport system permeates through to the lives of commuters. The city marches to the beat of its bus and train schedules. Wealthier cities overcome this inflexibility by providing numerous routes that operate at very high frequency throughout the day. However, this is also not ideal as it can result in significant wastage of public transport resources.

With mobile technology and smart algorithms, on-demand public transportation is fast becoming a reality. This year, we will be launching three on-demand bus services in Singapore. Through a smart phone, a commuter will indicate where he wants to be picked up and dropped off within a town area. The bus operator will use an algorithm to aggregate these demands and determine the number of buses to deploy and the routes they will take. The bus operator can thus avoid plying routes without commuters. Commuters will benefit from shorter waiting times and usually a more direct path to their destination. That said, some adjustment in behaviour is needed and we may need to ease commuters into this. Thus, we will be starting the trial during off-peak periods and will run some regular bus services along with the on-demand services.

With dynamic routes and schedules, we will also need to shift our mindset in transport planning. Instead of thinking about specific points of origin and destination, we will need to optimally determine geo-fenced areas, within which commuters usually travel. If the area is sized too small, there may not be enough commuters, especially during off-peak periods, to garner much efficiency. If the area is too large, commuters may have to endure long waiting times and circuitous routes. Transport planners may also need to rethink how they size their bus and train fleets. Rather than buy trains and buses that give them maximum carrying capacity, they may want a mix that gives them maximum flexibility. Fare structures may also need to be more dynamic: less a fixed function of the distance travelled and more reflective of demand and supply conditions.

Autonomous Vehicles: Overcoming Manpower and Land Constraints

The race to develop a “Level 5” fully autonomous vehicle (AV) is gathering pace. In November 2017, Waymo, a subsidiary of Alphabet, announced that it had begun testing vehicles without a safety driver (a “Level 4” AV). Car and component manufacturers like General Motors, Delphi, Nissan and Daimler are following close behind. Industry experts project that a fully autonomous vehicle will be ready within the next 10 years. AVs will transform how we move people and freight. Their impact on the car industry and on public transportation will be profound.

Watch this video for a glimpse into the future of Singapore’s public transport system
Source: Ministry of Transport

AVs are particularly appealing to Singapore because of our severe land and manpower constraints. The size of our bus and taxi fleet is limited by the number of drivers we can employ and the land take for roads, depots and car parks. AV technology removes the need for drivers. It can also increase the efficiency of road and parking spaces. Freight traffic can be shifted to night hours, thereby freeing up roads for human traffic in the day.

However, to maximise the benefits of AVs, we cannot simply drop them into existing regulations, operating models and infrastructure. As AV technology advances, there should be a parallel effort to develop an effective ecosystem within which it can be deployed. Recognising this, we started the Committee of Autonomous Vehicles (CARTS) in 2014 to bring together transport planners, city planners, traffic regulators and private companies to oversee and organise these efforts. We are developing a set of standards by which all new AV models must abide before they can be deployed on public roads. These standards will be scaled to cater to vehicles with different levels of sophistication—those which can meet the higher standards will be allowed to be used in more complex and crowded environments. We have also installed cameras and other sensors along the roads where AVs will be trialling. These, together with a mandatory “black box” in each vehicle, will be useful for attributing faults in the event of accidents.

By the start of 2019, we will also be piloting a limited autonomous bus service on the island of Sentosa. This pilot will help us gain insights into how commuters, pedestrians and other road users respond to AVs. It will also help us develop an operating model for an AV fleet. One important area we will be studying closely is how to incorporate a “man in the loop” to handle disruptions. From 2022, we will run a more extensive pilot in three new towns: Punggol, Tengah and the Jurong Innovation District. We envisaged two types of services: autonomous buses that run on scheduled routes and smaller on-demand shuttles. With feedback from AV developers, the town planners have incorporated some basic features, like AV charging spots and pick-up zones within the towns. This pilot will also try out new fare charging systems.

Autonomous Vehicle (AV) test at Sentosa
Source: Ministry of Transport

Narrowing the Gap between Private and Public Transportation

A key challenge of transport planning in large, metropolitan cities lies in managing the tradeoffs between private and public transportation. Private vehicles offer the most comfort and flexibility to commuters. Yet they result in huge negative externalities for everyone else: congestion, noise, pollution and heavy usage of land for roads and parking. Many cities attempt to capture these externalities through vehicular taxes and charges. Some cities (e.g., London, Singapore) have used road-usage pricing; others (e.g., Beijing, Shanghai, Singapore) have rationed vehicle ownership. While these measures make the price of vehicle ownership more reflective of their true costs, the net effect is that commuters who cannot or would not pay these prices have to settle for less desirable public transport modes.

A common bug-bear of public transport is its inconvenience. A public transport journey usually comprises more than one segment: for example, a walk to a bus stop, then a bus trip to the train station, then the train journey, and finally a walk to the destination. Transport planners have sought to reduce this inconvenience by placing bus stops and train stations nearer to homes and linking walkways to them. In Singapore, we plan to have 8 in 10 households to be within a 10-minute walk to a subway station by 2030. Bringing transport infrastructure nearer to homes is an expensive endeavour. Technology now enables us to close the first-last mile gap and reduce the tedium of public transport in a more cost-effective manner.

Firstly, there has been a surge in the number of privately provided public transport modes. Bicycle-sharing operations are a good example of this. As recently as three years ago, bicycle sharing required expensive docks. These docks severely limit the scale of bicycle-sharing services and make them commercially unviable in less densely populated areas, where such services are most needed. The emergence of dockless shared bicycles, which are tracked via GPS technology, changed this entirely. In Singapore, the number of dockless shared bicycles rose from just 10,000 to more than 100,000 within a year. Some companies are thinking about extending these to shared e-scooters.

Even as new public transport services emerge, there is a parallel effort to “stitch” these services together. Mobilityas-a-Service (MaaS) was first popularised in Finland. The idea was to allow a commuter to plan, execute and pay for different legs of a public transport journey with a single mobile app. This minimises hassle and waiting time, making a public transport experience very close to a private transport one.

From a transport planner’s perspective, the growth of privately provided public transport modes not only enhances the commuting experience, but also expands the choices of public transport commuters. This is a boon. However, it makes transport planning more complex. Travelling patterns are now less predictable. Commuter choices may change from day to day depending on, say, the weather. There is thus a need to build a degree of flexibility in bus and rail capacity to cater to this. To understand commuter behaviour, we will also need a wider data pool: not only data from public trains and buses, but also data in the private providers’ realm. To facilitate MaaS, there may be a need to create an open fare-charging platform. The fare structure may also need to more dynamically manage changing demand and supply from day to day.

New Technologies, New Challenges

While the benefits from these new technologies are manifold, there are two key challenges that transport policymakers need to address. The first is the extent and nature of government involvement in these new technologies. Calibrating this incorrectly may inadvertently stifle innovation or lead to perverse outcomes. Our experience with dockless bicycle sharing is a case in point. When the bicycle sharing operations first emerged, they provided a good means to plug the first-last mile gap without the need for public funding or infrastructure. There was no need to regulate them. However, as the operations grew in size, indiscriminate parking became pervasive. Bikes were strewn over parks and paths and public bicycle parking lots were overwhelmed by shared bikes. Left on its own, the situation was likely to worsen as each bicycle sharing operator sought to gain market share by growing its fleet aggressively. As a result, we will be licensing bicycle sharing operators. A key principle in this licensing regime is that an operator will only be allowed to grow its fleet size if it can control the indiscriminate parking problem from its fleet. In response, the operators are now competing to develop new technologies to address this parking problem. We have thus better aligned the interest of the operators with public interest.

Other technologies require different types of government intervention. AVs, for example, require financial support and regulatory sandboxes to facilitate experimentation. MaaS relies on the provision of real-time transport data. To be sure, government involvement will evolve along with the advancement and application of the technology. To ensure that this involvement is appropriate, regular consultation with the industry and with fellow regulators abroad is critical.

The second challenge is ensuring that the labour force is adequately equipped to apply these technologies. In most cities, public transport typically employs a large number of people—from drivers to technicians and service staff. The technological revolution will redefine many jobs, in some cases drastically. For example, when autonomous buses are ready, bus drivers will no longer be driving buses. They may instead be trained to handle exceptions, such as when the autonomous buses break down, or commuter incidents. Retraining the workers will be an enormous undertaking. In Singapore, we have instituted a tripartite framework, involving employees, employers and the government to ensure that the public transport workforce is adequately trained to handle current and future technologies.

Conclusion

We are at an intriguing juncture in the public transport landscape. While emerging technologies offer unprecedented opportunities, we will also need to evolve our policies, planning and regulatory frameworks to harness the most benefits for commuters. This process will necessarily involve some degree of trial and error. But without an appropriate ecosystem, such technologies will remain no more than an interesting curiosity.