An example of how bus datasets can differ semantically and syntactically
Before we can publish data in an interoperable format, we need to first generate the right data and this starts with equipping the bus network with the right set of technology.
At a basic level, every public bus service needs to provide accurate vehicle location information, which requires them to equip their fleet of buses with a GPS tracker. Vehicle locations should then be linked to schedules through the GTFS data hierarchy of: Stops > Stop Times > Trips > Routes.
Additionally, this information needs to be in real-time and with updates on any disruptions and delays to the schedule. To enable bus passengers to make travel decisions that include buses, each service should also supplement their dataset with fare and ticket information.
To improve overall interoperability thereafter, there is a need to develop a solution that automates the data publishing for bus operators and to ensure that the data quality is suitable for route planning.
There is also a need to raise awareness about the right ways to publish data and how that can impact the efficiency of the bus network, which can be done through training sessions for bus operators.
Modern Bus Services
Now that we’ve laid down the building blocks of a scheduled bus dataset, let us consider a more complex public transit service that is not tied to using fixed schedules and fixed routes.
This modern service is called Demand-Responsive Transit (DRT). They have the capacity and affordability of a small public shuttle vehicle that services a geofenced localised area but with the convenience and rich features of an e-hailing app. In this way, the service responds to the demands of commuters and benefits them, while DRT transport operators can optimise their operational costs.
If we were to compare the datasets of a DRT service with a fixed schedule bus service, it would be like comparing the datasets of a taxi to a train. Datasets of DRT services are dynamic, generated in real-time, based on the demand in the region. The stops may also vary based on the type of DRT service.
Development of a standard for publishing data of on-demand services is still at an early stage. However, modern public transit services like DRT come with the advantage of being fully digitalised with accurate updates on trips that allows for reliable journey planning.
Although DRT datasets are far from standardised, implementing DRT at a large scale can revolutionise the way public transit functions and hence, how buses are perceived.
The future of public transit
It is an inescapable fact that buses today are inefficient and unreliable and the majority of public commuters shun such services and only use them as a last resort. The absence of reliable first-mile and last-mile solutions remains the biggest hurdle against increasing the utilisation of public transportation in Malaysia.
But this perception can change as new modes of transport are emerging and technology can transform the age-old bus services as we know it today.
But in order for such new services to work, bus service providers need to ensure that the data they produce is published in the correct format so that the intelligence gleaned can be further processed by automated routing engines. This way, services such as DRT and Bus Rapid Transit (BRT) can be introduced to modernise existing public transit services.