Esra Suel

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Suel

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Esra

ORCID

0000-0001-9246-3966

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Publications1 - 10 of 13

Influence of tracking duration on the privacy of individual mobility graphs
Wiedemann, Nina; Martin, Henry; Suel, Esra; et al. (2023)
Journal of Location Based Services
Location graphs, compact representations of human mobility without geocoordinates, can be used to personalise location-based services. While they are more privacy-preserving than raw tracking data, it was shown that they still hold a considerable risk for users to be re-identified solely by the graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match the new tracking data of a user to a pool of previously recorded mobility profiles, and we analyse the dependence of the re-identification performance on the tracking duration. We find that the re-identification accuracy varies between 0.41% and 20.97% and is affected by both the pool duration and the test-user tracking duration, it is greater if both have the same duration, and it is not significantly affected by socio-demographics such as age or gender, but can to some extent be explained by different mobility and graph features. Overall, the influence of tracking duration on user privacy has clear implications for data collection and storage strategies. We advise data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
Vehicle-to-grid for car sharing - A simulation study for 2030
Wiedemann, Nina; Xin, Yanan; Medici, Vasco; et al. (2024)
Applied Energy
The proliferation of car sharing services in recent years presents a promising avenue for advancing sustainable transportation. Beyond merely reducing car ownership rates, these systems can play a pivotal role in bolstering grid stability through the provision of ancillary services via vehicle-to-grid (V2G) technologies - a facet that has received limited attention in previous research. In this study, we analyze the potential of V2G in car sharing by designing future scenarios for a national-scale service in Switzerland. We propose an agent-based simulation pipeline that considers population changes as well as different business strategies of the car sharing service, and we demonstrate its successful application for simulating scenarios for 2030. To imitate car sharing user behavior, we develop a data-driven mode choice model. Our analysis reveals important differences in the examined scenarios, such as higher vehicle utilization rates for a reduced fleet size as well as in a scenario featuring new car sharing stations. These disparities translate into variations in the power flexibility of the fleet available for ancillary services, ranging from 12 to 50 MW, depending on the scenario and the time of the day. Furthermore, we conduct a case study involving a subset of the car sharing fleet, incorporating real-world electricity pricing data. The case study substantiates the existence of a sweet spot involving monetary gains for both power grid operators and fleet owners. Our findings provide guidelines to decision makers and underscore the pressing need for regulatory enhancements concerning power trading within the realm of car sharing.
National-scale bi-directional EV fleet control for ancillary service provision
Nespoli, Lorenzo; Wiedemann, Nina; Suel, Esra; et al. (2023)
Energy Informatics
Deploying real-time control on large-scale fleets of electric vehicles (EVs) is becoming pivotal as the share of EVs over internal combustion engine vehicles increases. In this paper, we present a Vehicle-to-Grid (V2G) algorithm to simultaneously schedule thousands of EVs charging and discharging operations, that can be used to provide ancillary services. To achieve scalability, the monolithic problem is decomposed using the alternating direction method of multipliers (ADMM). Furthermore, we propose a method to handle bilinear constraints of the original problem inside the ADMM iterations, which changes the problem class from Mixed-Integer Quadratic Program (MIQP) to Quadratic Program (QP), allowing for a substantial computational speed up. We test the algorithm using real data from the largest carsharing company in Switzerland and show how our formulation can be used to retrieve flexibility boundaries for the EV fleet. Our work thus enables fleet operators to make informed bids on ancillary services provision, thereby facilitating the integration of electric vehicles.
Influence of tracking duration on the privacy of individual mobility graphs
Martin, Henry; Wiedemann, Nina; Suel, Esra; et al. (2022)
Proceedings: 17th International Conference on Location Based Services (LBS2022)
Location graphs are a compact representation of individual mobility that can be used as a mobility profile to personalize location-based services. While location graphs are more privacy-preserving than raw tracking data, it was shown that there is still a considerable risk for users to be re-identified by their mobility graph topology. However, it is unclear how this risk depends on the tracking duration. Here, we consider a scenario where the attacker wants to match new tracking data of a user to a pool of previously recorded mobility profiles, and we analyze the dependence of the re-identification performance on the tracking duration. For our experiment, we use a one-year long tracking dataset of 137 users divided into subsets of varying durations (4, 8, 16, 20, 24, and 28 weeks). We find that the re- identification performance increases with growing pool- and test-user tracking duration, and even the smallest tested duration allows to match users significantly better than random. The provided evidence for a tracking duration dependency of user privacy has clear implications for the data collection and storage strategies. It is advised for data collectors to limit the tracking duration or to reset user IDs regularly when storing long-term tracking data.
A Deep Learning Approach for Meter-Scale Air Quality Estimation in Urban Environments Using Very High-Spatial-Resolution Satellite Imagery
Sorek-Hamer, Meytar; von Pohle, Michael; Sahasrabhojanee, Adwait; et al. (2022)
Atmosphere
High-spatial-resolution air quality (AQ) mapping is important for identifying pollution sources to facilitate local action. Some of the most populated cities in the world are not equipped with the infrastructure required to monitor AQ levels on the ground and must rely on other sources, such as satellite derived estimates, to monitor AQ. Current satellite-data-based models provide AQ mapping on a kilometer scale at best. In this study, we focus on producing hundred-meter-scale AQ maps for urban environments in developed cities. We examined the feasibility of an image-based object-detection analysis approach using very high-spatial-resolution (2.5 m) commercial satellite imagery. We fed the satellite imagery to a deep neural network (DNN) to learn the association between visual urban features and air pollutants. The developed model, which solely uses satellite imagery, was tested and evaluated using both ground monitoring observations and land-use regression modeled PM2.5 and NO2 concentrations over London, Vancouver (BC), Los Angeles, and New York City. The results demonstrate a low error with a total RMSE < 2 µg/m3 and highlight the contribution of specific urban features, such as green areas and roads, to continuous hundred-meter-scale AQ estimations. This approach offers promise for scaling to global applications in developed and developing urban environments. Further analysis on domain transferability will enable application of a parsimonious model based merely on satellite images to create hundred-meter-scale AQ maps in developing cities, where current and historical ground data are limited.
Street-view greenspace exposure and objective sleep characteristics among children
Jimenez, Marcia P.; Suel, Esra; Rifas-Shiman, Sheryl L.; et al. (2022)
Environmental Research
Greenspace may benefit sleep by enhancing physical activity, reducing stress or air pollution exposure. Studies on greenspace and children's sleep are limited, and most use satellite-derived measures that do not capture ground-level exposures that may be important for sleep. We examined associations of street view imagery (SVI)-based greenspace with sleep in Project Viva, a Massachusetts pre-birth cohort. We used deep learning algorithms to derive novel metrics of greenspace (e.g., %trees, %grass) from SVI within 250m of participant residential addresses during 2007–2010 (mid-childhood, mean age 7.9 years) and 2012–2016 (early adolescence, 13.2y) (N = 533). In early adolescence, participants completed >5 days of wrist actigraphy. Sleep duration, efficiency, and time awake after sleep onset (WASO) were derived from actigraph data. We used linear regression to examine cross-sectional and prospective associations of mid-childhood and early adolescence greenspace exposure with early adolescence sleep, adjusting for confounders. We compared associations with satellite-based greenspace (Normalized Difference Vegetation Index, NDVI). In unadjusted models, mid-childhood SVI-based total greenspace and %trees (per interquartile range) were associated with longer sleep duration at early adolescence (9.4 min/day; 95%CI:3.2,15.7; 8.1; 95%CI:1.7,14.6 respectively). However, in fully adjusted models, only the association between %grass at mid-childhood and WASO was observed (4.1; 95%CI:0.2,7.9). No associations were observed between greenspace and sleep efficiency, nor in cross-sectional early adolescence models. The association between greenspace and sleep differed by racial and socioeconomic subgroups. For example, among Black participants, higher NDVI was associated with better sleep, in neighborhoods with low socio-economic status (SES), higher %grass was associated with worse sleep, and in neighborhoods with high SES, higher total greenspace and %grass were associated with better sleep time. SVI metrics may have the potential to identify specific features of greenspace that affect sleep.
Large-scale slum mapping in sub-Saharan Africa's major cities: Remote sensing and deep learning reveal strong slum growth in the urban periphery between 2016 and 2022
Büttner, Nicolas; Stalder, Steven; Volpi, Michele; et al. (2025)
Habitat International
Around half of sub-Saharan Africa's urban population lives in slums, yet data on the spatiotemporal development of slums remains scarce, impeding policies to alleviate urban poverty and inequality. We propose a solution to this problem by applying deep learning to open-access satellite imagery to map slums in 529 major cities across sub-Saharan Africa and track their spatiotemporal development. Our model produced 10m resolution ‘slum probability maps’ allowing timely and cost-effective tracking of slum growth. On this basis, we estimated that in 2022 the share of the urban population living in slums exceeded 50% in 274 cities, and in 84% of cities this share increased between 2016 and 2022, most severely in Middle and West Africa. Slum growth occurred primarily in the urban periphery, which tends to be missed in survey-based slum monitoring.
Self-supervised learning unveils urban change from street-level images
Stalder, Steven; Volpi, Michele; Büttner, Nicolas; et al. (2024)
Computers, Environment and Urban Systems
Cities around the world are grappling with multiple interconnected challenges, including population growth, shortage of affordable and decent housing, and the need for neighborhood improvements. Despite its critical importance for policy, our ability to effectively monitor and track urban change remains limited. Deep learning-based computer vision methods applied to street-level images have been successful in the measurement of socioeconomic and environmental inequalities but did not fully utilize temporal images to track urban change, as time-varying labels are often unavailable. We used self-supervised methods to measure change in London using 15 million street images taken between 2008 and 2021. Our novel adaptation of Barlow Twins, Street2Vec, embeds urban structure while being invariant to seasonal and daily changes without manual annotations. It outperformed generic pretrained embeddings, successfully identified point-level change in London's housing supply from street-level images, and distinguished between major and minor change. This capability can provide timely information for urban planning and policy decisions towards more liveable, equitable, and sustainable cities.
Characterisation of urban environment and activity across space and time using street images and deep learning in Accra
Nathvani, Ricky; Clark, Sierra N.; Muller, Emily; et al. (2022)
Scientific Reports
The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy.
Graph Convolutional Neural Networks for Human Activity Purpose Imputation
Martin, Henry; Bucher, Dominik; Suel, Esra; et al. (2018)
NIPS 2018 Spatiotemporal Workshop
Automatic location tracking of people has recently become a viable source for mobility and movement data. Such data are used in a wide range of applications, from city and transport planning to individual recommendations and schedule optimization. For many of these uses, it is of high interest to know why a person visited at a given location at a certain point in time. We use multiple personalized graphs to model human mobility behavior and to embed a large variety of spatio-temporal information and structure in the graphs' weights and connections. Taking these graphs as input for graph convolutional neural networks (GCNs) allows us to build models that can exploit the structural information inherent in human mobility. We use GPS travel survey data to build person specific mobility graphs and use GCNs to predict the purpose of a user's visit at a certain location. Our results show that GCNs are suitable to exploit the structure embedded in the mobility graphs.

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