35,543 research outputs found
App-based feedback on safety to novice drivers: learning and monetary incentives
An over-proportionally large number of car crashes is caused by novice drivers. In a field experiment, we investigated whether and how car drivers who had recently obtained their driving license reacted to app-based feedback on their safety-relevant driving behavior (speeding, phone usage, cornering, acceleration and braking). Participants went through a pre-measurement phase during which they did not receive app-based feedback but driving behavior was recorded, a treatment phase during which they received app-based feedback, and a post-measurement phase during which they did not receive app-based feedback but driving behavior was recorded. Before the start of the treatment phase, we randomly assigned participants to two possible treatment groups. In addition to receiving app-based feedback, the participants of one group received monetary incentives to improve their safety-relevant driving behavior, while the participants of the other group did not. At the beginning and at the end of experiment, each participant had to fill out a questionnaire to elicit socio-economic and attitudinal information.
We conducted regression analyses to identify socio-economic, attitudinal, and driving-behavior-related variables that explain safety-relevant driving behavior during the pre-measurement phase and the self-chosen intensity of app usage during the treatment phase. For the main objective of our study, we applied regression analyses to identify those variables that explain the potential effect of providing app-based feedback during the treatment phase on safety-relevant driving behavior. Last, we applied statistical tests of differences to identify self-selection and attrition biases in our field experiment.
For a sample of 130 novice Austrian drivers, we found moderate improvements in safety-relevant driving skills due to app-based feedback. The improvements were more pronounced under the treatment with monetary incentives, and for participants choosing higher feedback intensities. Moreover, drivers who drove relatively safer before receiving app-based feedback used the app more intensely and, ceteris paribus, higher app use intensity led to improvements in safety-related driving skills. Last, we provide empirical evidence for both self-selection and attrition biases
Analysis of distracted pedestrians' waiting time: Head-Mounted Immersive Virtual Reality application
This paper analyzes the distracted pedestrians' waiting time before crossing
the road in three conditions: 1) not distracted, 2) distracted with a
smartphone and 3) distracted with a smartphone in the presence of virtual
flashing LED lights on the crosswalk as a safety measure. For the means of data
collection, we adapted an in-house developed virtual immersive reality
environment (VIRE). A total of 42 volunteers participated in the experiment.
Participants' positions and head movements were recorded and used to calculate
walking speeds, acceleration and deceleration rates, surrogate safety measures,
time spent playing smartphone game, etc. After a descriptive analysis on the
data, the effects of these variables on pedestrians' waiting time are analyzed
by employing a cox proportional hazard model. Several factors were identified
as having impact on waiting time. The results show that an increase in initial
walk speed, percentage of time the head was oriented toward smartphone during
crossing, bigger minimum missed gaps and unsafe crossings resulted in shorter
waiting times. On the other hand, an increase in the percentage of time the
head was oriented toward smartphone during waiting time, crossing time and maze
solving time, means longer waiting times for participants.Comment: Published in the proceedings of Pedestrian and Evacuation Dynamics
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Measuring delays for bicycles at signalized intersections using smartphone GPS tracking data
The article describes an application of global positioning system (GPS) tracking data (floating bike data) for measuring delays for cyclists at signalized intersections. For selected intersections, we used trip data collected by smartphone tracking to calculate the average delay for cyclists by interpolation between GPS locations before and after the intersection. The outcomes were proven to be stable for different strategies in selecting the GPS locations used for calculation, although GPS locations too close to the intersection tended to lead to an underestimation of the delay. Therefore, the sample frequency of the GPS tracking data is an important parameter to ensure that suitable GPS locations are available before and after the intersection. The calculated delays are realistic values, compared to the theoretically expected values, which are often applied because of the lack of observed data. For some of the analyzed intersections, however, the calculated delays lay outside of the expected range, possibly because the statistics assumed a random arrival rate of cyclists. This condition may not be met when, for example, bicycles arrive in platoons because of an upstream intersection. This justifies that GPS-based delays can form a valuable addition to the theoretically expected values
Towards a Practical Pedestrian Distraction Detection Framework using Wearables
Pedestrian safety continues to be a significant concern in urban communities
and pedestrian distraction is emerging as one of the main causes of grave and
fatal accidents involving pedestrians. The advent of sophisticated mobile and
wearable devices, equipped with high-precision on-board sensors capable of
measuring fine-grained user movements and context, provides a tremendous
opportunity for designing effective pedestrian safety systems and applications.
Accurate and efficient recognition of pedestrian distractions in real-time
given the memory, computation and communication limitations of these devices,
however, remains the key technical challenge in the design of such systems.
Earlier research efforts in pedestrian distraction detection using data
available from mobile and wearable devices have primarily focused only on
achieving high detection accuracy, resulting in designs that are either
resource intensive and unsuitable for implementation on mainstream mobile
devices, or computationally slow and not useful for real-time pedestrian safety
applications, or require specialized hardware and less likely to be adopted by
most users. In the quest for a pedestrian safety system that achieves a
favorable balance between computational efficiency, detection accuracy, and
energy consumption, this paper makes the following main contributions: (i)
design of a novel complex activity recognition framework which employs motion
data available from users' mobile and wearable devices and a lightweight
frequency matching approach to accurately and efficiently recognize complex
distraction related activities, and (ii) a comprehensive comparative evaluation
of the proposed framework with well-known complex activity recognition
techniques in the literature with the help of data collected from human subject
pedestrians and prototype implementations on commercially-available mobile and
wearable devices
Characterizing web pornography consumption from passive measurements
Web pornography represents a large fraction of the Internet traffic, with
thousands of websites and millions of users. Studying web pornography
consumption allows understanding human behaviors and it is crucial for medical
and psychological research. However, given the lack of public data, these works
typically build on surveys, limited by different factors, e.g. unreliable
answers that volunteers may (involuntarily) provide.
In this work, we collect anonymized accesses to pornography websites using
HTTP-level passive traces. Our dataset includes about broadband
subscribers over a period of 3 years. We use it to provide quantitative
information about the interactions of users with pornographic websites,
focusing on time and frequency of use, habits, and trends. We distribute our
anonymized dataset to the community to ease reproducibility and allow further
studies.Comment: Passive and Active Measurements Conference 2019 (PAM 2019). 14 pages,
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Media use during adolescence: the recommendations of the Italian Pediatric Society.
BACKGROUND: The use of media device, such as smartphone and tablet, is currently increasing, especially among the youngest. Adolescents spend more and more time with their smartphones consulting social media, mainly Facebook, Instagram and Twitter because. Adolescents often feel the necessity to use a media device as a means to construct a social identity and express themselves. For some children, smartphone ownership starts even sooner as young as 7 yrs, according to internet safety experts. MATERIAL AND METHODS: We analyzed the evidence on media use and its consequences in adolescence. RESULTS: In literature, smartphones and tablets use may negatively influences the psychophysical development of the adolescent, such as learning, sleep and sigh. Moreover, obesity, distraction, addiction, cyberbullism and Hikikomori phenomena are described in adolescents who use media device too frequently. The Italian Pediatric Society provide action-oriented recommendations for families and clinicians to avoid negative outcomes. CONCLUSIONS: Both parents and clinicians should be aware of the widespread phenomenon of media device use among adolescents and try to avoid psychophysical consequences on the youngest
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