Using mobile phones to determine transportation modes

By Sasank Reddy, Min Y. Mun, Jeff Burke, Deborah Estrin, Mark H. Hansen, Mani B. Srivastava. TOSN, 2010

918 citations recorded by the source corpus.

Subfields: Global and Planetary Change, Transportation

View Paper: https://www.semanticscholar.org/paper/376dfa43778847987e2e3ac17e4df12418091956

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In Global and Planetary Change

In Transportation

Citations in the corpus

A Survey on Mobile Crowdsensing Systems: Challenges, Solutions, and Opportunities (2019, background): “Other examples consist of distinguishing transportation modes [74]–[77], recognition of speech [78] and driving style [79] and for indoor navigation [80]–[82].”
Classification Accuracies of Physical Activities Using Smartphone Motion Sensors (2012, methodology): “Researchers have used accelerometers on Nokia N95 phones [10,11] and Android phones [12] to detect common activities such walking, stair climbing, jogging, and sitting.”
Smart Devices are Different: Assessing and MitigatingMobile Sensing Heterogeneities for Activity Recognition (2015, background): “Accelerometer-based HAR has been deployed in a large number of domains including smart homes [5, 32, 38], health care [31, 37], daily activity tracking [29], fitness tracking [16], fall detection of elderly people [18] and transportation mode detection [9, 24, 42].”
Safe Driving Using Mobile Phones (2012, background): “The limitations of the refresh rate and software integration yield a usable refresh rate around 25–30 Hz [19], [20].”
Recognizing Detailed Human Context in the Wild from Smartphones and Smartwatches (2016, background): “In many studies the recognition setup involved cumbersome devices that had to be worn in a specific way [5], [12], [13], [14], [15], [17], [22], [23], [25], [28], [29].”
Park Here! a smart parking system based on smartphones' embedded sensors and short range Communication Technologies (2015, methodology): “Step2: Features Extraction Every Tr seconds, compute Vector Features V with Equation 4.”
Review of GPS Travel Survey and GPS Data-Processing Methods (2014, background): “Some research projects also use Smartphones to conduct surveys (Bierlaire, Chen, & Newman, 2013; Hudson, Duthie, Rathod, Larsen, & Meyer, 2012; Reddy et al., 2010; Xiao et al., 2012).”
DEEProtect: Enabling Inference-based Access Control on Mobile Sensing Applications (2017, background): “tem of context-aware apps such as behavior-based authentication [31], [47], [58], [18], [78], [94], fitness monitoring based on activity tracking [5], [76], [57], speech translation [48], [61] and traffic/environmental monitoring [3], [64], [83], [86].”
A Survey of Online Activity Recognition Using Mobile Phones (2015, methodology): “4 weeks [32] (1) 1 week [38] (8), [31] (5), [58] (10) 2 days [42] (10) 1 day [37] (2) Less than an hour [49] (2), [50] (5), [34] (2), [57] (5) NA [47,54,56] (2), [59] (6), [40] (5), [52] (12), [48] (21), [39] (1), [46] (8) NA [33,35,36,41,44,45,51,53,55,60] (NA)”
Transportation mode detection using mobile phones and GIS information (2011, methodology): “There have been studies that consider multiple sensors for transportation mode recognition [4, 17, 18, 29, 30].”
Advanced internet of things for personalised healthcare systems: A survey (2017, background): “Their applications cover gait rehabilitation, joint pathology [5], stork [10], Parkinson’s disease [59] and fall detection [56].”
Accelerometer-based transportation mode detection on smartphones (2013, methodology): “[26], which uses both GPS and accelerometer measurements.”
Deep Convolutional Neural Networks on Multichannel Time Series for Human Activity Recognition (2015, background): “Automatically recognizing human's physical activities (a.k.a. human activity recognition or HAR) has emerged as a key problem to ubiquitous computing, human-computer interaction and human behavior analysis [Bulling et al., 2014; Plätz et al., 2012; Reddy et al., 2010].”
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges (2016, neutral): “For example, researchers have examined the validity of smartphone-based accelerometers for measuring physical activity (Case, Burwick, Volpp, & Patel, 2015), as well as the validity of accelerometers and GPS for identifying modes of transportation (Reddy et al., 2010).”
TapLogger: inferring user inputs on smartphone touchscreens using on-board motion sensors (2012, background): “, inferring if the user is walking or taking vehicles [32, 38]) and activity recognition (i.”
A Survey of Methods for Time Series Change Point Detection (2016, neutral): “Some of the algorithms require information about the data, such as the number of change points in the data, the number of states in the system, and the features of the system states [50,67].”
A Survey on Human Activity Recognition using Wearable Sensors (2013, background): “Accelerometers are inexpensive, require relatively low power [37], and are embedded in most of today's cellular phones.”