A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys

Nitsche, Philippe; Widhalm, Peter; Breuss, Simon; Maurer, P.

doi:10.1016/j.sbspro.2012.06.1080

Cited by 60 publications

(38 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…A large number of studies has focused on the recording and analysis of mobility and activity data using smartphones, usually by utilizing location (GPS) and accelerometer data ( [18], [19], [20], [21], [22], [23], [24], [25], [26]). Issues such as suitability of sensors for activity recognition [27], accuracy of transport mode classification [28], [29] and energy consumption of the app [30] are well researched areas.…”

Section: Large Scale Automatic Mobility Monitoringmentioning

confidence: 99%

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Bucher¹,

Cellina²,

Mangili³

et al. 2016

Proceedings of ICT for Sustainability 2016

View full text Add to dashboard Cite

Abstract-The large interest in analyzing one's own fitness led to the development of more and more powerful smartphone applications. Most are capable of tracking a user's position and mode of locomotion, data that do not only reflect personal health, but also mobility choices. A large field of research is concerned with mobility analysis and planning for a variety of reasons, including sustainable transport. Collecting data on mobility behavior using fitness tracker apps is a tempting choice, because they include many of the desired functions, most people own a smartphone and installing a fitness tracker is quick and convenient. However, as their original focus is on measuring fitness behavior, there are a number of difficulties in their usage for mobility tracking. In this paper we denote the various challenges we faced when deploying GoEco! Tracker (an app using the Moves R fitness tracker to collect mobility measurements), and provide an analysis on how to best overcome them. Finally, we summarize findings after one month of large scale testing with a few hundred users within the GoEco! living lab performed in Switzerland.

show abstract

Section: Large Scale Automatic Mobility Monitoringmentioning

confidence: 99%

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Bucher¹,

Cellina²,

Mangili³

et al. 2016

Proceedings of ICT for Sustainability 2016

View full text Add to dashboard Cite

show abstract

“…Moreover, Nitsche et al (2012) gathered 266 h of travel data with the help of 14 test participants and extracted 72 features for use in probabilistic classifiers. The results ranged from 50 to 98 % over different modes of transportation.…”

Section: Gps With Accelerometermentioning

confidence: 99%

Use of acceleration data for transportation mode prediction

Shafique

Hato

2014

Transportation

View full text Add to dashboard Cite

Most smartphones today are equipped with an accelerometer, in addition to other sensors. Any data recorded by the accelerometer can be successfully utilised to determine the mode of transportation in use, which will provide an alternative to conventional household travel surveys and make it possible to implement customer-oriented advertising programmes. In this study, a comparison is made between changes in preprocessing, selection methods for generating training data, and classifiers, using the accelerometer data collected from three cities in Japan. The classifiers used were support vector machines (SVM), adaptive boosting (AdaBoost), decision tree and random forests. The results of this exercise suggest that using a 125-point moving average during preprocessing and selecting training data proportionally for all modes will maximise prediction accuracy. Moreover, random forests outperformed all other classifiers by yielding an overall prediction accuracy of 99.8 % for all three cities.

show abstract

“…A few studies have attempted to detect transportation modes using GPS and accelerometer data from smartphone sensors (Reddy, Burke, Estrin, Hansen, & Srivastava, 2008;Manzoni, Maniloff, Kloeckl, & Ratti, 2011;Nitsche, Widhalm, Breuss & Maurer, 2012;Parlak, Jariyasunant & Sengupta, 2012;Fan, Chen, Liao, & Douma, 2013).…”

Section: The Use Of Smartphones To Collect Travel Behavior Datamentioning

confidence: 99%

“…Previous studies have mainly applied two different approaches for the mode detection stage: rule-based algorithms (Bohte & Maat, 2009;Chen, Gong, Lawson, & Bialostozky, 2010) and machine learning approaches like fuzzy logic (Schuessler & Axhausen, 2009), Decision Trees (Manzoni, 2011;Reddy et al, 2010;Zheng et al, 2010;Siirtola & Röning, 2012), Bayesian Networks (Moiseeva, Jessurun, & Timmermans, 2010;Feng & Timmermans, 2012), Support Vector Machines (Bolbol, Cheng, Tsapakis, & Haworth, 2012), Neural Networks (Gonzalez et al, 2008;Stenneth, Wolfson, Yu, & Xu, 2011) and other methods based on decision trees (Random Subspace methods used by Nitsche, Widhalm, Breuss, & Maurer, 2012; Random Forest classifier used by Parlak, Jariyasunant, & Sengupta, 2012). Bohte & Maat (2009) applied rule-based algorithms for the processing of the GPS and GIS data and for the detection of travel modes.…”

Section: Processing Of the Data And Identification Of Transportation mentioning

confidence: 99%

Travel Behavior Characterization Using Raw Accelerometer Data Collected from Smartphones

Ferrer

Ruíz

2014

Procedia - Social and Behavioral Sciences

View full text Add to dashboard Cite

In this paper, we compare different algorithms for the recognition of transportation modes based on features extracted from the accelerometer data. The performance and effectiveness of the transportation mode classifiers presented is evaluated and their accuracy is discussed. The data set used for training and testing algorithms was collected by a group of volunteers in the city of Valencia in 2013; an Android application designed for the recording of trips and transportation modes application was installed on their smartphones. This application collected GPS readings each 10-12 seconds and accelerometer data at 1Hz. While GPS data was only used for the validation of trips for the training of the algorithms, accelerometer readings were used entirely for their training. Results show the high performance of Recurrent Neural Networks in recognizing travel modes using accelerometer data.

show abstract

A Strategy on How to Utilize Smartphones for Automatically Reconstructing Trips in Travel Surveys

Cited by 60 publications

References 10 publications

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Exploiting Fitness Apps for Sustainable Mobility - Challenges Deploying the GoEco! App

Use of acceleration data for transportation mode prediction

Travel Behavior Characterization Using Raw Accelerometer Data Collected from Smartphones

Contact Info

Product

Resources

About