A Classification Approach Using Machine Learning for Predicting Traffic Flows in Areas with Missing Sensors

in Transportation Research Board 101st Annual Meeting (2022)

The Impact of SARS-COVID-19 Outbreak on European Cities Urban Mobility

in Frontiers in Future Transportation (2021)

Mobility-Driven and Energy-Efficient Deployment of Edge Data Centers in Urban Environments

in IEEE Transactions on Sustainable Computing (2021)

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better support low-latency applications. In this paper ... [more ▼]

Multi-access Edge Computing (MEC) brings storage and computational capabilities at the edge of the network into so-called Edge Data Centers (EDCs) to better support low-latency applications. In this paper, we tackle the problem of EDC deployment in urban environments. Previous research on mobile phone data has exposed a strong correlation between the demand for mobile communications and the urban tissue. For example, joint analysis of mobile data and vehicle traffic can be extrapolated to estimate demand for transportation and human activities, thereby inferring the land use of the area where such activities take place. Our work takes into account the mobility of citizens and their spatial patterns to estimate the optimal placement of MEC EDCs in urban environments, in order to minimize outages while guaranteeing energy-efficiency. This is achieved by modeling both the energy consumption attributed to network components (e.g., base stations) and computing components (e.g., servers). We propose and compare three heuristics and show that mobility-aware deployments achieve superior performance. The results are obtained with a custom-designed simulator able to operate over large-scale realistic urban environments. [less ▲]

The CORONA Business in Modern Cities

Poster (2020, November)

As a response to the global outbreak of the SARS-COVID-19 pandemic, authorities have enforced a number of measures including social distancing, travel restrictions that lead to the “temporary” closure of ... [more ▼]

As a response to the global outbreak of the SARS-COVID-19 pandemic, authorities have enforced a number of measures including social distancing, travel restrictions that lead to the “temporary” closure of activities stemming from public services, schools, industry to local businesses. In this poster we draw the attention to the impact of such measures on urban environments and activities. For this, we use crowdsensed information available from datasets like Google Popular Times and Apple Maps to shed light on the changes undergone during the outbreak and the recovery [less ▲]

Inferring Urban Mobility and Habits from User Location History

in Transportation Research Procedia (2020, January), 47

Crowdsensed Data Learning-Driven Prediction of Local Businesses Attractiveness in Smart Cities

in IEEE Symposium on Computers and Communications (ISCC), Barcelona, Spain, 2019 (2019, July)

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven ... [more ▼]

Urban planning typically relies on experience-based solutions and traditional methodologies to face urbanization issues and investigate the complex dynamics of cities. Recently, novel data-driven approaches in urban computing have emerged for researchers and companies. They aim to address historical urbanization issues by exploiting sensing data gathered by mobile devices under the so-called mobile crowdsensing (MCS) paradigm. This work shows how to exploit sensing data to improve traditionally experience-based approaches for urban decisions. In particular, we apply widely known Machine Learning (ML) techniques to achieve highly accurate results in predicting categories of local businesses (LBs) (e.g., bars, restaurants), and their attractiveness in terms of classes of temporal demands (e.g., nightlife, business hours). The performance evaluation is conducted in Luxembourg city and the city of Munich with publicly available crowdsensed datasets. The results highlight that our approach does not only achieve high accuracy, but it also unveils important hidden features of the interaction of citizens and LBs. [less ▲]

High-Precision Design of Pedestrian Mobility for Smart City Simulators

in IEEE International Conference on Communications (ICC), Kansas City, MO, USA, 2018 (2018, May)

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality ... [more ▼]

The unprecedented growth of the population living in urban environments calls for a rational and sustainable urban development. Smart cities can fill this gap by providing the citizens with high-quality services through efficient use of Information and Communication Technology (ICT). To this end, active citizen participation with mobile crowdsensing (MCS) techniques is a becoming common practice. As MCS systems require wide participation, the development of large scale real testbeds is often not feasible and simulations are the only alternative solution. Modeling the urban environment with high precision is a key ingredient to obtain effective results. However, currently existing tools like OpenStreetMap (OSM) fail to provide sufficient levels of details. In this paper, we apply a procedure to augment the precision (AOP) of the graph describing the street network provided by OSM. Additionally, we compare different mobility models that are synthetic and based on a realistic dataset originated from a well known MCS data collection campaign (ParticipAct). For the dataset, we propose two arrival models that determine the users’ arrivals and match the experimental contact distribution. Finally, we assess the scalability of AOP for different cities, verify popular metrics for human mobility and the precision of different arrival models. [less ▲]