2,179 research outputs found
Exploring universal patterns in human home-work commuting from mobile phone data
Home-work commuting has always attracted significant research attention
because of its impact on human mobility. One of the key assumptions in this
domain of study is the universal uniformity of commute times. However, a true
comparison of commute patterns has often been hindered by the intrinsic
differences in data collection methods, which make observation from different
countries potentially biased and unreliable. In the present work, we approach
this problem through the use of mobile phone call detail records (CDRs), which
offers a consistent method for investigating mobility patterns in wholly
different parts of the world. We apply our analysis to a broad range of
datasets, at both the country and city scale. Additionally, we compare these
results with those obtained from vehicle GPS traces in Milan. While different
regions have some unique commute time characteristics, we show that the
home-work time distributions and average values within a single region are
indeed largely independent of commute distance or country (Portugal, Ivory
Coast, and Boston)--despite substantial spatial and infrastructural
differences. Furthermore, a comparative analysis demonstrates that such
distance-independence holds true only if we consider multimodal commute
behaviors--as consistent with previous studies. In car-only (Milan GPS traces)
and car-heavy (Saudi Arabia) commute datasets, we see that commute time is
indeed influenced by commute distance
Mobile travel services: A three-country study into the impact of local circumstances
In this paper we explore the difference in acceptance patterns of mobile services that are related to travelling in three countries: Finland, The Netherlands and New Zealand. The objective of this paper is to understand differences in the use of Mobile Travel Services in three countries that differ with regard to national travel patterns. This paper also contributes to the discussion of the relevance of the Technology Acceptance Model for mobile applications by focusing on the importance of context characteristics, such as the degree of mobility of the user, the social situation people are in, and their need for social interaction. Based on surveys in the three countries as executed in 2009, we use structural equation modelling to find differences in patterns. The paper concludes that context factors have an impact on the relation between the core concepts as used in TAM and DOI approach, and that t here is a clear need for closer research in the moderating effect of physical (e.g. mobile and fixed context) and social context, as well as the need for social interaction. Moreover it is clear that country specific characteristics play a role in the acceptance of mobile travel services. As we pointed out in many of our research projects before the acceptance and use of mobile services requires deep understanding from individual, context and technology related characteristics and their mutual interactions
Smartphone placement within vehicles
This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordSmartphone-based driver monitoring is quickly gaining ground as a feasible alternative to competing in-vehicle and aftermarket solutions. Currently the main challenges for data analysts studying smartphone-based driving data stem from the mobility of the smartphone. In this paper, we use kernel-based k-means clustering to infer the placement of smartphones within vehicles. The trip segments are mapped into fifteen different placement clusters. As a part of the presented framework, we discuss practical considerations concerning e.g., trip segmentation, cluster initialization, and parameter selection. The proposed method is evaluated on more than 10 000 kilometers of driving data collected from approximately 200 drivers. To validate the interpretation of the clusters, we compare the data associated with different clusters and relate the results to real-world knowledge of driving behavior. The clusters associated with the label “Held by hand” are shown to display high gyroscope variances, low maximum speeds, low correlations between the measurements from smartphone-embedded and vehicle-fixed accelerometers, and short segment durations
m-Reading: Fiction reading from mobile phones
Mobile phones are reportedly the most rapidly expanding e-reading device worldwide. However, the embodied, cognitive and affective implications of smartphone-supported fiction reading for leisure (m-reading) have yet to be investigated empirically. Revisiting the theoretical work of digitization scholar Anne Mangen, we argue that the digital reading experience is not only contingent on patterns of embodied reader–device interaction (Mangen, 2008 and later) but also embedded in the immediate environment and broader situational context. We call this the situation constraint. Its application to Mangen’s general framework enables us to identify four novel research areas, wherein m-reading should be investigated with regard to its unique affordances. The areas are reader–device affectivity, situated embodiment, attention training and long-term immersion
VANET Applications: Hot Use Cases
Current challenges of car manufacturers are to make roads safe, to achieve
free flowing traffic with few congestions, and to reduce pollution by an
effective fuel use. To reach these goals, many improvements are performed
in-car, but more and more approaches rely on connected cars with communication
capabilities between cars, with an infrastructure, or with IoT devices.
Monitoring and coordinating vehicles allow then to compute intelligent ways of
transportation. Connected cars have introduced a new way of thinking cars - not
only as a mean for a driver to go from A to B, but as smart cars - a user
extension like the smartphone today. In this report, we introduce concepts and
specific vocabulary in order to classify current innovations or ideas on the
emerging topic of smart car. We present a graphical categorization showing this
evolution in function of the societal evolution. Different perspectives are
adopted: a vehicle-centric view, a vehicle-network view, and a user-centric
view; described by simple and complex use-cases and illustrated by a list of
emerging and current projects from the academic and industrial worlds. We
identified an empty space in innovation between the user and his car:
paradoxically even if they are both in interaction, they are separated through
different application uses. Future challenge is to interlace social concerns of
the user within an intelligent and efficient driving
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