scholarly journals Trip mode inference from mobile phone signaling data using Logarithm Gaussian Mixture Model

2020 ◽  
Vol 13 (1) ◽  
pp. 429-445
Author(s):  
Xiaoxu Chen ◽  
Xiangdong Xu ◽  
Chao Yang
Keyword(s):  
Computational Complexity ◽  
Mobile Phone ◽  
Travel Time ◽  
Mobile Devices ◽  
Mixed Model ◽  
Gaussian Mixture ◽  
High Accuracy ◽  
Gps Data ◽  
Inference Method ◽  
Acceptable Accuracy

Trip mode inference plays an important role in transportation planning and management. Most studies in the field have focused on the methods based on GPS data collected from mobile devices. While these methods can achieve relatively high accuracy, they also have drawbacks in data quantity, coverage, and computational complexity. This paper develops a trip mode inference method based on mobile phone signaling data. The method mainly consists of three parts: activity-nodes recognition, travel-time computation, and clustering using the Logarithm Gaussian Mixed Model. Moreover, we compare two other methods (i.e., Gaussian Mixed Model and K-Means) with the Logarithm Gaussian Mixed Model. We conduct experiments using real mobile phone signaling data in Shanghai and the results show that the proposed method can obtain acceptable accuracy overall. This study provides an important opportunity to infer trip mode from the aspect of probability using mobile phone signaling data.

Predicting travel time reliability using mobile phone GPS data

2017 ◽  
Vol 75 ◽  
pp. 30-44 ◽  
Author(s):  
Dawn Woodard ◽  
Galina Nogin ◽  
Paul Koch ◽  
David Racz ◽  
Moises Goldszmidt ◽  
...  
Keyword(s):  
Mobile Phone ◽  
Travel Time ◽  
Gps Data ◽  
Travel Time Reliability

scholarly journals The Current State of Mobile Phone Apps for Monitoring Heart Rate, Heart Rate Variability, and Atrial Fibrillation: Narrative Review (Preprint)

2018 ◽  
Author(s):  
Ka Hou Christien Li ◽  
Francesca Anne White ◽  
Timothy Tipoe ◽  
Tong Liu ◽  
Martin CS Wong ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Mobile Phone ◽  
Mobile Devices ◽  
Large Scale ◽  
High Accuracy ◽  
Narrative Review ◽  
Recording Device ◽  
Current State

BACKGROUND Mobile phone apps capable of monitoring arrhythmias and heart rate (HR) are increasingly used for screening, diagnosis, and monitoring of HR and rhythm disorders such as atrial fibrillation (AF). These apps involve either the use of (1) photoplethysmographic recording or (2) a handheld external electrocardiographic recording device attached to the mobile phone or wristband. OBJECTIVE This review seeks to explore the current state of mobile phone apps in cardiac rhythmology while highlighting shortcomings for further research. METHODS We conducted a narrative review of the use of mobile phone devices by searching PubMed and EMBASE from their inception to October 2018. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) mobile phone monitoring, (2) AF, (3) HR, and (4) HR variability (HRV). RESULTS The findings of this narrative review suggest that there is a role for mobile phone apps in the diagnosis, monitoring, and screening for arrhythmias and HR. Photoplethysmography and handheld electrocardiograph recorders are the 2 main techniques adopted in monitoring HR, HRV, and AF. CONCLUSIONS A number of studies have demonstrated high accuracy of a number of different mobile devices for the detection of AF. However, further studies are warranted to validate their use for large scale AF screening.

scholarly journals Measure of Similarity between GMMs by Embedding of the Parameter Space That Preserves KL Divergence

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 957
Author(s):  
Branislav Popović ◽  
Lenka Cepova ◽  
Robert Cep ◽  
Marko Janev ◽  
Lidija Krstanović
Keyword(s):  
Computational Complexity ◽  
Parameter Space ◽  
Recognition Accuracy ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Dimensional Manifold ◽  
Dimensional Parameter ◽  
Trade Off ◽  
Measure Of Similarity ◽  
Lower Dimensional

In this work, we deliver a novel measure of similarity between Gaussian mixture models (GMMs) by neighborhood preserving embedding (NPE) of the parameter space, that projects components of GMMs, which by our assumption lie close to lower dimensional manifold. By doing so, we obtain a transformation from the original high-dimensional parameter space, into a much lower-dimensional resulting parameter space. Therefore, resolving the distance between two GMMs is reduced to (taking the account of the corresponding weights) calculating the distance between sets of lower-dimensional Euclidean vectors. Much better trade-off between the recognition accuracy and the computational complexity is achieved in comparison to measures utilizing distances between Gaussian components evaluated in the original parameter space. The proposed measure is much more efficient in machine learning tasks that operate on large data sets, as in such tasks, the required number of overall Gaussian components is always large. Artificial, as well as real-world experiments are conducted, showing much better trade-off between recognition accuracy and computational complexity of the proposed measure, in comparison to all baseline measures of similarity between GMMs tested in this paper.

scholarly journals Research on the Use of Mobile Devices and Headphones on Pedestrian Crossings—Pilot Case Study from Slovakia

Safety ◽  
2021 ◽  
Vol 7 (1) ◽  
pp. 17
Author(s):  
Miroslava Mikusova ◽  
Joanna Wachnicka ◽  
Joanna Zukowska
Keyword(s):  
Mobile Phone ◽  
Mobile Devices ◽  
Mobile Phones ◽  
Slovak Republic ◽  
Pedestrian Behavior ◽  
The Road ◽  
Phone Calls ◽  
Lack Of Information ◽  
Interaction Factor ◽  
Future Work

The topic of the use of mobile devices and headphones on pedestrian crossings is much less explored in comparison to the use of the mobile phone while driving. Recent years have seen many discussions on this issue, especially in foreign countries. The Slovak Republic, however, has not been giving it enough attention (and it is not mentioned in the National Road Safety Plan for the Slovak Republic from 2011 to 2020). This paper aims to draw attention to this issue. It presents basic outputs of a pilot study on pedestrian safety, with a focus on the use of mobile devices and headphones at selected non-signalized pedestrian crossings in three Slovak cities. Overall, 9% of pedestrians used headphones or mobile devices at observed pedestrian crossings (4% of them used headphones, 1% used headphones and at same time used their mobile phone, 2% made phone calls and 2% used their mobile phones). While these numbers can be considered relatively low, the study proved that during weekdays every 2 min someone was using the crossing without fully focusing on crossing the road safely. Another main finding was that although the safety risk at pedestrian crossings is increased by factors such as rush hour traffic or reduced visibility, pedestrian behavior related to the use of mobile phones and headphones does not change. A safety assessment was also carried out at the crossings. The results show that pedestrian behavior is not affected by the level of safety of the crossing (e.g., visibility of the crossing for drivers). The results of the presented analysis suggest that action is needed to change that. Due to the lack of information about accidents involving pedestrians using mobile phones and headsets when crossing the road, no relevant statistical data could be analyzed. The dataset collected can be used as a basis for further investigation or comparisons with other countries of the relevant indicators. In future work, we would like to include a pedestrian–driver interaction factor focusing on driver speed behavior in relation to pedestrians (who are on or are about to step onto a pedestrian crossing) and identify critical situations caused by improper behavior of drivers and/or pedestrians. This will help to understand speed adjustment problems related to pedestrian crossings.

Estimating Link Travel Time with Sparse GPS Data on Highway Corridors

2015 ◽  
Vol 2477 (1) ◽  
pp. 7-17 ◽  
Author(s):  
Qi Gong ◽  
Teresa M. Adams ◽  
Xiubin Bruce Wang
Keyword(s):  
Travel Time ◽  
Gps Data ◽  
Link Travel Time

Accelerating Climate Model Computation by Neural Networks: A Comparative Study

2021 ◽  
Author(s):  
Maha Mdini ◽  
Takemasa Miyoshi ◽  
Shigenori Otsuka
Keyword(s):  
Neural Networks ◽  
Computational Complexity ◽  
Statistical Model ◽  
Physical Model ◽  
Climate Model ◽  
Numerical Models ◽  
Computation Time ◽  
High Accuracy ◽  
Physical Models ◽  
Computer Resources

<p>In the era of modern science, scientists have developed numerical models to predict and understand the weather and ocean phenomena based on fluid dynamics. While these models have shown high accuracy at kilometer scales, they are operated with massive computer resources because of their computational complexity.  In recent years, new approaches to solve these models based on machine learning have been put forward. The results suggested that it be possible to reduce the computational complexity by Neural Networks (NNs) instead of classical numerical simulations. In this project, we aim to shed light upon different ways to accelerating physical models using NNs. We test two approaches: Data-Driven Statistical Model (DDSM) and Hybrid Physical-Statistical Model (HPSM) and compare their performance to the classical Process-Driven Physical Model (PDPM). DDSM emulates the physical model by a NN. The HPSM, also known as super-resolution, uses a low-resolution version of the physical model and maps its outputs to the original high-resolution domain via a NN. To evaluate these two methods, we measured their accuracy and their computation time. Our results of idealized experiments with a quasi-geostrophic model [SO3] show that HPSM reduces the computation time by a factor of 3 and it is capable to predict the output of the physical model at high accuracy up to 9.25 days. The DDSM, however, reduces the computation time by a factor of 4 and can predict the physical model output with an acceptable accuracy only within 2 days. These first results are promising and imply the possibility of bringing complex physical models into real time systems with lower-cost computer resources in the future.</p>

scholarly journals User-independent accelerometer-based gesture recognition for mobile devices

2013 ◽  
Vol 1 (3) ◽  
pp. 11-25 ◽  
Author(s):  
Xian Wang ◽  
Paula Tarrío ◽  
Ana María Bernardos ◽  
Eduardo Metola ◽  
José Ramón Casar
Keyword(s):  
Computational Complexity ◽  
Mobile Devices ◽  
Gesture Recognition ◽  
Inertial Sensors ◽  
Recognition Accuracy ◽  
State Of The Art ◽  
Smart Phone ◽  
Recognition System ◽  
Independent Manner ◽  
Wheeled Robot

Many mobile devices embed nowadays inertial sensors. This enables new forms of human-computer interaction through the use of gestures (movements performed with the mobile device) as a way of communication. This paper presents an accelerometer-based gesture recognition system for mobile devices which is able to recognize a collection of 10 different hand gestures. The system was conceived to be light and to operate in a user-independent manner in real time. The recognition system was implemented in a smart phone and evaluated through a collection of user tests, which showed a recognition accuracy similar to other state-of-the art techniques and a lower computational complexity. The system was also used to build a human-robot interface that enables controlling a wheeled robot with the gestures made with the mobile phone

scholarly journals Delay Measurement of Manually Controlled Intersection Using GPS

2014 ◽  
Vol 8 (1) ◽  
pp. 130-135
Author(s):  
S. Nithya ◽  
D. Senthurkumar ◽  
K. .Gunasekaran
Keyword(s):  
Travel Time ◽  
Traffic Control ◽  
Weather Conditions ◽  
Heterogeneous Traffic ◽  
Gps Data ◽  
Peak Period ◽  
Control Delay ◽  
Average Travel Time ◽  
Vehicle Acceleration ◽  
Acceleration And Deceleration

The travel time studies are one of the most important measures used for evaluating the performance of road networks. The Global Positioning System (GPS) is a space-based system that provides position and time information in all weather conditions. GPS data could be used to obtain the values of traffic control delay, vehicle queue, average travel time and vehicle acceleration and deceleration at intersections.The task of estimation of delay becomes complex if it is performed for intersections carrying heterogeneous traffic and that to for over saturated conditions. Most of the urban signalized intersections are manually controlled during peak hours. GPS device fitted in a vehicle was run repeatedly during morning peak period and the period during which vehicles were allowed to cross the intersection was recorded with video graphic camera. The attempt to identify the control delay with the GPS data from the test vehicle while crossing manually operated major intersection is presented in this paper.

scholarly journals CREATING DIGITAL ELEVATION MODEL USING A MOBILE DEVICE

2017 ◽  
Vol IV-4/W4 ◽  
pp. 203-207 ◽  
Author(s):  
A. İ. Durmaz
Keyword(s):  
Mobile Devices ◽  
Digital Elevation Model ◽  
Mobile Device ◽  
Point Clouds ◽  
Lidar Data ◽  
Government Agencies ◽  
Gps Data ◽  
Digital Elevation ◽  
The World ◽  
Elevation Model

DEM (Digital Elevation Models) is the best way to interpret topography on the ground. In recent years, lidar technology allows to create more accurate elevation models. However, the problem is this technology is not common all over the world. Also if Lidar data are not provided by government agencies freely, people have to pay lots of money to reach these point clouds. In this article, we will discuss how we can create digital elevation model from less accurate mobile devices’ GPS data. Moreover, we will evaluate these data on the same mobile device which we collected data to reduce cost of this modeling.

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