Article

enviroCar: A Citizen Science Platform for Analyzing and Mapping Crowd-Sourced Car Sensor Data

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Abstract

This article presents the enviroCar platform for collecting geographic data acquired from automobile sensors and openly providing those data for further processing and analysis. By plugging a low-cost On-Board Diagnostics (OBD-II) adapter into a car and using an Android smartphone, various kinds of sensor data measured by today's cars can be collected and uploaded on to the Web. Once available on the Web, these data can be used to monitor traffic and related environmental parameters. We analyse the OBD-II interface and its potential usage for environmental monitoring, e.g. to estimate fuel consumption and resulting emissions, noise emission, and standing times. Next, we present the main contribution of this article, the system design of the enviroCar platform. This system design consists of the enviroCar app and the enviroCar server, which allows for flexible geoprocessing of the uploaded data. We focus in this article on the description of the spatiotemporal RESTful Web Service interface and underlying data model specifically designed for handling the mobile sensor data. Finally, we present application scenarios in which the enviroCar platform can act as a powerful tool, e.g. regarding traffic monitoring and smarter cities (e.g. the detection of pollutant emission hotspots in the city), or towards applications for a quantified self (e.g. monitoring fuel consumption). We started the enviroCar project in 2013 and have been able to attract a growing number of participants since then. In a crowd-funding initiative, enviroCar was successfully funded by volunteers, demonstrating the interest in this platform.

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... Für umweltbezogene Analysen werden bevorzugt xFCD herangezogen, um neben den räumlichzeitlichen Informationen zusätzliche Daten von Sensoren und Fahrzeugen für beispielsweise die Beobachtung des Kraftstoffverbrauchs zu erhalten (Jakobsen et al. 2013). Bröring et al. (2015), Röger et al. (2018), Häußler et al. (2018) sowie Röger & Krisp (2019) benutzten ebenfalls xFCD für ihre umweltbezogenen Arbeiten. ...
... Diese xFCD können für die Analyse räumlicher und umweltbezogener Fragestellungen wie beispielsweise CO 2 -Emissionen des Verkehrs genutzt werden (Häußler et al. 2018;Röger et al. 2018). Bröring et al. (2015) stellen in ihrer Arbeit ebenso den Nutzen und die Verwendungsmöglichkeiten dieser Daten dar. Sie eignen sich zur Überwachung verschiedener Parameter mit Umweltauswirkung, z. ...
... Auch wenn die Berechnungsformeln konkrete Dezibelwerte ausgeben, wird aufgrund von Unsicherheiten in den Daten sowie zahlreichen Annahmen auf absolute Werte verzichtet. Auch die Methode der Lärmanalyse aus xFCD von Bröring et al. (2015) stellt keine absoluten Dezibelwerte bereit, sondern zeigt relative Emissionswerte und Trends an. Dabei werden ebenso die Drehzahl des Motors und die Geschwindigkeit des Fahrzeugs verwendet und statistisch ausgewertet. ...
Article
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Zusammenfassung Erweiterte Floating-Car-Daten (xFCD) bieten die Möglichkeit, Fahrzeuglärm aus den Daten zur Position, Geschwindigkeit und der Drehzahl des Motors eines Fahrzeugs zu ermitteln und die Ergebnisse u. a. visuell auszuwerten. Dazu werden in einer exemplarischen Fallstudie im Raum Mönchengladbach Daten des enviroCar-Projekts verwendet. Die Lärmkalkulation enthält die separate Berechnung von Rollgeräuschen und Antriebsgeräuschen der Fahrzeuge sowie deren energetische Addition zum Gesamtgeräusch. Zusätzlich sind das anzunehmende Alter der Straßen, die Temperatur und eventuell gefallener Niederschlag berücksichtigt. Die Resultate der räumlichen Lärmanalyse aus diesen xFCD werden mit „klassischen“ Lärmkarten verglichen. Häufig lassen sich die gleichen Lärmschwerpunkte wie z. B. größere Straßen oder Kreuzungen erkennen, auch wenn die Herangehensweisen zur Erstellung der Lärmkarten andere sind. Zudem zeigen wir exemplarisch Möglichkeiten, theoretisch vollständig elektrifizierten Verkehr mit Verkehr durch Verbrennerfahrzeuge hinsichtlich der Lärmemissionen zu vergleichen. Diese Studie zeigt, dass xFCD für die Analyse von Verkehrslärm erfolgreich genutzt werden können.
... The 52 North Initiative for Geospatial Open Source Software [37] proposed a platform named EnviroCar for collecting geographic data and vehicles' sensors. The EnviroCar is an open platform for Citizen Science projects, which aims to provide sustainable mobility, traffic planning and share the findings from the industry when collecting and analyzing car data. ...
... The system consists of the EnviroCar app and the EnviroCar server. Bröring et al. [37] described the spatiotemporal RESTful Web Service interface and the designed data model. Since 2015, there are over 500,000 measurement data points collected and these numbers are continuously growing. ...
... Bröring et al. [37] developed an app (EnviroCar) for Android smartphones to collect the location of vehicles and upload it to the Web. ...
Article
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Over the years, governments and automakers launched initiatives to improve road traffic efficiency, safety, and people mobility. They have been working on various aspects of Intelligent Transportation Systems (ITSs), which aim to improve decision-making, availability of information and communication technologies to provide applications and services to boost the transportation systems. The development of new applications and services for ITS depends on the availability of different datasources, what it is not the current case. Many studies focus on the communication issues of applications and their associated challenges. To reveal the recent vehicular data use, we examined the most remarkable studies of the last few years, which describe services and applications for ITS, however with a focus on the data employed by them. We introduce the concept of Vehicular Data Space (VDS), which is then used to describe the vehicular scenario from the perspective of data. Moreover, we outline a taxonomy, according to the different data sources. We also categorize the applications, highlighting the data each one used in their approach. Finally, we present some challenges and open issues related to the process of forData Creation, Data Preparation, Data processing, and data Use. In a nutshell, this work constitutes one of the first holistic surveys on services and applications for ITSs, focusing on the data used by them, as well as their future challenges.
... Following this (Sec. 5), two examples of this framework applied to common time geographic analysis tasks are presented, using space-time data on vehicle emissions from the EnviroCar platform [14]. We conclude (Sec. ...
... EnviroCar is a community-based data collection platform for gathering vehicleborne sensor data and producing environmental information [14]. EnviroCar uses standard Bluetooth OBD-II adapters 6 , which are connected to a vehicle via the standard OBD connection that allows it to read parameters such as speed or revolutions per minute. ...
... Basemap data, imagery, and map information provided by MapQuest, OpenStreetMap and contributors, ODbL. Trajectories data provided by EnviroCar [14], ODbL. ...
Conference Paper
As location-aware applications and location-based services continue to increase in popularity, data sources describing a range of dynamic processes occurring in near real-time over multiple spatial and temporal scales are becoming the norm. At the same time, existing frameworks useful for understanding these dynamic spatio-temporal data, such as time geography, are unable to scale to the high volume, velocity, and variety of these emerging data sources. In this paper, we introduce a computational framework that turns time geography into a scalable analysis tool that can handle large and rapidly changing datasets. The Hierarchical Prism Tree (HPT) is a dynamic data structure for fast queries on spatio-temporal objects based on time geographic principles and theories, which takes advantage of recent advances in moving object databases and computer graphics. We demonstrate the utility of our proposed HPT using two common time geography tasks (finding similar trajectories and mapping potential space-time interactions), taking advantage of open data on space-time vehicle emissions from the EnviroCar platform.
... Thus, for the training and testing of the ML models, we used data from a naturalistic driving dataset extracted from enviroCar, a citizen science platform for collecting pseudonymized information from ordinary drivers in several European countries [40]. EnviroCar data are collected through standard Bluetooth OBD-II adapters to the vehicle's Controller Area Network (CAN) bus. ...
... Our analysis considered 8726 gasoline tracks for a total of 983,291 measurements that were recorded mostly in Germany in the period 1 January 2012-15 June 2016. We focused on gasoline engines, since the estimation of FC (measured in liters/h) by enviroCar provides the best accuracy for these kinds of engines [40]. We worked with data recorded in different driving environments and not calibrated for a specific car model in order to target a certain degree of robustness. ...
... 8726 gasoline tracks for a total of 983,291 measurements that were recorded mostly in Germany in the period 1 January 2012-15 June 2016. We focused on gasoline engines, since the estimation of FC (measured in liters/h) by enviroCar provides the best accuracy for these kinds of engines [40]. We worked with data recorded in different driving environments and not calibrated for a specific car model in order to target a certain degree of robustness. ...
Article
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Internet of Things technologies are spurring new types of instructional games, namely reality-enhanced serious games (RESGs), that support training directly in the field. This paper investigates a key feature of RESGs, i.e., user performance evaluation using real data, and studies an application of RESGs for promoting fuel-efficient driving, using fuel consumption as an indicator of driver performance. In particular, we propose a reference model for supporting a novel smart sensing dataflow involving the combination of two modules, based on machine learning, to be employed in RESGs in parallel and in real-time. The first module concerns quantitative performance assessment, while the second one targets verbal recommendation. For the assessment module, we compared the performance of three well-established machine learning algorithms: support vector regression, random forest and artificial neural networks. The experiments show that random forest achieves a slightly better performance assessment correlation than the others but requires a higher inference time. The instant recommendation module, implemented using fuzzy logic, triggers advice when inefficient driving patterns are detected. The dataflow has been tested with data from the enviroCar public dataset, exploiting on board diagnostic II (OBD II) standard vehicular interface information. The data covers various driving environments and vehicle models, which makes the system robust for real-world conditions. The results show the feasibility and effectiveness of the proposed approach, attaining a high estimation correlation (R2 = 0.99, with random forest) and punctual verbal feedback to the driver. An important word of caution concerns users’ privacy, as the modules rely on sensitive personal data, and provide information that by no means should be misused.
... In recent years, multiple open source platforms and citizen science projects have been developed that not only promote but also provide different ready to use hardware and software solutions ( [31][32][33][34]). These solutions have not only been used as prototypes but have also been applied in research and commercial applications such as for example senseBox [35] or the Air Quality Egg [36]. ...
... However, once this model gets used on a statistically significant area the results begin to become accurate. This effect within parametric building modelling as well as a comparison with data driven models has been performed by [31]. This study indicates the best way to use our model, on larger areas that have statistical significance in the total urban energy demand. ...
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The usage of greenery systems as nature-based solutions to assist in urban cooling in summer time as well as urban warming in wintertime is considered a scientific validated approach in urban planning. The objective of this research is the investigation and quantification of the role of green roofs and green facade solutions concerning thermal behavior in buildings energy savings by using standardized semantic city models that allow the quantification of such measures on district and city scales. The implemented model uses standardized geospatial data based on the CityGML format, a semantic city model standard, for analysis and data storage. For storage of the thermal properties of the buildings, the behavior of its occupants as well as the sensor measurements the Energy ADE of the CityGML standard was used. A green roof/façades model was implemented to simulate the heat transfer in a building based on the heat balance principle of foliage, soil, and structural layers. This model allows analyzing the thermal influence of plant and substrate layers on the heat gains from incoming solar radiation into buildings and the heat losses. This implementation was validated for cooling solutions using monitoring data from real-time experiments during summer measurements at three locations in Germany. Results from this experiment correspond well with the findings of other relevant studies. A sensitivity analysis was conducted to test the impacts of climate, substrate and plants on the greenery layer performance.
... The project showed how a grassroot sensing network could reduce costs dramatically, and also engage citizens in environmental monitoring and regulation. Bröring et al. [17] used the built-in diagnostic interface of cars (OBD-II) to obtain sensor data used to estimate current fuel consumption, CO 2 emission, noise, standing time and slow moving traffic. Citizens can also act as sensors themselves, by reporting what they observe. ...
... The van can be driven to locations where measurements are wanted. As mentioned before, Bröring et al. demonstrated the use of the OBD-II [17] interface connector to collect information from the car itself (velocity, fuel consumption). We have not found anyone pursuing the idea of using parked cars as sensor platforms. ...
Article
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In this article, we present a research design for smart city initiatives, based on the argument that there is a connection between smart cities and the concepts of “smart buildings” and “smart users”. Smart cities refer to “places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems”. Smart buildings refer to ICT-enabled and networked constructions such as traffic cameras and lights, buildings and other manmade structures. With inexpensive hardware such as the Raspberry Pi, Intel Edison, Arduino, NodeMCU and their ecosystems of sensors, we can equip these structures with sensors. Smart users refer to the high level of education in developed societies, allowing us to utilize technology such as smart phones to create better cities. Citizens can provide data through their smart phones, and these data can, together with sensor data from buildings, be used to analyze and visualize a range of different variables aimed at creating smarter cities. We propose that a first step of smart city research should be a thorough process of identifying and collecting input from relevant stakeholders in order to find the most relevant objectives for research. Finally, we present case evidence from a pilot study that has followed our approach, which has now received funding for further development.
... The project showed how a grassroot sensing network could reduce costs dramatically, and also engage citizens in environmental monitoring and regulation. Bröring et al. [17] used the built-in diagnostic interface of cars (OBD-II) to obtain sensor data used to estimate current fuel consumption, CO 2 emission, noise, standing time and slow moving traffic. ...
... As mentioned before, Bröring et al. demonstrated the use of the OBD-II [17] interface connector to collect information from the car itself (velocity, fuel consumption). ...
Article
Full-text available
In this article, we present a research design for smart city initiatives, based on the argument that there is a connection between smart cities and the concepts of “smart buildings” and “smart users”. Smart cities refer to “places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems”. Smart buildings refer to ICT-enabled and networked constructions such as traffic cameras and lights, buildings and other manmade structures. With inexpensive hardware such as the Raspberry Pi, Intel Edison, Arduino, NodeMCU and their ecosystems of sensors, we can equip these structures with sensors. Smart users refer to the high level of education in developed societies, allowing us to utilize technology such as smart phones to create better cities. Citizens can provide data through their smart phones, and these data can, together with sensor data from buildings, be used to analyze and visualize a range of different variables aimed at creating smarter cities. We propose that a first step of smart city research should be a thorough process of identifying and collecting input from relevant stakeholders in order to find the most relevant objectives for research. Finally, we present case evidence from a pilot study that has followed our approach, which has now received funding for further development.
... Citizen Science refers to a scientific project which is led by researchers and where citizens participate in the collection of data [9]. To this end, researchers make use of a platform which is used to collect data by citizens regarding various phenomena in natural contexts, such as bird watching [10,11]. Citizen Science project delivers sustainable outcomes. ...
... The data quality issue is crucial for citizen science since reliable data is not often accomplished. To address this issue, citizen science projects employ accurate technologies [10] or stringent protocol. Bear in mind that the employment of stringent protocol shall be presented with a user-friendly way. ...
Chapter
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Citizen Science refers to a scientific project, managed by researchers, where volunteers are involved in studying and acquiring data related to a natural phenomenon. To this end, researchers design a platform to support this process. A successful Citizen Science project delivers important sustainable outcomes because it contributes to understanding and addressing environmental issues. For an effective Citizen Science project, researchers manage crucial actions: the management of volunteers’ motivation and engagement and the development of a Citizen Science platform. With this study, I contribute to the literature discussing these three topics proposing recommendations to address these actions, based on a literature review. The study has certain implications for practitioners and researchers.
... Commercial products are available and provide driving behaviour, fuel consumption, or CO2 analysis. For example, Bröring et al. (2015) read fuel consumption, engine speed, and vehicle speed from the CAN bus to monitor pollution and noise generation. Van Geem et al. (2016) read wheel speeds and acceleration for pothole detection. ...
Conference Paper
Modern vehicles are equipped with dozens of sensors collecting data for supporting automotive trends like Autonomous and/or Connected Driving. For road operators, in-vehicle data or so-called Extended Floating Car Data (XFCD) are often promised as the Holy Grail for supporting efficient road operations. In this work, we tackle the question of benefits with respect to potential in-vehicle sensor data usage from a road operator's point of view, focusing on traffic information, traffic management and road maintenance. Key results are amongst others: (1) the need for customized XFCD solutions supporting road operator's tasks, (2) standardized data access and formats for in-vehicle sensor data, (3) cost-effective integration of XFCD into existing monitoring infrastructure, and (4) the need to actively promote road operator's interests.
... This development is dynamic, and the uncertainty in crowdsourced CWS data much depends on the parameter of interest and the network density, but confidence increases that robust parameter estimates can be derived for spatial averages or climatology. Other crowdsourcing sensors of high interest are cars (Bröring et al., 2015;Bonczak and Kontokosta, 2019) and smartphones, which have been used to observe air pressure (Mass and Madaus, 2014;de Vos et al., 2020) and air temperature (Overeem et al., 2013;Droste et al., 2017a), amongst others (see previous section). ...
Article
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The scientific field of urban climatology has long investigated the two-way interactions between cities and their overlying atmosphere through in-situ observations and climate simulations at various scales. Novel research directions now emerge through recent advancements in sensing and communication technologies, algorithms, and data sources. Coupled with rapid growth in computing power, those advancements augment traditional urban climate methods and provide unprecedented insights into urban atmospheric states and dynamics. The emerging field introduced and discussed here as Urban Climate Informatics (UCI) takes on a multidisciplinary approach to urban climate analyses by synthesizing two established domains: urban climate and climate informatics. UCI is a rapidly evolving field that takes advantage of four technological trends to answer contemporary climate challenges in cities: advances in sensors, improved digital infrastructure (e.g., cloud computing), novel data sources (e.g., crowdsourced or big data), and leading-edge analytical algorithms and platforms (e.g., machine learning, deep learning). This paper outlines the history and development of UCI, reviews recent technological and methodological advances, and highlights various applications that benefit from novel UCI methods and datasets.
... Estas características fomentaram cada vez mais o surgimento de aplicativos e serviços para o seguimento au- tomobilístico. O aplicativo foi desenvolvido na plataforma Android da Google e com banco de dados embarcado SQLite[9,10]. Para o desenvolvimento foi utilizada a metodologia orientada a objetoss visando o reaproveitamento e a manutenção do código fonte. A arquitetura do DriverRating é mostrada na Figura 2.WebMedia'2018, October 2018, Salvador, BA BrazilRemoved for double-blind reviewFigura 2: Arquitetura do aplicativo DriverRating. ...
... EnviroCar 7 is a community-based data collection platform for gathering vehicle-borne sensor data and producing environmental information [54]. EnviroCar uses standard Bluetooth OBD-II adaptors, 8 which are connected to a vehicle via the standard OBD connection that allows it to read parameters such as speed or revolutions per minute. ...
Article
Full-text available
The Web of Data is a rapidly growing collection of datasets from a wide range of domains, many of which have spatial–temporal aspects. Hägerstrand’s time geography has proven useful for thinking about and understanding the movements and spatial–temporal constraints of humans. In this paper, we explore time geography as a means of querying and integrating multiple spatial–temporal data sources. We formalize the concept of the space–time prism as an ontology design pattern to use as a framework for understanding and representing constraints and interactions between entities in space and time. We build on a formalization of space–time prisms and apply it in the context of the Web of Data, making it usable across multiple domains and topics. We demonstrate the utility of this approach through two use cases from the domains of environmental monitoring and cultural heritage, showing how space–time prisms enable spatial–temporal and semantic reasoning directly on distributed data sources.
... The data used in the test is originally a GPS track of a car driving through Manhattan and Queens, New York City, which was collected by the enviroCar project (Broering et al. 2015). The uncertainty variable used for the visualization in this test is the accuracy of the GPS signal, i.e., positional uncertainty. ...
Chapter
Effectively communicating the uncertainty that is inherent in any kind of geographic information remains a challenge. This paper investigates the efficacy of animation as a visual variable to represent positional uncertainty in a web mapping context. More specifically, two different kinds of animation (a ‘bouncing’ and a ‘rubberband’ effect) have been compared to two static visual variables (symbol size and transparency), as well as different combinations of those variables in an online experiment with 163 participants. The participants’ task was to identify the most and least uncertain point objects in a series of web maps. The results indicate that the use of animation to represent uncertainty imposes a learning step on the participants, which is reflected in longer response times. However, once the participants got used to the animations, they were both more consistent and slightly faster in solving the tasks, especially when the animation was combined with a second visual variable. According to the test results, animation is also particularly well suited to represent positional uncertainty, as more participants interpreted the animated visualizations correctly, compared to the static visualizations using symbol size and transparency. Somewhat contradictory to those results, the participants showed a clear preference for those static visualizations.
... Examples in the literature that mention this term are Sansone et al. (2002) and Huber et al. (1999). xFCD appears as a solution to model not only movement information, but as well air exposures as in the case of EnviroCar (Bröring et al. 2015). ...
Thesis
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Vehicle traffic in urban environments consists of a variation of traffic phenomena. Defining and measuring these traffic phenomena is challenging, since traffic sensors can still not observe the traffic situation of one city entirely over a period of time. One possibility to get general overviews is analyzing data coming from tracked vehicle movements. In the best cases, tracked vehicles are numerous and part of vehicle fleets that represent a big proportion of traffic participants in the investigation area. Traffic data in the form of movement trajectories is producible via the Floating Car Data (FCD) technology, which uses mobile devices that allow positioning and recording on-board information in every tracked vehicle. In case of operating taxis, these devices are part of already installed dispatcher systems and are able to produce Floating Taxi Data (FTD). One type of applications with FCD and FTD consists of inferring traffic situations with numerous different computational techniques. This thesis introduces a traffic pattern analysis framework for FCD with the emphasis on detecting specific vehicle traffic patterns. The extracted patterns should define urban traffic congestion as the detectable traffic phenomenon, which is the focus of this work. In general, tracking numerous moving entities participating in traffic is part of a large body of ongoing research. By reviewing traditional traffic data acquisition techniques from different domains, this work aims to provide a connection to various research disciplines connected with research on moving objects. Those fields are coming from physics, computer science, GIScience and geography to mention a few. In contrast to traffic phenomena on highways, which are well studied, this work focus on urban traffic in highly populated cities with dense transportation infrastructure. By selecting, modifying, and applying various methodological aspects, this work shows the establishment of a traffic pattern analysis framework that allows extracting typical periodical and unusual traffic patterns for each day of the week. Traffic congestion can be seen as a daily event, since it has starting and end points, that occurs on specific rush hours of the day, but as well as traffic anomalies that are caused by different events in the urban environment. The distinguishing between different types of traffic congestion events is challenging, especially when relying on classified movement patterns from FCD, which is only a fraction of all traffic participants. The first step is to clarify the various terminologies and to associate them with respective formalizations of each appearance, as the terms road capacity and traffic bottlenecks. Additionally, there are different aspects of traffic congestion detection, which includes reasoning on FCD representations, preprocessing and analytical possibilities. The last mentioned include map matching on road segments and density-based clustering of vehicle movement. Preceding steps of the framework consist of adjusted preprocessing of the data. The following six framework techniques aim to reveal specific traffic patterns from the preprocessed FCD by different forms of representing urban traffic congestion events. The underlying computational methods of the framework enable the possibility to apply various computations as a sequence that reveal an increasing number of details on urban traffic congestion events. The results of the framework computations include mainly three different products that are subsequently inferable: congestion polygons, congestion propagation polylines (CPP) and bundles of associated road segments. The affected road segments result from previous matching between road segments and congestion polygons, or congestion propagation polylines. The evaluation of the framework outcomes consists of visual analysis methods. A test FTD set from taxis in Shanghai from 2007 serves for the framework evaluation. The results show selected parts of the urban investigation area influenced by recurrent and non-recurrent traffic congestion, which conclude to expected travel time variations during rush hours. Afterwards, the test results serve for extensive discussions on the usefulness and reasonability of the framework methods. A concluding outlook outlines ideas on future work, which mainly consists of proposed methodical extensions and finding suitable applications for the traffic pattern analysis framework.
... The algorithm estimates the full state of the traffic flow by using statistical inference methods and being able to reconstruct a 3D version of the traffic at a particular date and hour on the particular routes, the main focus of the application being the highway traffic. EnviroCar [3] is a platform composed of a mobile application and a Web application which, using an On-Board Diagnosis adapter for cars, gathers car and road data like carbon dioxide emissions, noise emissions and when cars are stationary, which are automatically recorded in the mobile application. In order for the user to gather data, he needs to attach the On-Board Diagnosis adapter to his car, install the enviroCar application on his mobile device and let the adapter transmit the data. ...
Article
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Traffic optimization is a subject that has become vital for the world we live in. People these days need to get from a starting point to a destination point as fast and as safe as possible. Traffic congestion plays a key role in the frustration of people and it results in lost time, reduced productivity and wasted resources. In our study we seek to address these issues by proposing a real-time road traffic planning system based on mobile context and crowdsourcing efforts. The first step toward this goal is real-time traffic characterization using data collected from mobile sensors of drivers, pedestrians, cyclists, passengers, etc.. We started developing a data collection and analysis system composed of a mobile application in order to collect user context data and a Web application to view and analyze the data. This new system will eventually give the users an automatically optimized route to the destination and predict the users’ traveling route based on live traffic conditions and historical data.
... Methods and tools are provided to capture information about both, indoor and outdoor air quality. • enviroCar is a Citizen Science platform for analysing and mapping crowd-sourced car sensor data [32]. The research project proposes an innovative approach towards the monitoring of car-related air pollution. ...
Article
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Although data is increasingly shared online and accessible for re-use, we still witness heterogeneous coverage of thematic areas and geographic regions. This especially becomes an issue when data is needed for large territories and including different nations, as, for example, required to support macro-regional development policies. Once identified, data gaps might be closed using different approaches. Existing—but so far non accessible—data might be made available; new public sector information could be gathered; or data might be acquired from the private sector. Our work explores a fourth option: closing data gaps with direct contributions from citizen (Citizen Science). This work summarizes a particular case study that was conducted in 2016 in the Danube Region. We provide a gap analysis over an existing macro-regional data infrastructure, and examine potential Citizen Science approaches that might help to close these gaps. We highlight already existing Citizen Science projects that could address a large part of the identified gaps, and suggest one particular new application in order to indicate how a—so far uncovered—gap might be approached. This new application addresses bioenergy as a particular field of the circular economy. On this basis we discuss the emerging opportunities and challenges for this particular way of public participation in regional development policy. We close by highlighting areas for future research.
... As implementações estão disponíveis em um repositório 2 . Os testes foram executados utilizando trajetórias da base de dados Enviro-Car (Bröring et al., 2015). A escolha desta base de dados foi devido a sua disponibilidade e no fato de que possui todas as características requeridas para a implementação dos algoritmos (posição, heading, velocidade, aceleração e precisão do GPS). ...
Conference Paper
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This paper presents a comparative study of online map-matching algorithms used in the prepro-cessing of vehicle's trajectories. Three state-of-the-art algorithms, based on Computational Intelligence, were compared in several scenarios. A methodology of comparison was proposed with the aim of evaluating the performance of the algorithms in real world scenarios with different levels of uncertainty. The results show that, in general, the simpler algorithms present better results in real world scenarios. The complex methods present high variance, being more efficient for trajectories with larger error.
... Benché questi fenomeni abbiano luogo largamente al di fuori della geografia, come si è già osservato, la maggioranza di questi dati sono per natura georeferenziati, ossia associati ad un preciso luogo geografico e archiviati in associazione a quel luogo, o addirittura si tratta di dati spazio-temporali in tempo reale (Richardson, 2013). In risposta alla disponibilità crescente di dati (big data) e a fenomeni collegati, quali l'informazione geografica fornita volontariamente (volunteered geographic information o VGI) (Mohammadi and Malek, 2015), l'emergere di un social sensing come complemento al più tradizionale remote sensing (Liu et al., 2015), si sono andate sviluppando nuove metodologie numeriche, come l'attività estrattiva applicata ai dati (geografici o non) (data mining) (Li et al., 2015), e si sono persino coniati i termini computational social science e digital humanities (Bröring et al., 2015;Panteras et al., 2015;Wu et al., 2014), mentre l'enorme mole di dati spaziali numerici è stata vista come strumento per la calcolabilità dello spazio, presupposto per la sua governabilità (Rose-Redwood, 2012). Ormai da alcuni anni la comunità che si riconosce nella pratica e nello studio dell'informazione geografica (GI) ha cominciato a rispondere all'ondata di dati spaziali cogliendone le opportunità analitiche ed affinando i propri strumenti tecnici così da far fronte alla valanga di big data (Mao, 2014). ...
Article
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The recent evolution of quantitative geography: Which perspectives for geography? - The quantitative geography of today is profoundly different from the geography that emerged from the old quantitative revolution. Modern quantitative geography is a geography that can acknowledge its own strengths as well as its own weaknesses. By way of a long path, it has come to comprehend the relativity of spatial relationships: it has abandoned the pretense that laws are universal and that quantitative relationships - empirically derived from data measured on a portion of space - have general validity. As a result, modern quantitative geography can provide effective tools to address the questions posed by our society and environment; it can provide reliable answers, yet appreciate the limited and contingent value of those answers. Today the ubiquity of georeferenced data leads to questions that geography must confront, presenting it with an unprecedented opportunity for visibility, influence, and relevance. Modern quantitative geography can embrace this opportunity, accepting its challenges to methods, ethics, and epistemology. The quantitative geography of today is prepared to address these questions because it is not alone: on its path, it has learned to engage in a constructive dialogue with other geographies, and to take advantage of their contributions. Ultimately, georeferenced data alone cannot boost geography; the data are a sign of a changing world, one where quantitative geography is better positioned to address questions that, nonetheless, cannot be exhausted within numbers. The true challenge is to identify a path where quantitative geography, in concert with all other geographies, can embrace the opportunities and demands expressed by today's world.
... Bröring et al. [8] presented the enviroCar platform, which aims to acquire vehicle sensors' data and provide free access to such data, thus enabling traffic monitoring and environment analysis through the Internet. Given the importance of sensors to a vehicle's operation, new vehicle models embed many high-quality sensors to get more reliable and diverse information about themselves. ...
Conference Paper
In this work, we propose Traffic Data EnrichmentSensor (TraDES), towards a low-cost traffic sensor for IntelligentTransportation System (ITS) based on heterogeneous data fusion.TraDES aims at fusing data from vehicular traces with road traffic data to enrich current spatiotemporal traffic data. In that direction, we propose a robust methodology to group spatially and temporally these different data sources, producing a vehicular trace with its respective traffic conditions, which is given as input to a learning-based model based on Artificial Neural Networks (ANN). Hence, TraDES is an enriched traffic sensor that is able to sense (detect) traffic conditions using a scalable and low-cost approach and to increase the spatiotemporal traffic data coverage.
... FCD ist eine relative neue Technologie, Daten aus einer Vielzahl von beobachteten GNSSbestückten Fahrzeugen zu sammeln. Auf Grundlage von FCD-Sätzen ist es möglich Straßensegmente abzuleiten (LI et al. 2015a), Pendlerbewegungsmuster zu untersuchen (DEWULF et al. 2015), typische periodische Verkehrsflussmuster in Teilen des Straßennetzes zu detektieren (KÖRNER 2011) und durch Fahrzeuge verursachte Emissionen zu modellieren (GÜHNE- MANN et al. 2004, BRÖRING et al. 2015 ...
Chapter
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Tägliche Mobilität in hochbesiedelten urbanen Regionen der Welt ist auf gut funktionierende effektive Straßennetzwerke angewiesen. Verkehrsengpässe sind jedoch typisch für urbane Regionen mit periodischen Verkehrsstauereignissen. Diese Arbeit hat zum Ziel zu untersuchen, wie Stau mit komplizierten Kreuzungen zusammenhängt. Als erstes wird ein Ansatz gewählt, um anhand von Geodaten aus dem OpenStreetMap-Projekt (OSM) die Komplexität von Straßensegmenten zu klassifizieren und komplexe Kreuzungen abzuleiten. In einem zweiten Schritt wird Verkehrsstau anhand Floating Taxi Data (FTD) von Shanghaier Taxis aus dem Jahre 2007 berechnet. Anschließend wird eine Polygonverschneidungstechnik entwickelt, um Stau mit komplizierten Kreuzungen zu verknüpfen. Hierbei wird das Konzept definiert, Verkehrsengpässe als Polygone darzustellen. Die abgeleiteten Verkehrsengpässe indizieren Standorte, dessen Verkehrsinfrastruktur komplex ist und die periodisch von Stau beeinflusst sind. In einem letzten Schritt werden kartographische Darstellungen für die Visualisierung von Verkehrsengpässen gewählt. Dies hat zur Absicht mögliche thematische Verkehrskarten zu ergänzen.
... We used data collected from the enviroCar project [3,6], which is a public project that encourages users to collect, share and analyze data generated by sensors embedded on vehicles or on smartphones used to run the project's application. The enviroCar dataset contains more than 16,000 trips collected around the world by volunteer drivers. ...
Conference Paper
In this work, we apply information theory metrics to car trips logged by volunteers around the world and use quantifiers such as location entropy to reveal aspects of users' mobility, like the context in which trips happened. The dataset used in this work was collected from the enviroCar project and contains not only location logs but also sensor readings associated with each location. Information theory measurements can also reveal relationships between sensor measurements in order to reveal rare occurrences and reduce uncertainty. This work shows that it is possible to differentiate driving contexts and capture relationships among sensors using location entropy and mutual information, respectively. These contributions pave the way for developing new features that may ultimately improve traffic context classification results.
... In a citizen science approach the enviroCar platform 1 encourages people to upload GPS movement data together with sensory data from the car's CAN bus. Bröring et al. (2015) analyse the enviroCar FCD and estimate the CO 2 emissions in a road network using both the GPS movement data and the cars' engine data. Similarly, study the temporal progression of fuel efficiency along one of Beijing's major roads. ...
Article
Urban road traffic is highly dynamic. Traffic conditions vary in time and with location and so do the movement patterns of individual road users. In this article, a movement pattern is the behaviour of a car when traversing a road link in an urban road network. A movement pattern can be recorded with a global navigation satellite system (GNSS), such as the Global Positioning System (GPS). A movement pattern has a specific energy-efficiency, which is a measure of how fuel-intensively the car is moving. For example, a car driving uniformly at medium speed consumes little fuel and, therefore, is energy-efficient, whereas stop-and-go driving consumes much fuel and is energy-inefficient. In this article we introduce a model to estimate the energy-efficiency of movement patterns in urban road traffic from GNSS data. First, we derived statistical features about the car's movement along the road. Then, we compared these to fuel consumption data from the car's controller area network (CAN) bus, normalized to the car's overall range of fuel consumption. We identified the optimal feature set for prediction. With the optimal feature set we trained, tested and verified a model to estimate energy-efficiency, with the fuel consumption serving as ground truth. Existing fuel consumption models usually view movement as a snapshot. Thus, the behaviour of the car remains unknown that causes a movement pattern to be energy-efficient or energy-inefficient. Our model views movement as a process and allows to interpret this process. A movement pattern can, for example, be energy-inefficient because the car is driving in stop-and-go traffic, because it is travelling at high speed, or because it is accelerating. Our model allows to distinguish between these different types of behaviours. Thus, it can provide new insights into the dynamics of urban road traffic and its energy-efficiency.
... Bröring et al. [15], used the built-in diagnostic interface of cars (OBD-II) to obtain sensor data used to estimate current fuel consumption, CO 2 emission, noise, standing time and slow moving traffic. ...
Conference Paper
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In this paper, we present a proposal for a research design, based on the argument that there is a connection between smart cities and the concepts of "smart buildings" and "smart users". Smart cities refer to "places where information technology is combined with infrastructure, architecture, everyday objects, and even our bodies to address social, economic, and environmental problems". Smart buildings refer to ICT-enabled and networked constructions such as traffic cameras and lights, buildings and other man-made structures. With inexpensive hardware such as the Raspberry Pi, Edison, Arduino, and their ecosystems of sensors, we can equip these structures with sensors. Smart users refer to the high level of education in western societies, which allows us to utilize technology such as smart phones to create better cities. Citizens can provide data through their smart phones, and these data can, together with sensor data from buildings, be used to analyze and visualize a range of different variables aimed at creating smarter cities. We propose that a first step of smart city research should be a thorough process of identifying and collecting input from relevant stakeholders in order to find the most relevant objectives for research.
... Palma et al. (2008)). As an example, the enviroCar platform is an open platform for sustainable mobility where users are able to connect their mobile phones to sensors in a car, to collect the sensor data while driving, and to share the collected data sets with other users (Broering et al. 2015). Several analysis tools are currently being developed in this context. ...
... For the second dataset, Floating Car Data emerges from the enviroCar project (Bröring et al. 2015). It features a citizen science platform, where people can upload xFCD produced by their own cars and download it using an API. ...
Article
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We present an approach to use static traffic count data to find relatively representative areas within Floating Car Data (FCD) datasets. We perform a case study within the state of Nordrhein-Westfalen, Germany using enviroCar FCD and traffic count data obtained from Inductive Loop Detectors (ILD). Findings indicate that our approach combining FCD and traffic count data is capable of assessing suitable subsets within FCD datasets that contain a relatively high ratio of FCD records and ILD readings. We face challenges concerning the correct choice of traffic count data, counting individual FCD trajectories and defining a threshold by which an area can be considered as representative.
... This system was implemented for Brazil and gave promising results and achievements in real-time flood monitoring. Bröring et al. (2015) designed an Android program for smartphones, called "enviroCar platform", which is able to acquire car movement data and send them to a server. This platform is able to use this information for traffic monitoring. ...
Article
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Air pollution is a significant concern for both managers and disaster decision-makers in megacities. Considering the importance of having access to correct and up to date spatial data, it goes without saying that designing and implementing an environmental alerting and monitoring system is quite necessary. A standard infrastructure is needed to utilize sensor observations so as to be ready in case of critical conditions. The use of sensor web is regarded a fundamental solution to control and manage air quality in megacities. The proposed system uses the SWE framework of OGC, the reference authority in spatial data, to integrate both sensors and their observations, while utilizing them in the spatial data infrastructure. The developed system provides the capability to collect, transfer, share, and process the sensor observations, calculate the air quality condition, and report real-time critical conditions. For this purpose, a four-tier architectural structure, including sensor, web service, logical, and presentation layer, has been designed. Using defined routines and subsystems, the system applies web sensor data to a set of web services to produce alerting information. The developed system, which is assessed through sensor observation, measures the concentration of carbon monoxide, ozone, and sulfur dioxide in 20 stations in Tehran. In this way, the real-time air quality index is calculated, and critical conditions are sent through email to those users, who have been registered in the system. In addition, interpolation maps of the observations along with time diagrams of sensors' observations can be obtained through a series of processes, carried out by the process service.
... Palma et al. (2008)). As an example, the enviroCar platform is an open platform for sustainable mobility where users are able to connect their mobile phones to sensors in a car, to collect the sensor data while driving, and to share the collected data sets with other users (Broering et al. 2015). Several analysis tools are currently being developed in this context. ...
Article
Maintaining knowledge about the provenance of datasets, that is, about how they were obtained, is crucial for their further use. Contrary to what the overused metaphors of ‘data mining’ and ‘big data’ are implying, it is hardly possible to use data in a meaningful way if information about sources and types of conversions is discarded in the process of data gathering. A generative model of spatiotemporal information could not only help automating the description of derivation processes but also assessing the scope of a dataset’s future use by exploring possible transformations. Even though there are technical approaches to document data provenance, models for describing how spatiotemporal data are generated are still missing. To fill this gap, we introduce an algebra that models data generation and describes how datasets are derived, in terms of types of reference systems. We illustrate its versatility by applying it to a number of derivation scenarios, ranging from field aggregation to trajectory generation, and discuss its potential for retrieval, analysis support systems, as well as for assessing the space of meaningful computations.
... Environmental sensing is not limited to the observation of changing weather conditions. Other applications include, but are not limited to, automatic temperature and humidity control with anomaly detection in smart buildings [84], early detection of collisions between pedestrians, cyclists and drivers to generate timely alerts on mobile devices [97], data collection from in-car sensors to predict generated noise, travel time and fuel consumption [98], and overseeing the food production process in the agri-food sector to reduce CO 2 emission levels and energy consumption levels [99]. The applications discussed are summarised in Table 1. ...
Article
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Mining ubiquitous sensing data is important but also challenging, due to many factors, such as heterogeneous large-scale data that is often at various levels of abstraction. This also relates particularly to the important aspects of the explainability and interpretability of the applied models and their results, and thus ultimately to the outcome of the data mining process. With this, in general, the inclusion of domain knowledge leading towards semantic data mining approaches is an emerging and important research direction. This article aims to survey relevant works in these areas, focusing on semantic data mining approaches and methods, but also on selected applications of ubiquitous sensing in some of the most prominent current application areas. Here, we consider in particular: (1) environmental sensing; (2) ubiquitous sensing in industrial applications of artificial intelligence; and (3) social sensing relating to human interactions and the respective individual and collective behaviors. We discuss these in detail and conclude with a summary of this emerging field of research. In addition, we provide an outlook on future directions for semantic data mining in ubiquitous sensing contexts.
... Point events appear in many application domains, such as law enforcement (Johansson et al., 2015;Malik, Maciejewski, Towers, McCullough, & Ebert, 2014), zoology (Sarkar et al., 2015;Travaini et al., 2007), epidemiology (Bizimana & Nduwayezu, 2021;Lukasczyk et al., 2015), food service (Zhang et al., 2021), internet applications (Guan, Cheng, Song, & Wu, 2014), and environmental science (Bröring et al., 2015). Researchers and practitioners are often interested in regions with a relatively high number of observed events. ...
Article
This article describes a scalar field topology (SFT)‐based methodology for the interactive characterization and analysis of hotspots for density fields defined on a regular grid. In contrast to the common approach of simply identifying hotspots as areas that exceed a chosen density threshold, SFT provides various data abstractions—such as the merge tree and the Morse complex—to characterize hotspots and their boundaries at multiple scales. Moreover, SFT enables the ranking of hotspots based on analyst‐defined importance measures, which also makes it possible to explore hotspots using a level‐of‐detail approach. We present a visual analytics system to support analysts in hotspot analysis and abstraction using SFT, and we demonstrate the merit of the proposed SFT‐based methodology on two crime datasets.
... FCD are used to derive real-time traffic information from the dynamics of single cars [6]. Moreover, FCD are an important data source for eco-routing [7] and help to detect emission hotspots in cities [8]. ...
Article
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Floating car data (FCD) recorded with the Global Positioning System (GPS) are an important data source for traffic research. However, FCD are subject to error, which can relate either to the accuracy of the recordings (measurement error) or to the temporal rate at which the data are sampled (interpolation error). Both errors affect movement parameters derived from the FCD, such as speed or direction, and consequently influence conclusions drawn about the movement. In this paper we combined recent findings about the autocorrelation of GPS measurement error and well-established findings from random walk theory to analyse a set of real-world FCD. First, we showed that the measurement error in the FCD was affected by positive autocorrelation. We explained why this is a quality measure of the data. Second, we evaluated four metrics to assess the influence of interpolation error. We found that interpolation error strongly affects the correct interpretation of the car's dynamics (speed, direction), whereas its impact on the path (travelled distance, spatial location) was moderate. Based on these results we gave recommendations for recording of FCD using the GPS. Our recommendations only concern time-based sampling, change-based, location-based or event-based sampling are not discussed. The sampling approach minimizes the effects of error on movement parameters while avoiding the collection of redundant information. This is crucial for obtaining reliable results from FCD.
... For example, consider sensors operating in energy restrained environments and adopt opportunistic sensing techniques, or event-based sensing [2]. Volunteered Geographic Information Systems which enable individuals [11,13] or cars [6] as data providers, fall in the same case. In these situations, it is impossible for the client to make any kind of estimate on the response size, and devise a strategy to reduce accordingly the spatiotemporal boundaries of their query. ...
Conference Paper
This work contributes towards extending OGC Sensor Observation Service to become ready for Internet of Things, i.e. can be employed by devices with limited capabilities or opportunistic internet connection. We present an extension based on progressive data transmission, which by-design facilitates selective data harvesting and disruption-tolerant communication. The extension economizes resources, while respects the SOS specification requirement that the client should have no a-priori knowledge of the server capabilities. Empirical experiments in two case studies demonstrate that the extension adds little overhead and may lead to significant performance improvements in certain cases, as for irregular timeseries. Also, the proposed extension is not invasive and backwards compatible with legacy clients.
Article
Traffic and transportation are ongoing digitalisation. Travellers always carry smartphones everywhere they go. Smartphone-based crowdsensing can be used to collect and aggregate traffic information for services that contribute to smoother and more sustainable transportation and traffic - but only if the business model is profitable in the long-term. We analyse two existing crowdsensing services in traffic and transportation context (Waze, Moovit) and one being developed (TrafficSense) using findings from business model (two-sided markets; data use), crowdsensing (technical overview, participant incentives), and transportation (efficiency, sustainable urban transportation) literature. Waze may alleviate traffic congestion by helping its millions of users to avoid traffic jams. Moovit makes public transport more attractive by making it easier and smoother to use for travellers. TrafficSense service is developed in a research project. It uses crowdsensing to learn regular, multimodal routes of travellers. The information can be used to predict the general traffic and congestion levels based on the predicted intents of the crowd of travellers. Our contribution is to combine distinct but complementary viewpoints from two-sided markets, business models, crowdsensing, and transportation research to analyse the potential business and sustainability impacts of the emerging crowdsensing-based smart transportation services.
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Citizen science allows mobility researchers and mobility planners to collect data at a larger scale and at lower costs compared to traditional data collection methods. Citizen observatories (COs) are especially interesting because public authorities are often involved in the setup and the participation barrier for citizens is reduced by relying on smartphones. Citizen science and COs both foster public participation in scientific research. An evaluation framework can increase the quality of and the trust in citizen science data. Although considered important by existing evaluation frameworks for citizen science, detailed instructions on how to evaluate data accuracy are lacking. This is especially the case in domains where citizen science has not been widely used, such as mobility. This paper therefore presents a framework to evaluate the data quality of a CO for mobility. The framework includes representativeness, accuracy, reliability, and validity. The framework is demonstrated by applying it to a CO in Ghent, Belgium, in which cyclists used their smartphones to collect their perceived and objective waiting times at signalised intersections. Although the representativeness of the data could be improved, the data was found to be accurate. Data showed that participants on average spent 4% of their trip time waiting at signalised intersections. Furthermore, no clear overestimation or underestimation of the waiting time was found. The main limitation of the evaluation framework is the focus on data quality, which is only one aspect of citizen science.
Chapter
Crowd-funding is used by business and social entrepreneurs to fund projects that impact society in many ways. Although crowd-funders fund and support entrepreneurial projects, stakeholders are less aware of crowd-funder motivations and behaviours towards products/services, which impact on the success of these projects. The purpose of this research, therefore, is two-fold: a) development of crowd-funder value framework for understanding crowd-funder motivations; and b) provision of robust theoretical basis to the construct of 'crowd-funding'. Different social constructs, such as social identity, and symbolic, crowd and relational social capital related to crowd-funding are discussed based on symbolic convergence theory of communication and social identity theory. This framework will act as a roadmap to understand how crowd-funder motivations impact and create value for individuals, organisations and society, and inform how social, environmental and economic value and impact can be maximised through crowd-funding initiatives.
Chapter
Interest in citizen science and the number of related projects have increased considerably during the last decade. Citizen science revolves around gathering data and using it. This means, that data storing is a vital part of any citizen science project and can affect the success or failure. Many researches focus on the citizen side, while the data side is often left out. This study aims to fill the gap by trying to find the current data storing practices in the field of citizen science. A systematic literature review was conducted and multiple similarities in data storing and management techniques were identified between different citizen science projects. Results show that most projects used a traditional relational database to store data, a separate web interface to add, use, modify, and access the data, and data validation was left to users by having them vote on existing data. Data models always considered the data provider (citizen) but left out the end user in their design. In the future, the results will be compared to ongoing citizen science project and see if it is possible to improve the efficiency and overall quality of citizen science databases.
Chapter
Reality-enhanced serious games (RESGs) incorporate data from the real world to enact training in the wild. This – with the proper cautions due to safety - can be done also for daily activities, such as driving. We have developed two modules that may be integrated as field user performance evaluators in third-party RESGs, aimed at improving driver’s fuel efficiency. They exploit vehicular signals (throttle position, engine revolutions per minute and car speed), which are easily accessible through the common On-Board Diagnostics-II (OBD-II) interface. The first module detects inefficient and risky driving manoeuvres while driving, in order to suggest improvement actions based upon fuzzy rules, derived from analyzing naturalistic driving data. The second module provides an eco-driving categorization for a drive via two indicators, fuel efficiency and throttle position values. The estimation of fuel efficiency for the whole trip relies on the mentioned signals, plus the OBD-II calculated engine load. Data from ‘enviroCar’ project’s, a naturalistic driving archive, was used in a simulation. The results are promising in terms of accuracy and encourage further steps towards more effective modules to support a better driving performance, for RESGs.
Chapter
Crowd-funding is used by business and social entrepreneurs to fund projects that impact society in many ways. Although crowd-funders fund and support entrepreneurial projects, stakeholders are less aware of crowd-funder motivations and behaviours towards products/services, which impact on the success of these projects. The purpose of this research, therefore, is two-fold: a) development of crowd-funder value framework for understanding crowd-funder motivations; and b) provision of robust theoretical basis to the construct of ‘crowd-funding'. Different social constructs, such as social identity, and symbolic, crowd and relational social capital related to crowd-funding are discussed based on symbolic convergence theory of communication and social identity theory. This framework will act as a roadmap to understand how crowd-funder motivations impact and create value for individuals, organisations and society, and inform how social, environmental and economic value and impact can be maximised through crowd-funding initiatives.
Chapter
The purpose of this study is to analyze the Role of Higher Education Institutions on the E-Innovation of Oman. It was found that there are several conditions for the expansion of the contribution of universities to regional development of E-Innovation system. These conditions are predominantly related to a broad set of factors that relate to characteristics of HEIs, characteristics of the regional firms, aspects of the collaborative relationship, and characteristics of environmental context in which HEIs and firms are embedded.
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This chapter aims to predict the foreign currency exchange rate over twenty-two different currencies based on the US dollar. This chapter proposes three machine learning algorithms, such as ridge regression, lasso regression, decision tree, and a deep learning algorithm named Bi-directional Long Short-Term Memory (Bi-LSTM) to predict the foreign currency exchange rate. Technical analysis of foreign currency exchange is also discussed in this chapter. The authors use mean absolute error (MAE), mean square error (MSE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) to measure the performance of the algorithms. Empirical findings indicate that overall performance of all the algorithms is satisfactory, but Bi-LSTM performs better than others. This study is beneficial for the stakeholders in setting a range of strategies for the foreign exchange market.
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Long-term community resilience, which privileges a long view look at chronic issues influencing communities, has begun to draw more attention from city planners, researchers and policymakers. In Phoenix, resilience to heat is both a necessity and a way of life. In this paper, we attempt to understand how residents living in Phoenix experience and behave in an extreme heat environment. To achieve this goal, we introduced a smartphone application (ActivityLog) to study spatio-temporal dynamics of human interaction with urban environments. Compared with traditional paper activity log results we have in this study, the smartphone-based activity log has higher data quality in terms of total number of logs, response rates, accuracy, and connection with GPS and temperature sensors. The research results show that low-income residents in Phoenix mostly stay home during the summer but experience a relatively high indoor temperature due to the lack/low efficiency of air-conditioning (AC) equipment or lack of funds to run AC frequently. Middle-class residents have a better living experience in Phoenix with better mobility with automobiles and good quality of AC. The research results help us better understand user behaviors for daily log activities and how human activities interact with the urban thermal environment, informing further planning policy development. The ActivityLog smartphone application is also presented as an open-source prototype to design a similar urban climate citizen science program in the future.
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Urbanization is an increasing global trend resulting in a strong increase in public and individual transportation needs. Accordingly, a major challenge for traffic and urban planners is the design of sustainable mobility concepts to maintain and increase the long-term health of humans by reducing environmental pollution. Recent developments in sensor technology allow the precise tracking of vehicle sensor information, allowing a closer and more in-depth analysis of traffic data. We propose a visual analytics system for the exploration of environmental factors in these high-resolution and high-dimensional mobility sensor data. Additionally, we introduce an interactive visual logging approach to enable experts to cope with complex interactive analysis processes and the problem of the reproducibility of results. The usefulness of our approach is demonstrated via two expert studies with two domain experts from the field of environment-related projects and urban traffic planning.
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With the increased availability of user generated data, assessing the quality and credibility of such data becomes important. In this article, we propose to assess the location correctness of visually generated Volunteered Geographic Information (VGI) as a quality reference measure. The location correctness is determined by checking the visibility of the point of interest from the position of the visually generated VGI (observer point); as an example we utilize Flickr photographs. Therefore we first collect all Flickr photographs that conform to a certain point of interest through their textual labelling. Then we conduct a reverse viewshed analysis for the point of interest to determine if it lies within the area of visibility of the observer points. If the point of interest lies outside the visibility of a given observer point, the respective geotagged image is considered to be incorrectly geotagged. In this way, we analyze sample datasets of photographs and make observations regarding the dependency of certain user/photo metadata and (in)correct geotags and labels. In future the dependency relationship between the location correctness and user/photo metadata can be used to automatically infer user credibility. In other words, attributes such as profile completeness, together with location correctness, can serve as a weighted score to assess credibility.
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Geographic Information Science and business are facing a new challenge: understanding and exploiting data and services emerging from online communities. In the emerging technologies of the social web, GI user roles switched from being data consumers to become data producers, the challenge we argue is in making this generated GI usable. As a use case we point to the increasing demands for up-to-date geographic information combined with the high cost of maintenance which present serious challenges to data providers. In this paper we argue that the social web combined with social network science present a unique opportunity to achieve the goal of reducing the cost of maintenance and update of geospatial data and providing a platform for bottom up approaches to GI. We propose to focus on web-based trust as a proxy measure for quality and to study its spatio-temporal dimensions. We also point to work on combining folksonomies with ontologies, allowing for alternative models of metadata and semantics as components of our proposed vision.
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In this paper we present a new approach to monitor noise pollution involving citizens and built upon the notions of participatory sensing and citizen science. We enable citizens to measure their personal exposure to noise in their everyday environment by using GPS-equipped mobile phones as noise sensors. The geo-localised measures and user-generated meta-data can be automatically sent and shared online with the public to contribute to the collective noise mapping of cities. Our prototype, called NoiseTube, can be found online.
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This paper focuses on an overview of kernel density estimation especially for what concerns the choice of bandwidth and intensity parameters according to local conditions. A case study inherent seismic risk analysis of the old town centre of Potenza hilltop town has been discussed, with particular attention to the evaluation of the possible local amplifying factors. This first integrated application of kernel density maps to analyse seismic damage scenarios with a geostatistical approach allowed to evaluate the local geological, geomorphological and 1857 earthquake macroseismic data, offering a new point of view of civil protection planning. The aim of geostatistical approach is to know seismic risk variability at local level, modelling and visualizing it.
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Many sensor networks have been deployed to monitor Earth's environment, and more will follow in the future. Environmental sensors have improved continuously by becoming smaller, cheaper, and more intelligent. Due to the large number of sensor manufacturers and differing accompanying protocols, integrating diverse sensors into observation systems is not straightforward. A coherent infrastructure is needed to treat sensors in an interoperable, platform-independent and uniform way. The concept of the Sensor Web reflects such a kind of infrastructure for sharing, finding, and accessing sensors and their data across different applications. It hides the heterogeneous sensor hardware and communication protocols from the applications built on top of it. The Sensor Web Enablement initiative of the Open Geospatial Consortium standardizes web service interfaces and data encodings which can be used as building blocks for a Sensor Web. This article illustrates and analyzes the recent developments of the new generation of the Sensor Web Enablement specification framework. Further, we relate the Sensor Web to other emerging concepts such as the Web of Things and point out challenges and resulting future work topics for research on Sensor Web Enablement.
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Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the true values from the radar observations. The study is carried out for the 3300 km2 Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown, that often a few classes for rainfall observation are enough for satisfactory areal rainfall estimation. The findings of the study suggest also a revisiting of the rain gauge network optimisation problem.
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Optimal spatial assessment of short-time step precipitation for hydrological modelling is still an important research question considering the poor observation networks for high time resolution data. The main objective of this paper is to present a new approach for rainfall observation. The idea is to consider motorcars as moving rain gauges with windscreen wipers as sensors to detect precipitation. This idea is easily technically feasible if the cars are provided with GPS and a small memory chip for recording the coordinates, car speed and wiper frequency. This study explores theoretically the benefits of such an approach. For that a valid relationship between wiper speed and rainfall rate considering uncertainty was assumed here. A simple traffic model is applied to generate motorcars on roads in a river basin. Radar data are used as reference truth rainfall fields. Rainfall from these fields is sampled with a conventional rain gauge network and with several dynamic networks consisting of moving motorcars. Those observed point rainfall data from the different networks are then used to calculate areal rainfall for different scales. Ordinary kriging and indicator kriging are applied for interpolation of the point data with the latter considering uncertain rainfall observation by cars e.g. according to a discrete number of windscreen wiper operation classes. The results are compared with the true values from the radar observations. The study is carried out for the 3300 km2 Bode river basin located in the Harz Mountains in Northern Germany. The results show, that the idea is theoretically feasible. Only a small portion of the cars needed to be equipped with sensors for sufficient areal rainfall estimation. Regarding the required sensitivity of the potential rain sensors in cars it could be shown, that often a few classes for rainfall observation are enough for satisfactory areal rainfall estimation. The findings of the study suggest also a revisiting of the rain gauge network optimisation problem.
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Owing to their rapidly increasing numbers and very limited use of emission control technologies, motor vehicles are emerging as the largest source of urban air pollution in the developing world. Without timely and effective measures to mitigate the adverse impacts of motor vehicle use, the living environment in the cities of the developing world will continue to deteriorate and become increasingly unbearable. This handbook presents a state-of-the-art review of vehicle emission standards and testing procedures and attempts to synthesize worldwide experience with vehicle emission control technologies and their applications in both industrialized and developing countries.
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The SOAP web services stack (SOAP, WSDL, WS-*) described in the previous chapter delivers interoperability in both message integration and RPC style. With the rise of Web 2.0, new web frameworks such as Rails have emerged, and a new kind of web service has gained in popularity: the RESTful web service.
Article
In recent months there has been an explosion of interest in using the Web to create, assemble, and disseminate geographic information provided voluntarily by individuals. Sites such as Wikimapia and OpenStreetMap are empowering citizens to create a global patchwork of geographic information, while Google Earth and other virtual globes are encouraging volunteers to develop interesting applications using their own data. I review this phenomenon, and examine associated issues: what drives people to do this, how accurate are the results, will they threaten individual privacy, and how can they augment more conventional sources? I compare this new phenomenon to more traditional citizen science and the role of the amateur in geographic observation.
Conference Paper
CarTel is a mobile sensor computing system designed to collect, process, deliver, and visualize data from sensors lo- cated on mobile units such as automobiles. A CarTel node is a mobile embedded computer coupled to a set of sensors. Each node gathers and processes sensor readings locally be- fore delivering them to a central portal, where the data is stored in a database for further analysis and visualization. In the automotive context, a variety of on-board and external sensors collect data as users drive. CarTel provides a simple query-oriented programming in- terface, handles large amounts of heterogeneous data from sensors, and handles intermittent and variable network con- nectivity. CarTel nodes rely primarily on opportunistic wire- less (e.g., Wi-Fi, Bluetooth) connectivity—to the Internet, or to "data mules" such as other CarTel nodes, mobile phone flash memories, or USB keys—to communicate with the por- tal. CarTel applications run on the portal, using a delay- tolerant continuous query processor, ICEDB, to specify how the mobile nodes should summarize, filter, and dynamically prioritize data. The portal and the mobile nodes use a delay- tolerant network stack, CafNet, to communicate. CarTel has been deployed on six cars, running on a small scale in Boston and Seattle for over a year. It has been used to analyze commute times, analyze metropolitan Wi-Fi de- ployments, and for automotive diagnostics.
Book
Like many other incipient technologies, Web services are still surrounded by a substantial level of noise. This noise results from the always dangerous combination of wishful thinking on the part of research and industry and of a lack of clear understanding of how Web services came to be. On the one hand, multiple contradictory interpretations are created by the many attempts to realign existing technology and strategies with Web services. On the other hand, the emphasis on what could be done with Web services in the future often makes us lose track of what can be really done with Web services today and in the short term. These factors make it extremely difficult to get a coherent picture of what Web services are, what they contribute, and where they will be applied.Alonso and his co-authors deliberately take a step back. Based on their academic and industrial experience with middleware and enterprise application integration systems, they describe the fundamental concepts behind the notion of Web services and present them as the natural evolution of conventional middleware, necessary to meet the challenges of the Web and of B2B application integration. Rather than providing a reference guide or a "how to write your first Web service" kind of book, they discuss the main objectives of Web services, the challenges that must be faced to achieve them, and the opportunities that this novel technology provides. Established, as well as recently proposed, standards and techniques (e.g., WSDL, UDDI, SOAP, WS-Coordination, WS-Transactions, and BPEL), are then examined in the context of this discussion in order to emphasize their scope, benefits, and shortcomings. Thus, the book is ideally suited both for professionals considering the development of application integration solutions and for research and students interesting in understanding and contributing to the evolution of enterprise application technologies.
Article
Within the framework of Web 2.0 mapping applications, the most striking example of a geographical application is the OpenStreetMap (OSM) project. OSM aims to create a free digital map of the world and is implemented through the engagement of participants in a mode similar to software development in Open Source projects. The information is collected by many participants, collated on a central database, and distributed in multiple digital formats through the World Wide Web. This type of information was termed ‘Volunteered Geographical Information’ (VGI) by Goodchild, 2007. However, to date there has been no systematic analysis of the quality of VGI. This study aims to fill this gap by analysing OSM information. The examination focuses on analysis of its quality through a comparison with Ordnance Survey (OS) datasets. The analysis focuses on London and England, since OSM started in London in August 2004 and therefore the study of these geographies provides the best understanding of the achievements and difficulties of VGI. The analysis shows that OSM information can be fairly accurate: on average within about 6 m of the position recorded by the OS, and with approximately 80% overlap of motorway objects between the two datasets. In the space of four years, OSM has captured about 29% of the area of England, of which approximately 24% are digitised lines without a complete set of attributes. The paper concludes with a discussion of the implications of the findings to the study of VGI as well as suggesting future research directions.
Article
This paper outlines the issues involved in the problem of global warming. The road transport sector's contributions to this problem are then detailed and various policy options to reduce greenhouse gas emissions from private cars are discussed. The paper then describes a model which forecasts greenhouse gas emissions from cars. The effects of various policy options are then modelled and the results compared. Policies considered include: raising fuel prices in terms of the UK government's commitment to increase road fuel duties; subsidising public transport in terms of reduced public transport fares; and a tax differentiated by engine size.
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