Disaggregated Data Can Help Provide More Inclusive Transport Services

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Digitalisation and big data technologies (such as online ticketing and smart cards) have enabled the tracing of human mobility on a large scale. In Indonesia, PT Kereta Api Indonesia (KAI), the national train company, has been serving the Indonesian public since 1945, and has played a crucial role in both improving social welfare as well as growing the economy in regions it operates (Java and Sumatra). Realizing its important role, PT KAI has been at the forefront in trying to better understand passenger travel behaviour to improve services, especially amongst vulnerable cohorts such as the elderly, women and people with disabilities. This fulfills PT KAI’s social imperative as a state-owned enterprise to provide quality services, but importantly is also proving to be good business. In collaboration with Pulse Lab Jakarta and the Ministry of National Development Planning (Bappenas), PT KAI recently undertook data analytics research with this vision in mind. Here we reflect on some of the policy and development opportunities emerging from findings.

A Passenger-Centric Approach

An understanding of each passenger type, behaviour and needs can improve operations and service delivery. However, transport agencies nowadays are experiencing increasing and more varied ridership, leading to a change in demands and expectations. This requires a shift in how we assess travel patterns and passenger behaviour, from a conventional approach (e.g. aggregating ridership along a route) to a more passenger-centric approach (e.g disaggregating data to address the needs of females travelling alone at night).

Between January to September 2019, PT KAI transported more than 400 million passengers over 29 million kilometers, transiting through more than 400 railway stations across Java and Sumatra. Since passengers are required to provide identification details for reservations, the company holds vast amounts of data that can be anonymised and disaggregated, resulting in actionable insights. As a baseline research, we started with the data available for 2019 that included passengers’ anonymised demographic information, transaction and origin-destination details.

Exchanging Learnings Along the Way

In the first phase of the research, our objective was mainly to scope and explore the feasibility of utilising, processing and analysing the passenger datasets. Based on the results achieved, a baseline analysis was then conducted to generate insights on the travel behaviour of the passengers, using reconstructed itineraries with anonymised information at an individual level.

As a collaborative research, all the activities were co-created, with regular meetings convened to share learnings and progress updates amongst our team and the project teams at PT KAI and Bappenas. Given the COVID-19 situation in Indonesia, this collaborative data analytics initiative became mostly a remote research endeavour, guided by the following common principles:

  • Iterative Process: the activities were carried out in a way to ensure that each methodology and code used was properly communicated in a transparent manner;
  • Collaborative Action: all the parties involved had equal opportunity to provide inputs and contribute at every stage of the project; and
  • Capacity Building: knowledge on big data processing, as well as methods and tools were introduced and shared across the project teams.
Disaggregated Data Can Help Provide More Inclusive Transport Services 1

Inclusive Transport Sector Planning

The research highlighted that disaggregated insights on population movements across cities can help inform overall transport sector planning. For example, the disaggregated analysis by gender indicated that about half of all passengers in 2019 were female, with a significant number of them travelling in the evening hours (which calls to mind our prior After Dark research that examined the mobility patterns and perceptions of safety among women who regularly travel during these hours). By age, the data also shows that eldely passengers (who are more than 60 years old) completed more than 1.5 million trips in that year. These cohorts are known to face certain challenges in transit, and require assistance and supportive infrastructure to improve their comfort and safety.

Disaggregated Data Can Help Provide More Inclusive Transport Services 2
An elderly passenger is offered a complimentary snack onboard (Photo courtesy of PT KAI)

The process of obtaining and analyzing the data also highlights benefits in rethinking PT KAI’s underlying data infrastructure as a means to better integrate and leverage the varied data that is collected through its different systems. This serves to improve the company’s ability to combine their own data with external datasets for better business development, as well as to help achieve their inclusive development objectives as a state-owned enterprise. More importantly, the experiences in developing this architecture can also provide Bappenas and other stakeholders with learnings that can be harnessed for planning and improving data accessibility and governance of the rest of the data ecosystem in Indonesia’s transport sector.

The improved understanding of the value and utility of PT KAI’s data has also helped Bappenas to see where it can be applied to analyse the effectiveness of a number of national policies on subsidised transportation for the elderly and other cohorts. It should also be noted that railway passenger data as reported annually by the Ministry of Transportation has never been disaggregated by sex, and so this research has also contributed in encouraging more state owned companies to follow suit.

Contributing to the SDGs

From a national perspective, catering to vulnerable cohorts such as women, the elderly, and people with disabilities in service delivery contributes to the Sustainable Development Goals. In particular inclusive and safe transport services can enable vulnerable cohorts to live more independent lives, which in turn can improve their economic opportunities. By attracting more people to public and mass transport options this reduces the burden on the transport infrastructure, which can lead to a host of benefits from decreases in productivity losses due to reduced congestion, to better environmental outcomes from reduced pollution.

As a baseline research, this project also served as a capacity building activity that enables train authorities and government decision makers to better understand and make use of their data before delving into more complex analyses. By proactively identifying opportunities to improve service delivery especially for vulnerable cohorts, PT KAI is well placed to be an exemplar for not just state-owned enterprises, but also for the private sector engaged in transport services. These opportunities will undoubtedly serve to ease some of the friction for vulnerable cohorts to access economic opportunities and create a vibrant and inclusive economy.

PLJ Project Team: Muhammad Rizal Khaefi (Data Scientist), Desi Vicianna (Government Partnership Coordinator), Angga Gumilar (Programme Assistant), Sriganesh Lokanathan (Data Innovation & Policy Lead), Alfian Maulana Latief (Visual Information Designer) and Dwayne Carruthers (Communication Manager).

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