Traditional approaches to modeling human mobility often depend heavily on extensive travel surveys and abundant data streams, which are not universally accessible. In many parts of the world, especially in resource-limited or underdeveloped areas, these data gaps hinder the accurate depiction of movement patterns critical for local governance and planning. The neuroGravity model addresses this challenge head-on by leveraging publicly available information such as urban facility distributions and population densities. It bypasses the need for exhaustive mobility datasets, reconstructing flows with a level of fidelity that was previously unattainable with scarce data.

At the heart of neuroGravity lies its novel architecture, which marries physical principles governing human movement with state-of-the-art deep learning techniques. This physics-informed approach ensures the model is not just a black box but a system that incorporates spatial interactions and constraints observed in real-world mobility. By encoding fundamental transportation and urban spatial dynamics, neuroGravity generates regional embeddings that carry deep insights into mobility flows without relying on extensive ground-truth observables.

A particularly striking aspect of neuroGravity’s design is its transferability. Unlike many data-intensive machine learning models, neuroGravity can be trained on data-rich cities and then applied successfully to reconstruct mobility in cities where no mobility data exists. This transfer learning capability extends the impact of the model globally, dramatically broadening its utility for cities that would otherwise be left in data darkness. The implications are profound: urban planners and policymakers across continents could potentially rely on neuroGravity’s reconstructions as proxies for expensive and cumbersome surveys.

The researchers behind neuroGravity discovered a compelling link between the model’s transferability and socioeconomic factors, particularly spatial income segregation within urban environments. Income segregation refers to the degree to which residents of varying income levels are spatially separated, influencing travel behaviors and network connectivity. The model transferred most effectively between cities exhibiting similar patterns of income segregation, suggesting that shared social and spatial dynamics underpin the predictability of human movement.

To quantify and harness this insight, the team developed a novel segregation index that measures spatial income segregation levels systematically. This index acts as a predictive gauge for the model’s transferability, offering a data-driven way to select appropriate source cities for training when aiming to reconstruct mobility networks in a target city with no data. The ability to anticipate performance boosts confidence in deploying neuroGravity in unfamiliar urban contexts.

Beyond theoretical advances, neuroGravity’s practical impact is already taking shape. The research team applied their model to generate proxy mobility flow datasets for over 1,200 cities globally, encompassing vast regions of the developing world that have long suffered from data shortages. These reconstructed networks hold enormous promise for improving urban management at scale, enabling evidence-based decision-making that was previously out of reach.

The implications of this research extend into public health realms as well. Accurate human mobility data are critical during epidemics and pandemics to anticipate disease spread and implement targeted interventions. NeuroGravity offers an avenue for timely, reliable proxies of population movement to inform strategies, particularly in settings lacking robust surveillance infrastructure.

Moreover, the regional embeddings learned by neuroGravity correlate strongly with indicators of socioeconomic status and urban livability. This suggests a dual function of the model: not only reconstructing mobility flows but also offering new metrics that capture underlying social and economic dynamics at a regional level. Such proxies could complement or even replace the need for costly and logistically challenging surveys currently employed to assess urban well-being.

On the technical front, neuroGravity’s framework integrates urban facility data—such as locations of workplaces, schools, and shops—with population distributions and a physically grounded representation of how people choose destinations by distance and resource availability. The deep learning model is trained to understand and generalize these interactions, producing fine-grained estimations of origin-destination flows that mirror real-world patterns closely.

The model’s architecture leverages graph neural network components that efficiently encode the complex spatial relationships across urban zones, capturing not only physical proximity but also functional connectivity shaped by amenities and socioeconomic factors. This method surpasses simpler gravity or radiation models, adding nuance and adaptability essential for accurate reconstructions through transfer learning.

Robust validation on observed cities demonstrated neuroGravity’s superior performance compared to baseline methods in reconstructing detailed mobility flows. The results showed remarkably low errors and high correlation with empirical data, attesting to the power of incorporating physics-informed constraints into deep learning paradigms.

Looking forward, the research team envisions enhancing neuroGravity by integrating additional urban features, such as transportation networks and temporal dynamics, to capture peak travel hours and variability in movement. They also foresee its application expanding into emergency response scenarios and urban sustainability planning, where understanding human dynamics swiftly and accurately is paramount.

Ultimately, neuroGravity marks a breakthrough at the intersection of artificial intelligence, urban science, and socioeconomics. By fusing physics-based modeling with deep learning and leveraging modest yet widely accessible data, it provides a scalable solution for mapping human movement worldwide. In doing so, it bridges critical data gaps, offering equitable access to insights that can foster resilient and livable cities, especially across the globe’s most vulnerable regions.

As urban populations surge and the challenges confronting cities multiply, tools like neuroGravity pave the way toward smarter, data-driven futures. Its transferability across socioeconomically diverse cities underscores a fundamental truth: despite differences, shared spatial and income patterns govern how humans navigate their environments, and these patterns can be decoded and predicted with sophisticated modeling. This paradigm shift holds promise not only for science but for the millions who stand to benefit from better-informed urban contexts.

In summary, neuroGravity’s introduction heralds a new frontier in human mobility research. It democratizes access to vital movement data, reveals socio-spatial determinants of mobility, and opens expansive avenues for application. As the global urban tapestry becomes ever more dynamic, such innovative modeling approaches will be indispensable in shaping cities that are adaptive, inclusive, and sustainable for generations to come.

Subject of Research: Transferable reconstruction of human mobility networks using physics-informed deep learning models.

Article Title: Transferable human mobility network reconstruction with neuroGravity.

Article References:
Yang, J., Huang, S., Huang, Z. et al. Transferable human mobility network reconstruction with neuroGravity. Nat Comput Sci (2026). https://doi.org/10.1038/s43588-026-01003-y

Image Credits: AI Generated

DOI: https://doi.org/10.1038/s43588-026-01003-y

The rapid expansion of the global population, projected to reach an estimated 10.3 billion by the mid-2080s, is expected to fuel unprecedented demand for urban infrastructure. This intense urbanization trajectory poses a double-edged sword; while it necessitates vast quantities of construction materials, it also exacerbates industrial carbon emissions. Among these, Portland cement production remains the largest single industrial emitter due to the calcination process that releases CO₂ when limestone is heated to produce clinker. Consequently, the imperative to discover alternative binder materials that reduce reliance on Portland cement emerges as a paramount environmental priority.

Dr. Fakhruddin’s research focuses on the formulation of Class C fly ash-based geopolymer concrete (GPC) infused with SCBA, a by-product abundant in sugarcane processing industries, combined with PP fibers to enhance mechanical properties. Unlike ordinary Portland cement, geopolymer concrete utilizes aluminosilicate materials activated by alkaline solutions to form a robust binder, significantly lowering carbon emissions associated with cement clinker production. However, GPC’s inherent brittleness has limited its widespread adoption in structural applications, a challenge addressed by the incorporation of fibers.

The study meticulously evaluated three geopolymer concrete formulations: a control mix with no SCBA, and two variants substituting 5% and 10% of the fly ash with SCBA, each maintaining a constant fiber concentration of 0.6 kilograms per cubic meter. Comprehensive tests assessed compressive, tensile, and flexural strengths, alongside microstructural analysis through scanning electron microscopy. The environmental metrics incorporated a life cycle perspective, quantifying carbon emissions and cost-efficiency relative to performance.

Remarkably, the mix containing 5% SCBA (SCBA-5) exhibited a substantial leap in mechanical performance, boasting a 41% increase in compressive strength, a 29% enhancement in tensile strength, and a 56% boost in fracture energy compared to the control. These improvements signal enhanced ductility and crack resistance, attributes critical for structural integrity under dynamic loading. Conversely, the 10% SCBA mix (SCBA-10) augmented flexural strength by 9.3% but introduced increased brittleness, indicating a threshold beyond which SCBA content may become detrimental to toughness.

The microstructural investigations revealed that SCBA particles interact synergistically with fly ash and alkaline activators, densifying the concrete matrix and enhancing cohesiveness. Concurrently, the inclusion of PP fibers acts at a microscale to arrest crack propagation by bridging fracture surfaces, thereby elevating the tensile capacity and delaying failure. This composite action facilitates a cohesive microstructure capable of dissipating energy and resisting brittle fracture, a key advancement over traditional GPC formulations.

From an environmental standpoint, the SCBA-5 mixture achieves a remarkable 25–30% reduction in CO₂ emissions relative to conventional Portland cement concrete, without sacrificing, and in fact improving, mechanical performance. Furthermore, this formulation demonstrates a 52% higher strength-to-carbon ratio and a 53% increased strength-to-cost ratio, indicating not only ecological but also economic viability. These findings position the sugarcane waste-based geopolymer concrete as a compelling candidate in the transition toward sustainable construction materials.

The potential for scaling this innovative material is particularly significant in regions such as Indonesia, where sugarcane production yields massive quantities of bagasse ash as industrial waste. Utilizing this by-product in construction not only minimizes waste disposal challenges but also fosters a circular economy by valorizing agro-industrial residues. Moreover, this approach aligns with global Sustainable Development Goal 12, focusing on responsible consumption and production patterns, a framework increasingly embraced by governments and industries worldwide.

Dr. Fakhruddin emphasizes that, while the study primarily focused on early-age mechanical properties and environmental assessments, the long-term durability and performance of SCBA-incorporated geopolymer concrete under varying environmental stresses warrant further exploration. Future research directions include investigating the material’s resistance to chemical attack, freeze-thaw cycles, and prolonged mechanical loading, crucial for ensuring the material’s reliability across diverse climatic and service conditions.

The practical implications extend to structural applications where sustainable materials must meet stringent safety and performance criteria. The SCBA-5 mix’s balanced enhancement in strength, ductility, and durability renders it suitable for low-rise building structures and non-prestressed concrete members, offering a realistic pathway for adoption in mainstream construction practices. Additionally, the reduction in carbon footprint supports global efforts to mitigate climate change impacts, contributing to a decarbonized built environment.

Importantly, the research conducted by Hasanuddin University, one of Indonesia’s premier autonomous institutions with a strong focus on engineering and sustainable development, highlights the critical role of academic innovation in driving industry transformation. Dr. Fakhruddin’s work exemplifies how locally available materials can be harnessed to produce globally relevant technology, reinforcing the nexus between environmental responsibility and engineering advancement.

As urbanization and infrastructure development proceed unabated, the integration of geopolymer concrete enhanced with sugarcane bagasse ash and polypropylene fibers marks a pivotal innovation. This sustainable composite not only reduces reliance on carbon-intensive cement but also adds tangible value in mechanical performance and cost-effectiveness. It embodies a promising intersection of ecological consideration, economic practicality, and structural resilience—a blueprint for future construction materials in an era demanding environmental consciousness and technological excellence.

Subject of Research: Not applicable
Article Title: Mechanical and sustainability assessment of sugarcane bagasse ash and polypropylene fiber in Class C fly ash geopolymer concrete
News Publication Date: 1-Mar-2026
Web References: Results in Engineering – Article Link
References: DOI: 10.1016/j.rineng.2025.108724
Image Credits: “Vanishing point” by Paul Bica via Flickr
Keywords: Civil engineering, Construction materials, Construction techniques, Construction engineering, Engineering, Applied sciences and engineering, Sustainable development, Sugarcane, Agriculture, Environmental sciences, Carbon emissions, Pollution