SMARTPHONE-BASED SENSING: LIFESTYLE AND MOBILITY DATA INTERPRETATION BY SMART CITIES

Intelligent Transportation Systems are an important enabler for the smart cities paradigm. Currently, such systems generate massive amounts of granular data that can be analyzed to better understand people’s dynamics. To address the multivariate nature of spatiotemporal urban mobility data, researchers and practitioners have developed an extensive body of research and interactive visualization tools. Data visualization provides multiple perspectives on data and supports the analytical tasks of domain experts. This article surveys related studies to analyze which topics of urban mobility were addressed and their related phenomena, and to identify the adopted visualization techniques and sensors data types. We highlight research opportunities based on our findings.

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QoS-Aware Approximate Query Processing for Smart Cities Spatial Data Streams

Sensors ◽

10.3390/s21124160 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4160

Author(s):

Isam Mashhour Al Jawarneh ◽

Paolo Bellavista ◽

Antonio Corradi ◽

Luca Foschini ◽

Rebecca Montanari

Keyword(s):

Data Streams ◽

Spatial Data ◽

Data Stream ◽

Smart Cities ◽

Stream Processing ◽

Processing System ◽

Strategic Decision ◽

Approximate Query Processing ◽

Data Stream Processing ◽

Mobility Data

Large amounts of georeferenced data streams arrive daily to stream processing systems. This is attributable to the overabundance of affordable IoT devices. In addition, interested practitioners desire to exploit Internet of Things (IoT) data streams for strategic decision-making purposes. However, mobility data are highly skewed and their arrival rates fluctuate. This nature poses an extra challenge on data stream processing systems, which are required in order to achieve pre-specified latency and accuracy goals. In this paper, we propose ApproxSSPS, which is a system for approximate processing of geo-referenced mobility data, at scale with quality of service guarantees. We focus on stateful aggregations (e.g., means, counts) and top-N queries. ApproxSSPS features a controller that interactively learns the latency statistics and calculates proper sampling rates to meet latency or/and accuracy targets. An overarching trait of ApproxSSPS is its ability to strike a plausible balance between latency and accuracy targets. We evaluate ApproxSSPS on Apache Spark Structured Streaming with real mobility data. We also compared ApproxSSPS against a state-of-the-art online adaptive processing system. Our extensive experiments prove that ApproxSSPS can fulfill latency and accuracy targets with varying sets of parameter configurations and load intensities (i.e., transient peaks in data loads versus slow arriving streams). Moreover, our results show that ApproxSSPS outperforms the baseline counterpart by significant magnitudes. In short, ApproxSSPS is a novel spatial data stream processing system that can deliver real accurate results in a timely manner, by dynamically specifying the limits on data samples.

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Unpacking a citizen self-tracking device: Smartness and idiocy in the accumulation of cycling mobility data

Environment and Planning D Society and Space ◽

10.1177/0263775817744781 ◽

2017 ◽

Vol 36 (2) ◽

pp. 294-312 ◽

Cited By ~ 10

Author(s):

Martín Tironi ◽

Matías Valderrama

Keyword(s):

Citizen Participation ◽

Urban Space ◽

Smart Cities ◽

Ways Of Knowing ◽

Smart Devices ◽

Government Officials ◽

Ordinary Experience ◽

Mobility Data ◽

Data Driven Decisions ◽

Tracking Device

Based on the Smart Cities imaginary, the bottom-up project Stgo2020 created a self-tracking device known as Rastreador Urbano de Bicicletas (or Urban Bicycle Tracker) to record the daily trips of cyclists in Santiago de Chile and use the data gathered to help government officials make better and data-driven decisions on cycling infrastructure planning. In this article, we examine the iterative design of this technology as well as its introduction into the everyday practices of cyclists. We argue that efforts to quantify the ordinary experience of cycling were overwhelmed and interrupted by an ecology of breakdowns, everyday contingencies, forgetfulness, and re-interpretations in the assemblage of devices, data, humans, and bicycles. These breakdowns generated incoherent or absurd bits of information that we call them as “idiotic data” based on recent conceptualizations of the character of the idiot. Significant displacements were provoked by these idiotic data, forcing the engineer behind the device to control and purify the sample by design and algorithms, waning the civic nature of the project at the same time. The case shows how new ways of knowing the urban space by smart devices should be not separated from the emergence of idiotic data, putting into question the versions of citizen participation and smartness at stakes.

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Getting Real: The Challenge of Building and Validating a Large-Scale Digital Twin of Barcelona’s Traffic with Empirical Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi11010024 ◽

2021 ◽

Vol 11 (1) ◽

pp. 24

Author(s):

Javier Argota Sánchez-Vaquerizo

Keyword(s):

Big Data ◽

Empirical Data ◽

Large Scale ◽

Smart Cities ◽

Digital Twin ◽

Fine Grained ◽

Mobility Data ◽

Digital Twins ◽

Alternative Scenarios ◽

Hourly Distribution

Large-scale microsimulations are increasingly resourceful tools for analysing in detail citywide effects and alternative scenarios of our policy decisions, approximating the ideal of ‘urban digital twins’. Yet, these models are costly and impractical, and there are surprisingly few published examples robustly validated with empirical data. This paper, therefore, presents a new large-scale agent-based traffic microsimulation for the Barcelona urban area using SUMO to show the possibilities and challenges of building these scenarios based on novel fine-grained empirical big data. It combines novel mobility data from real cell phone records with conventional surveys to calibrate the model comparing two different dynamic assignment methods for getting an operationally realistic and efficient simulation. Including through traffic and the use of a stochastic adaptive routing approach results in a larger 24-hour model closer to reality. Based on an extensive multi-scalar evaluation including traffic counts, hourly distribution of trips, and macroscopic metrics, this model expands and outperforms previous large-scale scenarios, which provides new operational opportunities in city co-creation and policy. The novelty of this work relies on the effective modelling approach using newly available data and the realistic robust evaluation. This allows the identification of the fundamental challenges of simulation to accurately capture real-world dynamical systems and to their predictive power at a large scale, even when fed by big data, as envisioned by the digital twin concept applied to smart cities.

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GPU UTILIZATION IN GEOPROCESSING BIG GEODATA: A REVIEW

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w3-2021-295-2022 ◽

2022 ◽

Vol XLVI-4/W3-2021 ◽

pp. 295-304

Author(s):

S. Saupi Teri ◽

I. A. Musliman ◽

A. Abdul Rahman

Keyword(s):

Remote Sensing ◽

Smart Cities ◽

Data Interpretation ◽

3D Modelling ◽

Graphics Hardware ◽

Future Trends ◽

Advancement Opportunities ◽

Gis Applications ◽

Gpu Processing ◽

Significant Data

Abstract. The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

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Mobility Trace Analysis for Intelligent Vehicular Networks

ACM Computing Surveys ◽

10.1145/3446679 ◽

2021 ◽

Vol 54 (3) ◽

pp. 1-38

Author(s):

Clayson Celes ◽

Azzedine Boukerche ◽

Antonio A. F. Loureiro

Keyword(s):

Large Volume ◽

Vehicular Networks ◽

Smart Cities ◽

Data Communication ◽

Transportation Systems ◽

Data Driven ◽

Mobility Data ◽

Spatiotemporal Information ◽

Research Problems ◽

Hidden Knowledge

Intelligent vehicular networks emerge as a promising technology to provide efficient data communication in transportation systems and smart cities. At the same time, the popularization of devices with attached sensors has allowed the obtaining of a large volume of data with spatiotemporal information from different entities. In this sense, we are faced with a large volume of vehicular mobility traces being recorded. Those traces provide unprecedented opportunities to understand the dynamics of vehicular mobility and provide data-driven solutions. In this article, we give an overview of the main publicly available vehicular mobility traces; then, we present the main issues for preprocessing these traces. Also, we present the methods used to characterize and model mobility data. Finally, we review existing proposals that apply the hidden knowledge extracted from the mobility trace for vehicular networks. This article provides a survey on studies that use vehicular mobility traces and provides a guideline for the proposition of data-driven solutions in the domain of vehicular networks. Moreover, we discuss open research problems and give some directions to undertake them.

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Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

Energies ◽

10.3390/en14113044 ◽

2021 ◽

Vol 14 (11) ◽

pp. 3044

Author(s):

Vitória Albuquerque ◽

Ana Oliveira ◽

Jorge Lourenço Barbosa ◽

Rui Simão Rodrigues ◽

Francisco Andrade ◽

...

Keyword(s):

Smart Cities ◽

Research Work ◽

Data Availability ◽

Data Driven ◽

City Management ◽

Cascading Effect ◽

Mobility Data ◽

Local Municipality ◽

Big Picture ◽

The City

Transportation data in a smart city environment is increasingly becoming available. This data availability allows building smart solutions that are viewed as meaningful by both city residents and city management authorities. Our research work was based on Lisbon mobility data available through the local municipality, where we integrated and cleaned different data sources and applied a CRISP-DM approach using Python. We focused on mobility problems and interdependence and cascading-effect solutions for the city of Lisbon. We developed data-driven approaches using artificial intelligence and visualization methods to understand traffic and accident problems, providing a big picture to competent authorities and supporting the city in being more prepared, adaptable, and responsive, and better able to recover from such events.

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Gravity Model of Passenger and Mobility Fleet Origin–Destination Patterns with Partially Observed Service Data

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/0361198121992074 ◽

2021 ◽

pp. 036119812199207

Author(s):

Brian Yueshuai He ◽

Joseph Y. J. Chow

Keyword(s):

Gravity Model ◽

Large Scale ◽

Smart Cities ◽

Transportation Network ◽

Real Data ◽

Small Scale ◽

Entropy Maximization ◽

Limited Mobility ◽

Mobility Data ◽

City Agencies

Mobility-as-a-service systems are becoming increasingly important in the context of smart cities, with challenges arising for public agencies to obtain data from private operators. Only limited mobility data are typically provided to city agencies, which are not enough to support their decision-making. This study proposed an entropy-maximizing gravity model to predict origin–destination patterns of both passenger and mobility fleets with only partial operator data. An iterative balancing algorithm was proposed to efficiently reach the entropy maximization state. With different trip length distributions data available, two calibration applications were discussed and validated with a small-scale numerical example. Tests were also conducted to verify the applicability of the proposed model and algorithm to large-scale real data from Chicago transportation network companies. Both shared-ride and single-ride trips were forecast based on the calibrated model, and the prediction of single-ride has a higher level of accuracy. The proposed solution and calibration algorithms are also efficient to handle large scenarios. Additional analyses were conducted for north and south sub-areas of Chicago and revealed different travel patterns in these two sub-areas.

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BIG MOBILITY DATA ANALYTICS FOR TRAFFIC MONITORING AND CONTROL

Facta Universitatis Series Automatic Control and Robotics ◽

10.22190/fuacr2002087s ◽

2020 ◽

Vol 19 (2) ◽

pp. 087

Author(s):

Natalija Stojanović ◽

Dragan Stojanović

Keyword(s):

Traffic Congestion ◽

Large Scale ◽

Smart Cities ◽

Heavy Traffic ◽

Traffic Monitoring ◽

Monitoring And Control ◽

Dynamic Configuration ◽

Large Cities ◽

Traffic Lights ◽

Mobility Data

With the overpopulation of large cities, the problems with citizens’ mobility, transport inefficiency, traffic congestions and environmental pollution caused by the heavy traffic require advanced ITS solutions to be overcome. Recent advances and wide proliferation of mobile and Internet of Things (IoT) devices, carried by people, built in vehicles and integrated in a road infrastructure, enable collection of large scale data related to mobility and traffic in smart cities, still with a limited use in real world applications. In this paper, we propose the traffic monitoring, control and adaptation platform, named TrafficSense, based on Big Mobility Data processing and analytics. It provides a continuous monitoring of a traffic situation and detection of important traffic parameters, conditions and events, such as travel times along the street segments and traffic congestions in real time. Upon detecting a traffic congestion on an intersection, the TrafficSense application leverages the feedback control loop mechanism to provide a traffic adaptation based on the dynamic configuration of traffic lights duration in order to increase the traffic flows in critical directions at the intersections. We tested and evaluated the developed application on the distributed cloud computing infrastructure. By varying the streaming workload and the cluster parameters we show the feasibility and applicability of our approach and the platform.

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