New framework reveals where transport emissions concentrate in Singapore

(Press-News.org) Compact, mixed-use districts are often assumed to naturally produce cleaner travel patterns, but the reality on the ground is far more complex.

 

In Singapore, for instance, two adjacent employment hubs—One-North and Science Park—share similar locations but differ sharply in how people move through them. A new study from the Singapore University of Technology and Design (SUTD) reveals why and offers a high-resolution approach to understanding where emissions accumulate within districts rather than across entire cities.

 

Published in a research paper titled “A spatial framework for estimating transport emissions at the district scale: A case study of Singapore’s integrated urban typologies,” the study combined mobile phone GPS data, smartcard public transit records, public transit network data, land-use information, and vehicle emissions factors to build a bottom-up picture of how cars, buses, and the metro contribute to carbon emissions across the two major business districts in Singapore’s Queenstown.

 

“While existing studies largely examine transport-related carbon emissions at city or regional scales, little is known about how district-level design shapes mobility behaviour and associated emissions,” said Sanjana Singh Raichur, Research Associate at SUTD. “By analysing mobility patterns at the district scale, this study fills a critical gap—identifying emission hotspots and providing evidence for targeted, scalable interventions to support low-carbon urban development.”

 

Co-first author and SUTD researcher Dr Irina Orlenko noted that mixed-use districts are often thought to perform better, but data to evaluate them has been limited. “We still know relatively little about how specific district-level planning and design choices influence people’s daily mobility behaviour and the emissions that result,” she shared. “Using large-scale mobile phone data, we revealed mobility patterns and emission hotspots that are not visible through traditional surveys or aggregate datasets.”

 

To build this picture, the team reconstructed anonymous movement traces from mobile phone data, parsing millions of location points into inferred home, work, and activity locations. From there, they identified trip chains, dominant travel modes, and peak-hour patterns and validated the results with public transport smartcard flows, and census data. These trip patterns were then converted into vehicle- and passenger-kilometres for each transport mode, enabling emissions estimation at a fine spatial scale.

 

“With this approach, we designed a processing pipeline that cleans vast amounts of passive mobile records and transforms them into meaningful representations of human movement that planners can directly use,” said Dr Orlenko. “The expanded mobility patterns were then aggregated to a grid to identify emission hotspots, offering a low-cost alternative for transport and urban planning authorities.”

 

The results uncovered a striking contrast between total emissions and emissions efficiency. One-North recorded higher overall emissions—mainly because it attracts far more daily users—but performed substantially better once the numbers are normalised per person.

 

“Although One-North showed higher overall emissions largely due to greater level of activity, it performed significantly better when evaluated on a per-capita basis,” Raichur explained. “Once we accounted for the number of district users, One-North emerged as the more efficient district, with emissions efficiency approximately 60 percent higher than Science Park.”

 

Yet the findings also showed that land-use integration alone does not eliminate car dependency. Despite being transit-rich, One-North’s northern and central areas still registered high car-related emissions. These areas include dense clusters of offices where activity remains strong throughout the day.

 

“High-density activity can itself generate significant car traffic,” Raichur noted. “Without complementary measures such as higher parking fees or road-user charges, mixed-use planning and improved transit do not fully shift travel behaviour.”

 

At Science Park, its per-passenger bus emissions were markedly higher. The researchers found that travel demand dropped sharply outside peak hours at this predominantly office-based district, leaving buses with fewer riders. On the other hand, One-North’s continuous activity—supported by retail, amenities, and housing nearby—kept buses fuller and more efficient. The study also pointed to the role of shuttle services, which often absorb demand that would otherwise go to public buses, altering occupancy patterns.

 

Spatial mapping further revealed where emissions concentrate: major arterial roads, edges of districts with weaker transit access, and busy corridors such as North Buona Vista Road. These findings translates directly into actionable insights that urban planners can consider, such as electrifying buses along high-demand routes, improving first- and last-mile connectivity, and strengthening cycling networks for short trips that dominate both districts.

 

Looking ahead, the team highlights future research directions, including analysing how street layout, land-use mix, and activity patterns influence the emergence of hotspots.

 

“Future studies should examine the underlying planning and design factors that may be reinforcing these emissions,” Dr Orlenko said. “Identifying these drivers will be essential for refining district planning strategies and ensuring that sustainable districts deliver the low-carbon outcomes they are intended to achieve.”

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