1,721,114 research outputs found
What are the potentialities of crowdsourcing for dynamic maps of on-street parking spaces?
No full textFinding a parking space is a crucial mobility problem, which could be mitigated by dynamic maps of parking availability. The creation of these maps requires current information on the state of the parking stalls, which could be obtained by (I) instrumenting the road infrastructure with sensors, (II) using probe vehicles, or (III) using mobile apps. In this paper, we investigate the potential predictive performances of a random forest binary classifier, comparing these three data collection strategies. As for the dataset, we used real infrastructure measurements in San Francisco for solution I. We simulated the crowdsourcing solutions II and III by downsampling that dataset, based on different assumptions. Evaluations show that the instrumented solution is clearly superior over the two crowdsourcing strategies, but with remarkably small differences to the probe vehicle scenario. On the other hand, a mobile app would require a very high penetration rate in order to be used for meaningful predictions
What Is the Impact of On-street Parking Information for Drivers?
No full textParking Guidance and Information (PGI) solutions are a well-known class of Intelligent Transportation Systems meant to support drivers by recommending locations and routes with higher chance to find parking. However, the relevance of such systems for on-street parking spaces is barely studied. In this paper, we investigate the consequences of providing the drivers with different parking information to the search. Based on real-world parking data from San Francisco, we investigated the scenario in which a driver does not find a parking space at the destination and has to decide on the next road to go. We consider three different scenarios: (I) No parking availability information; (II) static information about the capacity of a road segment and temporary parking limitations; (III) real-time information collected from stationary sensors. Clearly the latter has strong implications in terms of deployment and operational costs. These scenarios lead to three different guidance strategies for a PGI system. The empirical experiments we conducted on real on-street parking data from San Francisco show that there is a significant reduction of parking search with more informed strategies, and that the use of real-time information offers only a limited improvement over static one
GIS maps as powerful curriculum artefacts
Full text linkMaps have always been central to high quality geography education. Recent developments in web GIS have opened up new potential for teachers using GIS maps as powerful curriculum artefacts. Curriculum artefacts are resources that have signature meaning for teaching and learning. This paper argues that the use of GIS maps as curriculum artefacts can significantly enhance geography teaching and learning in schools. To illustrate this line of argument, a GIS curriculum artefact constructed in ESRI ArcGIS Online is critically evaluated using Maude’s typology of powerful geography knowledge as an analytical framework. The analysis identifies a number of educational benefits of using GIS maps as curriculum artefacts in school geography via a GeoCapabilities approach. The paper concludes with recommendations for the future geography curriculum development with GIS map artefacts in schools
Data-Driven Approaches for Smart Parking
No full textFinding a parking space is a key problem in urban scenarios, often due to the lack of actual parking availability information for drivers. Modern vehicles, able to identify free parking spaces using standard on-board sensors, have been proven to be effective probes to measure parking availability. Nevertheless, spatio-temporal datasets resulting from probe vehicles pose significant challenges to the machine learning and data mining communities, due to volume, noise, and heterogeneous spatio-temporal coverage. In this paper we summarize some of the approaches we proposed to extract new knowledge from this data, with the final goal to reduce the parking search time. First, we present a spatio-temporal analysis of the suitability of taxi movements for parking crowd-sensing. Second, we describe machine learning approaches to automatically generate maps of parking spots and to predict parking availability. Finally, we discuss some open issues for the ML/KDD community
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
A Data Driven Approach for Estimating Traffic Demand of Different Transportation Modes
Full text linkTo have a comprehensive overview of the current challenges in the transportation sector, it is essential to analyze traffic behavior and, particularly, to estimate the demand realistically. Therefore, traffic demand, represented by origin-destination (OD) matrices, is a vital key input for many traffic-related applications in traffic planning and management domains. Many studies have developed models to estimate single-modal traffic demand matrices. The conventional models use section traffic counts and traffic surveys as inputs. Unfortunately, it is highly expensive and time-consuming to carry out traffic survey campaigns as the process is not fully automated. A few studies have also developed models to estimate the traffic demand of multimodal shared systems, for example, public or freight transportation systems. However, these models depend on rich data provided by particular data sources, such as user smart cards of public transport systems. To the best of our knowledge, there are no models for estimating multimodal traffic demand of private transportation modes, such as driving, cycling, and walking. We argue that the significant hurdle to developing such models is the lack of reliable data. GPS modules allow the automatic collection of floating data (FD). FD are extensive trip records that provide location coordinates, timestamps, and speed values of devices equipped with active GPS modules. This kind of data can provide the required rich information and significantly reduce the disadvantages of traffic survey campaigns. This work aims to estimate multimodal traffic demand matrices of private transportation modes by fusing different data sources. Specifically, it develops a model to estimate OD matrices of driving, cycling, and walking using traffic counts and FD. To achieve this, the work is divided into three main parts. In the first part, we exploited the potential of floating car data (FCD) to enhance the quality and performance of the demand estimation process for vehicles. This was done by improving all input data of the information minimization (IM) model using FCD. The output of the proposed model was compared to the conventional bi-level demand estimation model. The results confirm that FCD improves the process efficiency and estimation quality. To estimate multimodal OD matrices, floating smartphone data (FSD) should replace FCD in the proposed model. The major disadvantage of automatically collected FSD is the missing information about the used transportation modes for conducting the trips. Many methods in the litera-ture rely on supervised machine learning (ML) algorithms to tackle this problem. However, such algorithms require labeled data, which are not always available. This research part aimed to search for a reliable method to infer transportation modes from unlabeled data. It did this by benchmarking different unsupervised and supervised ML algorithms with various input attributes. Two unsupervised algorithms proved accurate enough when using a reliable data attribute as a feature. The objective of the last research part was to investigate the ability of the developed model in the first part to estimate multimodal OD matrices using the processed FSD in the second part. The main challenge was adjusting the model to consider different transportation modes and obtaining input and validation data for all three modes. Therefore, we launched a traffic surveillance campaign alongside the simulation study to collect real counting and ground truth data using video cameras. These data were used for the field study. Furthermore, we conducted a sensitivity analysis using synthetic data to reflect the development of the estimation accuracy by increasing the penetration rate of FSD in a simulation environment. This part found the proposed model estimated the demand of the car and bicycle modes determined in the field study with an average correlation coefficient of 92%. This result corresponds to the simulation study's sensitivity analysis, which indicates that the model should achieve a demand estimation with an average correlation coefficient of 93% to 96%. For future work, we recommend further developing the model to rely only on FSD as one data source so the need for section traffic counts is no longer a prerequisite.Um einen umfassenden Überblick zum verkehrlichen Infrastrukturbedarf zu erhalten, ist es unabdingbar das Verkehrsverhalten zu analysieren und insbesondere die Verkehrsnachfrage genau abzuschätzen. Für viele verkehrsbezogene Anwendungen, sowohl im Planungs- als auch im Steuerungsbereich, ist die Verkehrsnachfrage, dargestellt durch Quelle-Ziel-Matrizen (auch O-D-Matrizen (origin-destination)), eine wichtige Grundlage. Die Ansätze zur Nachfrageschätzung beziehen sich meist auf einzelne Verkehrsmittel. Die konventionellen Modelle nutzen abschnittsbezogene Verkehrszählungen und Daten aus Verkehrserhebungen. Solche Erhebungen sind jedoch mit hohen Kosten und großem Aufwand verbunden. Die vorhandenen Modelle zur multimodalen Nachfrageschätzung wurden für die Schätzung der Nachfrage verschiedener Güterverkehrsträger oder öffentlicher Verkehrsmittel entwickelt. Diese Modelle nutzen umfangreiche Datenquellen, wie z.B. aus Chipkarten von Nutzerinnen und Nutzern öffentlicher Verkehrssysteme. Soweit bekannt, gibt es kein Modell, das eine multimodale Verkehrsnachfrageschätzung für Individualverkehr ermöglicht. Das Kernproblem liegt hierbei in der Bereitstellung einer zuverlässigen Datengrundlage für ein solches Modell. Mobile GPS-fähige Geräte ermöglichen eine automatische Erfassung von "Floating Data". Floating Data bestehen aus Fahrtenaufzeichnungen (z.B. Kraftfahrzeugen Floating Car Data (FCD) und Smartphones Floating Smartphone Data (FSD)) vieler aktiver Geräten, bei denen die Geopositionierung des Gerätes mit Zeitstempeln dokumentiert ist. Diese Datenquelle kann die benötigte Datengrundlage bereitstellen und eine Alternative zu teuren Erhebungsmethoden darstellen. Vor diesem Hintergrund liegt das Ziel dieser Arbeit liegt darin, ein geeignetes Modell für die Abschätzung der multimodalen Verkehrsnachfrage des motorisierten Individualverkehrs sowie von Radfahrern und Fußgängern zu entwickeln, dass die neue Datenquelle der Floating Data in Verbindung mit Querschnittszählungen nutzt. Um dieses Ziel zu erreichen, wurde die Forschungsarbeit in drei Hauptteile unterteilt. Ziel des ersten Teils war es, das Potenzial von FCD auszunutzen, um den Prozess der Nachfrageschätzung der Fahrzeuge in Bezug auf Ergebnisqualität und Rechenzeitanforderungen zu verbessern. Dies geschah durch die Verbesserung der Eingabedaten des Informationsminimierungsmodells (IM) mit Hilfe von FCD. Das Ergebnis dieses verbesserten Modells wurde analysiert und mit dem herkömmlichen zweistufigen Modell zur Nachfrageschätzung verglichen. Die Vorteile der Verwendung von FCD im verbesserten Modell waren signifikant, was die Grundlage des zweiten Teils bildete. Da FCD lediglich Informationen zum Kraftfahrzeugverkehr beinhalten, wurde für die multimodale Nachfrageschätzung im zweiten Teil der Arbeit die Datengrundlage auf FSD erweitert. Jedoch liegt der größte Nachteil von FSD darin, dass sie keine Informationen über das genutzte Verkehrsmittel liefern können, wenn sie komplett automatisch erhoben wurden. Die vorgeschlagenen Modelle in der Literatur zur Lösung dieses Problems beruhen jedoch auf Algorithmen des überwachten maschinellen Lernens (ML), welche gelabelte Daten für das Training benötigen, die nicht immer zur Verfügung stehen. Ziel des zweiten Teils war es demnach, eine alternative Lösung zur Erkennung von Verkehrsmitteln aus ungelabelten Daten zu untersuchen. Das erfolgte durch einen Vergleich der Genauigkeit von unüberwachten und überwachten ML-Algorithmen mit unterschiedlichen Eingabeattributen. zwei unüberwachte Algorithmen erwiesen sich als ausreichend genau, wenn ein zuverlässiges Eingabeattribut verwendet wurde. Ziel des letzten Forschungsschrittes war es, zu untersuchen, ob das im ersten Teil entwickelte Modell in der Lage ist, multimodale OD-Matrizen unter Verwendung der im zweiten Teil entwickelten Methode zur Verkehrsmittelzuordnung von FSD mit ausreichender Genauigkeit zu schätzen. Die größte Herausforderung bestanden darin, Ground-Truth-Daten sowie Verkehrszählungen für alle drei Verkehrsmittel zu erhalten. Aufgrund dessen wurde eine Kampagne zur Verkehrsüberwachung gestartet, um die erforderlichen Daten mit Videodetektionen zu erheben. Diese Daten wurden für eine Feldstudie verwendet. Darüber hinaus wurde eine Sensitivitätsanalyse mit synthetischen Daten durchgeführt, um die Entwicklung der Schätzgenauigkeit durch eine Erhöhung der FSD-Penetrationsrate in einer Simulationsumgebung widerzuspiegeln. In der Feldstudie führte eine durchschnittliche FSD-Penetrationsrate von 13% zu einer guten Nachfrageschätzung mit einem durchschnittlichen Korrelationskoeffizienten von 92%. Dieses Ergebnis entspricht der Sensitivitätsanalyse aus der Simulationsstudie, welches zeigt, dass eine durchschnittliche FSD-Penetrationsrate von 13% zu einer Nachfrageschätzung mit einem durchschnittlichen Korrelationskoeffizienten zwischen 93% und 96% führen soll. Für die künftige Arbeit empfehlen wir, das Modell so weiterzuentwickeln, dass es sich nur auf die FSD als Datenquelle stützt, so dass die Notwendigkeit von Verkehrszählungen nicht mehr gegeben ist
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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