The rapid growth of tropical cities and the rising challenges of climate change call for efficient, low-carbon energy systems. Solar photovoltaics could play a key role, but deployment in tropical climates is constrained by localized thunderstorms that cause rapid generation fluctuations and stress electricity grids. While electric vehicles could balance such fluctuations by acting as distributed energy storage, this potential has not been systematically explored. Here, using Singapore as a case study, we develop a decentralized, district-level vehicle charging strategy that aligns with urban mobility patterns inferred from mobile phone data. Contrary to conventional centralized charging strategies, our approach substantially reduces grid flows, enabling greater photovoltaic integration into the existing grid infrastructure. We further show that detailed urban mobility patterns are critical to the balancing performance of electric vehicle storage. Our results highlight the potential of coordinated photovoltaic and electric vehicle systems for large-scale solar energy deployment in tropical cities.

Urban transportation network design is typically approached through top-down planning grounded in engineering and economics. Yet cities are complex systems characterized by feedback loops between infrastructure and mobility demand: network structure shapes origin-destination (OD) flows, while OD flows adapt in turn affect network development. Despite this interdependence, computational tools to benchmark world networks against alternative generative principles remain limited. Here, we introduce a data-driven framework to compare three types of metro networks: a self-organized (desire-path) network derived from local rules, a system-optimal network derived from global optimization, and the empirical real-world network. quantify similarities and discrepancies using geometric comparisons and optimal transport theory. Applying framework to Singapore's Mass Rapid Transit system, we find that the empirical metro network is substantially closer to the self-organized benchmark than to the system-optimal benchmark, with remaining discrepancies largely explained by geographic constraints. These findings highlight that system-wide optimality alone be inadequate for guiding practical interventions, motivating planning approaches that explicitly incorporate local service needs. The framework is transferable across regions and can diagnose design-use misalignments to support adaptive infrastructure planning.

Although a number of studies have investigated human mobility patterns during natural hazards, mechanistic models that capture mobility dynamics under large-scale perturbations, such as extreme floods, remain scarce. Leveraging mobile phone data and building upon recent insights into universal mobility patterns, we assess whether the general structure of population flows persists during the extreme floods that struck Emilia-Romagna, Italy, in 2023. Our analysis reveals that the relationship between visitor density, distance, and visitation frequency remains robust even under extreme flooding conditions. To disentangle the effects of distance and visitation frequency, we define two aggregated visitor densities: the marginal density over frequency and the aggregated density over distance. We find that the marginal density over frequency exhibits a time-invariant power-law exponent, indicating resilience to flooding disturbances. In contrast, the aggregated density over distance displays more complex behavior: an exponential decay over biweekly periods and a power-law decay over a monthly interval. We propose that the observed power law emerges from the superposition of exponential distributions across shorter timescales. These findings provide new insights into human mobility scaling laws under extreme perturbations, highlighting the robustness of visitation patterns and suggesting avenues for improved mechanistic modeling during natural disasters.

The mobility patterns of people in cities evolve alongside changes in land use and population. This makes it crucial for urban planners to simulate and analyze human mobility patterns for purposes such as transportation optimization and sustainable urban development. Existing generative models borrowed from machine learning rely heavily on historical trajectories and often overlook evolving factors like changes in population density and land use. Mechanistic approaches incorporate population density and facility distribution but assume static scenarios, limiting their utility for future projections where historical data for calibration is unavailable. This study introduces a novel, data-driven approach for generating origin-destination mobility flows tailored to simulated urban scenarios. Our method leverages adaptive factors such as dynamic region sizes and land use archetypes, and it utilizes conditional generative adversarial networks (cGANs) to blend historical data with these adaptive parameters. The approach facilitates rapid mobility flow generation with adjustable spatial granularity based on regions of interest, without requiring extensive calibration data or complex behavior modeling. The promising performance of our approach is demonstrated by its application to mobile phone data from Singapore, and by its comparison with existing methods.

New electricity demands from electric vehicle (EV) charging introduce significant challenges to the power supply infrastructure. This paper introduces a methodology for modeling the spatial-temporal EV charging demand by leveraging mobility data. The detailed individual mobility patterns extracted from these data enable the simulation of the district-level EV charging demand profiles. Further, by incorporating non-EV demand (e.g., household consumption) data, we also identify critical areas that require grid expansion to accommodate this new demand. Our study contributes to the understanding of the spatial-temporal vehicle-grid interactions, laying the groundwork for the development of EV charging strategies.

Human mobility has been significantly affected by COVID-19 and associated travel restrictions imposed by government policies. This study examines changes in mobility patterns in Singapore during different stages of the pandemic using mobile phone data. Our results indicate that population mobility decreased over the COVID pandemic and is slowly increasing after the restrictions were lifted. However, there have been changes in the mobility patterns of the population. Despite the easing of COVID-19 measures, the population is making fewer trips and the trip distances are longer for some people. This change may be attributed to several factors. One of which is that the population has not quite come back to the pre-COVID working routine and has adopted a semi-work-from-home policy, where the staff is working in the office for a few days a week. The other factor is the habit of making more “purposeful” trips remains. Further, we investigate changes in mobility patterns among the classified explorers and returners. The results demonstrate that the change in mobility has a similar trend to the population as a whole. These observed changes in mobility patterns may become the new normal and should be taken into account for traffic management, business decision-making, policy-making, and urban or transport infrastructure planning.

The ability to understand and predict the flows of people in cities is crucial for the planning of transportation systems and other urban infrastructures. Deep-learning approaches are powerful since they can capture non-linear relations between geographic features and the resulting mobility flow from a given origin location to a destination location. However, existing methods are not able to quantify the uncertainty of the predictions, which limits their interpretability and thus their use for practical applications in urban infrastructure planning. To that end, we propose a Bayesian deep-learning approach that formulates deep neural networks as Gaussian processes and integrates automatic variable selection. Our method provides uncertainty estimates for the predicted origin-destination flows while also allowing to identify the most important geographic features that drive the mobility patterns. The developed machine learning approach is applied to large-scale taxi trip data from New York City.

This paper investigates the potential of building attached / integrated Photovoltaic (PV) and vehicle-to-grid (V2G) coupling for the city of Singapore.Using the city's 55 planning areas as spatial units, a linear programming (LP) optimization model is developed to determine economically optimal PV scaling and charge/discharge strategies within and across planning areas.Mobility flows between planning areas are assessed using a large set of GPS mobile phone records, from which electric vehicle (EV) schedules are derived.Local electricity demand and solar potentials are modelled using a bottom-up approach based on building geometries and land use information, and loads are calibrated to match measured aggregate city loads.Parametrized assumptions in our model are systematically tested through scenario analysis, including varying carbon taxes, PV system cost, EV penetration, wholesale electricity prices, and local building self-consumption levels.Our study finds significant economic and environmental potential for PV systems, while economic benefits of V2G are strongly scenario dependent but generally limited.This may be explained by the high on-site PV electricity self-consumption potential due to the electricity loads generally exceeding PV generation.However, through the aggregation to the planning area level in our model, local building-resolved mismatches in production and demand were partially flattened, and thus the potential for V2G to act as intermediate storage can be expected to be higher when modelled at a finer spatial resolution.In order to gain further insight, future research could focus on combining large-scale city dynamics with more fine-grained local analysis, e.g., by limiting the analysis to one district only, as well as incorporate explicit grid balancing constraints in the model.

The vehicle-to-grid (V2G) concept utilises electric vehicles as distributed energy storage and thus may help to balance out the intermittent availability of renewable energy sources such as photovoltaics. V2G is therefore considered to play an important role for achieving low-carbon energy and transportation systems in cities. However, the adequate planning of city-wide V2G infrastructures requires detailed knowledge of the aggregate mobility patterns of individuals and also needs to keep track with ongoing developments of urban transportation modes. Here, we introduce an initial framework that infers population-wide mobility patterns from anonymised mobile phone location data and subsequently superimposes a vehicle charging and discharging scheme. The framework allows for the estimation of the aggregate V2G energy supply and demand at fine-grained spatial and temporal scales under a given electric vehicle usage scenario. This information provides an adequate basis for assessing the role of V2G in the context of maximising the deployment of photovoltaics, as well as for the sizing and placement of the required vehicle (dis)charging infrastructure. The proposed framework is applied to Singapore as a case study.

Reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people’s lives—food, education, healthcare. It spurs new economic opportunities, thus improving livelihoods. Using a comprehensive dataset of pseudonymized mobile phone records, we analyse the impact of electrification on attractiveness for rural areas in Senegal. We extract communication and mobility flows from call detail records and show that electrification is positively and specifically correlated with centrality measures within the communication network and with the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination and the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than centralizing electricity supplies to maximize the development of specifically targeted sites.

Human mobility impacts many aspects of a city, from its spatial structure1-3 to its response to an epidemic4-7. It is also ultimately key to social interactions8, innovation9,10 and productivity11. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models-such as the gravity law12,13 or the radiation model14-concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf's law15. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory16 and Weber's theory of emergent optimality10) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a popular proxy to evaluate the density, activity and social characteristics of a population. They offer additional advantages: they are updated in real-time, include mobility information and record visitors' activity. However, we show with the example of Senegal that the direct correlation between the average phone activity and both the population density and the nighttime lights intensity may be insufficiently high to provide an accurate representation of the situation. There are reasons to expect this, such as the heterogeneity of the market share or the particular granularity of the distribution of cell towers. In contrast, we present a method based on the daily, weekly and yearly phone activity curves and on the network characteristics of the mobile phone data, that allows to estimate more accurately such information without compromising people's privacy. This information can be vital for development and infrastructure planning. In particular, this method could help to reduce significantly the logistic costs of data collection in the particularly budget-constrained context of developing countries.

Human mobility patterns are surprisingly structured. In spite of many hard to model factors, such as climate, culture, and socioeconomic opportunities, aggregate migration rates obey a universal, parameter-free, `radiation' model. Recent work has further shown that the detailed spectral decomposition of these flows -- defined as the number of individuals that visit a given location with frequency $f$ from a distance $r$ away -- also obeys simple rules, namely, scaling as a universal inverse square law in the combination, $rf$. However, this surprising regularity, derived on general grounds, has not been explained through microscopic mechanisms of individual behavior. Here we confirm this by analyzing large-scale cell phone datasets from three distinct regions and show that a direct consequence of this scaling law is that the average `travel energy' spent by visitors to a given location is constant across space, a finding reminiscent of the well-known travel budget hypothesis of human movement. The attractivity of different locations, which we define by the total number of visits to that location, also admits non-trivial, spatially-clustered structure. The observed pattern is consistent with the well-known central place theory in urban geography, as well as with the notion of Weber optimality in spatial economy, hinting to a collective human capacity of optimizing recurrent movements. We close by proposing a simple, microscopic human mobility model which simultaneously captures all our empirical findings. Our results have relevance for transportation, urban planning, geography, and other disciplines in which a deeper understanding of aggregate human mobility is key.

A reliable and affordable access to electricity has become one of the basic needs for humans and is, as such, at the top of the development agenda. It contributes to socio-economic development by transforming the whole spectrum of people's lives - food, education, health care; it spurs new economic opportunities and thus improves livelihoods. Using a comprehensive dataset of pseudonymised mobile phone records, provided by the market share leader, we analyse the impact of electrification on the attractiveness of rural areas in Senegal. We extract communication and mobility flows from the call detail records (CDRs) and show that electrification has a small, yet positive and specific, impact on centrality measures within the communication network and on the volume of incoming visitors. This increased influence is however circumscribed to a limited spatial extent, creating a complex competition with nearby areas. Nevertheless, we found that the volume of visitors between any two sites could be well predicted from the level of electrification at the destination combined with the living standard at the origin. In view of these results, we discuss how to obtain the best outcomes from a rural electrification planning strategy. We determine that electrifying clusters of rural sites is a better solution than attempting to centralise electricity supplies to maximise the development of specifically targeted sites.

Future land use/cover change (LUCC) analysis has been increasingly applied to spatial planning instruments in the last few years. Nevertheless, stakeholder participation in the land use modelling process and analysis is still low. This paper describes a methodology engaging stakeholders (from the land use planning, agriculture, and forest sectors) in the building and assessment of future LUCC scenarios. We selected as case study the Torres Vedras Municipality (Portugal), a peri-urban region near Lisbon. Our analysis encompasses a participatory workshop to analyse LUCC model outcomes, based on farmer LUCC intentions, for the following scenarios: A0 - current social and economic trend (Business as Usual); A1 - regional food security; A2 - climate change; and B0 - farming under urban pressure. This analysis allowed local stakeholders to develop and discuss their own views on the most plausible future LUCC for the following land use classes: artificial surfaces, non-irrigated arable land, permanently irrigated land, permanent crops and heterogeneous agricultural land, pastures, forest and semi-natural areas, and water bodies and wetlands. Subsequently, we spatialized these LUCC views into a hybrid model (Cellular Automata - Geographic Information Systems), identifying the most suitable land conversion areas. We refer to this model, implemented in NetLogo, as the stakeholder-LUCC model. The results presented in this paper model where, when, why, and what conversions may occur in the future in regard to stakeholders' points of view. These outcomes can better enable decision-makers to perform land use planning more efficiently and develop measures to prevent undesirable futures, particularly in extreme events such as scenarios of food security, climate change, and/or farming under pressure.

This is a reply to Martilli et al. (2020), Summer average urban-rural surface temperature differences do not indicate the need for urban heat reduction (https://doi.org/10.31219/osf.io/8gnbf).

Urban heat islands (UHIs) exacerbate the risk of heat-related mortality associated with global climate change. The intensity of UHIs varies with population size and mean annual precipitation, but a unifying explanation for this variation is lacking, and there are no geographically targeted guidelines for heat mitigation. Here we analyse summertime differences between urban and rural surface temperatures (ΔTs) worldwide and find a nonlinear increase in ΔTs with precipitation that is controlled by water or energy limitations on evapotranspiration and that modulates the scaling of ΔTs with city size. We introduce a coarse-grained model that links population, background climate, and UHI intensity, and show that urban-rural differences in evapotranspiration and convection efficiency are the main determinants of warming. The direct implication of these nonlinearities is that mitigation strategies aimed at increasing green cover and albedo are more efficient in dry regions, whereas the challenge of cooling tropical cities will require innovative solutions.

High quality census data are not always available in developing countries. Instead, mobile phone data are becoming a go to proxy to evaluate population density, activity and social characteristics. They offer additional advantages for infrastructure planning such as being updated in real-time, including mobility information and recording temporary visitors' activity. We combine various data sets from Senegal to evaluate mobile phone data's potential to replace insufficient census data for infrastructure planning in developing countries. As an applied case, we test their ability at predicting accurately domestic electricity consumption. We show that, contrary to common belief, average mobile phone activity is not well correlated with population density. However, it can provide better electricity consumption estimates than basic census data. More importantly, we successfully use curve and network clustering techniques to enhance the accuracy of the predictions, to recover good population mapping potential and to reduce the collection of informative data for planning to substantially smaller samples.