scholarly journals Travel Mode Detection Based on GPS Raw Data Collected by Smartphones: A Systematic Review of the Existing Methodologies

Information ◽  
2016 ◽  
Vol 7 (4) ◽  
pp. 67 ◽  
Author(s):  
Linlin Wu ◽  
Biao Yang ◽  
Peng Jing
Keyword(s):  
Systematic Review ◽  
Raw Data ◽  
Travel Mode ◽  
Mode Detection

scholarly journals Travel mode detection based on GPS raw data collected by smartphones: a systematic review of the existing methodologies

2016 ◽  
Author(s):  
Linlin Wu ◽  
Biao Yang ◽  
Peng Jing
Keyword(s):  
Systematic Review ◽  
Large Scale ◽  
Great Chance ◽  
Raw Data ◽  
Travel Mode ◽  
Review Section ◽  
Travel Survey ◽  
Large Scale Data ◽  
Mode Detection ◽  
Gps Devices

Over the past couple of decades, Global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., need much money to buy devices, forget to take devices to collect data, limit the simple size because of the number of devices, etc.), and the phenomenon that the smartphone is becoming one of necessities of life, there is a great chance for the smartphone to replace dedicated GPS devices. Although, several general reviews have been done about smartphone-based GPS travel survey in the literature review section in some articles, a systematic review from smartphone-based GPS data collection to travel mode detection has none. The included studies were searched from six databases. The purpose of this review is to critically assess the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to the systematic comparison among different methods from data-preprocessing to travel mode detection, this paper could carefully provide the Strengths and Weaknesses of existing methods. Furthermore, it is the crucial step to develop the methodologies and applications of GPS raw data collected by smartphones.

scholarly journals Reproducible and Transparent Research Practices in Published Neurology Research

2019 ◽  
Author(s):  
Shelby Rauh ◽  
Trevor Torgerson ◽  
Austin L. Johnson ◽  
Jonathan Pollard ◽  
Daniel Tritz ◽  
...  
Keyword(s):  
Systematic Review ◽  
Conflict Of Interest ◽  
Data Extraction ◽  
Meta Analysis ◽  
Research Practices ◽  
Raw Data ◽  
Replication Studies ◽  
Study Materials ◽  
Funding Sources ◽  
Pubmed Search

AbstractBackgroundThe objective of this study was to evaluate the nature and extent of reproducible and transparent research practices in neurology research.MethodsThe NLM catalog was used to identify MEDLINE-indexed neurology journals. A PubMed search of these journals was conducted to retrieve publications over a 5-year period from 2014 to 2018. A random sample of publications was extracted. Two authors conducted data extraction in a blinded, duplicate fashion using a pilot-tested Google form. This form prompted data extractors to determine whether publications provided access to items such as study materials, raw data, analysis scripts, and protocols. In addition, we determined if the publication was included in a replication study or systematic review, was preregistered, had a conflict of interest declaration, specified funding sources, and was open access.ResultsOur search identified 223,932 publications meeting the inclusion criteria, from which 300 were randomly sampled. Only 290 articles were accessible, yielding 202 publications with empirical data for analysis. Our results indicate that 8.99% provided access to materials, 9.41% provided access to raw data, 0.50% provided access to the analysis scripts, 0.99% linked the protocol, and 3.47% were preregistered. A third of sampled publications lacked funding or conflict of interest statements. No publications from our sample were included in replication studies, but a fifth were cited in a systematic review or meta-analysis.ConclusionsCurrent research in the field of neurology does not consistently provide information needed for reproducibility. The implications of poor research reporting can both affect patient care and increase research waste. Collaborative intervention by authors, peer reviewers, journals, and funding sources is needed to mitigate this problem.

scholarly journals Travel Mode Detection Exploiting Cellular Network Data

2016 ◽  
Vol 81 ◽  
pp. 03008 ◽  
Author(s):  
Arash Kalatian ◽  
Yousef Shafahi
Keyword(s):  
Cellular Network ◽  
Network Data ◽  
Travel Mode ◽  
Mode Detection

Toward More Sustainable Behavior: A Systematic Review of the Impacts of Involuntary Workplace Relocation on Travel Mode Choice

2018 ◽  
Vol 34 (1) ◽  
pp. 38-58 ◽  
Author(s):  
Z. Zarabi ◽  
S. Lord
Keyword(s):  
Systematic Review ◽  
Travel Behavior ◽  
Mode Choice ◽  
Travel Mode ◽  
Travel Mode Choice ◽  
Home Work ◽  
Habitual Behavior ◽  
Sustainable Behavior ◽  
Transport Modes ◽  
Daily Travel

Daily home–work travel is a habitual behavior that can be disrupted when the location of work, as one of the behavioral contexts, changes. It is then likely that individuals will reconsider their travel behavior more intentionally and choose alternative transport modes. To identify motivations and barriers to incorporating the use of sustainable modes into the individual’s daily travel, this article systematically reviews the literature on the impacts of involuntary workplace relocation on commuting behavior. Effective measures that incentivize sustainable commuting behavior are also discussed. This study on involuntary workplace relocation informs considerations of changes in travel behavior related to other contextual changes.

scholarly journals Travel Mode Detection with Varying Smartphone Data Collection Frequencies

Sensors ◽  
2016 ◽  
Vol 16 (5) ◽  
pp. 716 ◽  
Author(s):  
Muhammad Shafique ◽  
Eiji Hato
Keyword(s):  
Data Collection ◽  
Travel Mode ◽  
Mode Detection

scholarly journals What cognitive mechanisms predict travel mode choice? A systematic review with meta-analysis

2017 ◽  
Vol 37 (5) ◽  
pp. 631-652 ◽  
Author(s):  
Christin Hoffmann ◽  
Charles Abraham ◽  
Mathew P. White ◽  
Susan Ball ◽  
Stephen M. Skippon
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Mode Choice ◽  
Cognitive Mechanisms ◽  
Travel Mode ◽  
Travel Mode Choice

scholarly journals Using genetic programming on GPS trajectories for travel mode detection

2021 ◽  
Author(s):  
Farnoosh Namdarpour ◽  
Mahmoud Mesbah ◽  
Amir H. Gandomi ◽  
Behrang Assemi
Keyword(s):  
Genetic Programming ◽  
Travel Mode ◽  
Mode Detection ◽  
Gps Trajectories

scholarly journals Incorporating repeated measurements into prediction models in the critical care setting: a framework, systematic review and meta-analysis

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Joost D. J. Plate ◽  
Rutger R. van de Leur ◽  
Luke P. H. Leenen ◽  
Falco Hietbrink ◽  
Linda M. Peelen ◽  
...  
Keyword(s):  
Systematic Review ◽  
Critical Care ◽  
Care Setting ◽  
Prediction Models ◽  
Meta Analysis ◽  
Repeated Measurements ◽  
Critical Care Setting ◽  
Annual Number ◽  
Observation Window ◽  
Raw Data

Abstract Background The incorporation of repeated measurements into multivariable prediction research may greatly enhance predictive performance. However, the methodological possibilities vary widely and a structured overview of the possible and utilized approaches lacks. Therefore, we [1] propose a structured framework for these approaches, [2] determine what methods are currently used to incorporate repeated measurements in prediction research in the critical care setting and, where possible, [3] assess the added discriminative value of incorporating repeated measurements. Methods The proposed framework consists of three domains: the observation window (static or dynamic), the processing of the raw data (raw data modelling, feature extraction and reduction) and the type of modelling. A systematic review was performed to identify studies which incorporate repeated measurements to predict (e.g. mortality) in the critical care setting. The within-study difference in c-statistics between models with versus without repeated measurements were obtained and pooled in a meta-analysis. Results From the 2618 studies found, 29 studies incorporated multiple repeated measurements. The annual number of studies with repeated measurements increased from 2.8/year (2000–2005) to 16.0/year (2016–2018). The majority of studies that incorporated repeated measurements for prediction research used a dynamic observation window, and extracted features directly from the data. Differences in c statistics ranged from − 0.048 to 0.217 in favour of models that utilize repeated measurements. Conclusions Repeated measurements are increasingly common to predict events in the critical care domain, but their incorporation is lagging. A framework of possible approaches could aid researchers to optimize future prediction models.

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