Issue 07: Techno Transit
AI’s role in Asia’s mobility futures
Dear Reader,
Transportation is Asia’s second largest source of emissions, representing 40% of global transport emissions, equivalent to that of Europe and North America combined. This is primarily linked to fossil-fuel dependence in the region, although countries have taken many steps to decarbonise the sector and electrify fleets.
As efforts are made to combat climate change and decarbonise transportation, concerns around equity and user-friendly mobility complicate policymaking in urban transport. To address some of these concerns, leaders from 21 Asian countries signed the Aichi 2030 Declaration for achieving universally accessible, safe, affordable, low-carbon, multi-modal transport. One of the strategies in the Declaration is the adoption of smart information and communication technologies like intelligent transport systems (ITS). In these systems, machine learning (ML) is used to optimise traffic flow and navigation, minimise accidents, enhance driver experience, and in autonomous driving. But safety and privacy concerns persist, and other challenges like lack of public benchmark datasets, explainability gaps, difficulties in identifying multi-modal transport, and infrastructure limitations pose adoption barriers.
In this issue, we explore how advanced technologies like AI are being deployed for mobility in Asia. What goals are being optimised for? Is the pursuit of efficiency in the sector leading to sustainable outcomes? How are access and connectivity incorporated in AI policies for mobility?
Curated Reads
AI use in the transport sector
As commuters move from one place to another, they generate a lot of data, which can feed into AI systems. This includes location data from mobile devices, data from traffic sensors, and frequency of travel from ride-sharing apps. Dynamic GPS data, in particular, supports transportation demand forecasting, route optimisation, and traffic and safety management. However, GPS data alone lacks important context and cannot directly identify location names or transport modes, making accurate transport mode detection challenging. Traditional methods to differentiate between cycling or car travel rely on predefined routes or traffic patterns, limiting adaptability to real-world conditions such as unexpected traffic jams.
This proposed approach eliminates fixed time intervals by extracting features directly from raw GPS data and converting them into images for training a deep learning model. Using GeoLife, a five-year GPS trajectory dataset from Microsoft Asia, researchers first extract features like velocity and acceleration to generate images. Then, the deep learning model divides these images into patches to identify transport modes such as walking, biking, and driving. While achieving 92.96% accuracy, misclassification of certain modes, like cars, and unbalanced training data highlight the need for further feature analysis and improvement.
Autonomous Vehicles (AVs) in Asia
While driverless vehicles have long promised safety and sustainability, technological, regulatory and cost barriers have slowed down widespread deployment. But researchers encourage us to look beyond current AV discourse that is shaped by technological deterministic narratives and a narrow focus on ‘trolley problem’ ethical concerns to consider how AVs are deeply political, intertwined with urban environments, labour, infrastructure, and mobility.
Mostly emerging in high-income countries in Asia, such as Singapore’s Grab and Moovita, South Korea’s A2Z and China’s ApolloGo and Pony.ai robotaxis, AV rollout is still limited in size and scope due to requirements for data governance, access, infrastructure, and public acceptance. In contrast to usual goals for optimisation and efficiency, for Singapore, the highest ranking in AV readiness, AVs have become a strategic investment to address land scarcity and labour shortages across transport and delivery sectors. In other parts of Asia, AV infrastructure requirements may create inequities. Some challenges for deployment include the high cost and complexity of software and equipment, and reliance on high-speed network connection and urban communication infrastructure – all of which have an impact on safety. In South and Southeast Asia, AVs also have to account for dynamic and chaotic road conditions and transport modes like tuk-tuks, jeepneys, and rickshaws. This suggests that AV use may be restricted to areas with predictable road conditions that can afford to build and maintain this infrastructure, bringing up questions of spatial equity in urban mobility access.
In rapidly developing urban contexts, responsible steering of AVs towards shared transport models could transform mobility ecosystems, but this requires long-term, localised, multi-stakeholder planning. In case studies on potential governance approaches to multi-modal AV deployment in Ho Chi Minh City and Bangkok, authors present a comprehensive framework on how different policy pathways adapted to unique urban contexts could shape AV implementation. They argue that without regulatory guidance, AV deployment in the region could exacerbate urban sprawl, congestion, heightened emissions, and socio-spatial inequities. They recommend context-specific participatory planning between local and regional governments, communities, and mobility actors.
Mobility data justice
Modern mobility increasingly involves the generation, storage, and sharing of data, raising pertinent questions: What inequalities arise when mobility intersects with datafication? Whose movement is captured or overlooked in data collection and sharing, and what factors influence these inclusions and exclusions?
Researchers highlight ‘data and information asymmetry’ between the public and private sectors, where governments provide open transport data while businesses use it to develop and profit from mobility services. They note tensions emerge between ‘the need to be seen and represented’ versus ‘the need for autonomy and integrity’ – for example, data from app-based transport represents those who can afford it, rendering other commuters invisible.
To challenge discrimination in mobility data, they develop a mobility data justice framework, centered on:
Distributive justice: How access to mobility and data is unequally distributed, who benefits or is harmed, and how marginalised groups reclaim control through counter-mapping and commons-based approaches.
Procedural justice: Who designs and controls mobility data infrastructures, highlighting the exclusionary role of AI and governance systems.
Epistemic justice: What counts as mobility-related knowledge, how data systems shape inequalities, and how alternative ways of knowing can promote fairness.
Electric Vehicles & the Nickel Pickle
AI is expected to play a crucial role in electric vehicles (EV) - from optimising energy management to predictive maintenance. But like our last issue, we find ourselves in a similar pickle when looking at the EV battery supply chain - a case of carbon-intensive processing with outsized human impacts. While most battery manufacturing happens in Japan, South Korea and China, in Southeast Asia, Vietnam and Indonesia are emerging as battery hubs, due to their nickel reserves, a key input for battery cathodes.
Indonesia’s reliance on coal-powered smelting for nickel production has made them one of the highest emitters amongst producers globally. Difficulties in disposing of industrial waste have also halted deep sea mining waste disposal. While Variable Renewable Tech and Hydro/Geothermal power are alternatives, high electricity requirements and the remote locations of Indonesia’s mining sites remain barriers to meeting heightened battery supply chain standards.
In reports from CRI and Mighty Earth, Indonesian communities surrounding the Weda Bay Industrial Park face unlawful land dispossession, unfair compensation, and disruption to indigenous livelihoods. Wildlife and crops have been impacted, and water and air pollution are causing health impacts. There is also rapid deforestation due to expanded mining activities, including in protected areas. Both reports stress the importance of private and public commitment to transparency in EV battery supply chains, stronger observation of Indonesian laws protecting indigenous lands and protected forests, and adequate compensation for those experiencing harms.
Advancing sustainable and equitable transport futures
Data justice issues, inequitable access, and compounding ethical and environmental harms complicate AI-enabled green mobility transitions in the region. In a recent study, authors argue that current sustainability efforts in the sector are insufficient as they focus on the least effective action - improving mobility efficiency. As a complex system embedded within the broader urban environment, urban mobility requires a systems-thinking approach, advanced by:
Cross-disciplinary collaboration between engineers, planners, economists, and behavioral scientists.
Simulation-based approaches using reusable models and datasets to predict and evaluate intervention outcomes.
Transparency and ethical research addressing privacy concerns, unequal data access, quantifying environmental costs, and abandoning projects that cause harms.
Public engagement by researchers to fact-check misinformation and communicate findings quickly to support evidence-based policymaking.
On our Radar: Mobility-as-a-Service (MaaS)
MaaS refers to the integration of public transit, ride-hailing, bike-sharing, and car rentals into one digital service. Popularised since 2014 as a flexible, sustainable, user-friendly mobility solution, it is still untested in localised contexts, and a lack of empirical studies and real-world applications makes it difficult to validate its benefits.
MaaS has found some success in Japan, as researchers found that a MaaS-AI on-demand shuttle significantly improved the elderly’s access to healthcare and shopping activities based on place (geographical distances) and person-based (individual’s unique behaviour) factors. However, what may work in developed economies may not be easily transferable to all contexts - insufficient physical infrastructure, entrenched institutional frameworks, lack of data standards, and fragmented operator groups pose challenges. In developing contexts, government-led rather than private models may be more suitable to ensure equitable and sustainable implementation.
Further, these authors warn that if AI-enabled MaaS relies solely on digital data reflecting real-world gender or age-based mobility access issues, it may exacerbate these inequalities. AI recommender algorithms could also prioritise convenience over equity, favouring cars over active and environmentally responsible modes like cycling. The integration of multiple data sources by private actors offering AI services poses a risk of corporate power concentration and standardised solutions. To address this, a collaborative governance model for MaaS, centered on sustainability, transparency, and accountability while balancing public interest and innovation, can foster equity in access.
MaaS as a concept is also evolving. In this vision paper, researchers envision Mobility-as-a-Resource (MaaR) - a shared resource managed by technology. Data science is essential to MaaR - providing cross-disciplinary insights and integrating knowledge from travel behaviour, traffic flow, and network science to support equitable mobility. But it is imperative that MaaR is not reliant on market-based systems alone - its implementation must be carefully managed with new policy tools and regulatory frameworks to account for digital divides and mobility inequalities, and to reduce environmental harms.
Around the Web
Slime mould grows network just like Tokyo Rail System: A fascinating peek into how single-celled slime mould construct nutrient-channeling tubes strikingly similar to Tokyo’s railway networks.
The atlas of sustainable city transport: Curious about sustainable transport in your city? Explore this atlas to know how close you are to public transit.
Grab built its own map in Southeast Asia, and is now going after Google: A deep-dive into how Grab used their drivers and cameras to create hyperlocal maps in Southeast Asia.
Transport-Our World in Data: A visualisation of global transport patterns and their environmental impacts.
Tuning in…
In the latest episode of Code Green, Huê-Tâm Jamme and Kris Villanueva-Libunao discuss mobility transitions in Asia and AI’s role in the sector. Together, they advocate for a whole-of-systems approach to mobility infrastructure, centering people’s needs and experiences in planning and policy-making for the future.
Credits
Research and curation: Dona Mathew, Meredith Stinger, Tammanna Aurora | Illustrations: Nayantara Surendranath | Art Direction: Tammanna Aurora | Layout Design: Shivranjana Rathore