Online travel mode detection method using automated machine learning and feature engineering

Soares, Elton; Campos, Carlos Alberto Vieira; Lucena, Sidney C. de

doi:10.1016/j.future.2019.07.056

Cited by 17 publications

(10 citation statements)

References 27 publications

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“…This feature set is selected by the use of correlation based feature selection (CFS) method. [5] . Accelerometer variance can be used to infer if the user is running and the DFT coefficients help in differentiating between foot-based modes.…”

Section: Reddymentioning

confidence: 99%

“…Peak and segment-based features describe the movement patterns of vehicles, instead of movements of the user, making these features robust against different device positioning (i.e., it helps to meet position independency requirement). [5] Correlation-based feature selection (CFS) is a feature subset selector that eliminates irrelevant and redundant attributes.…”

Section: Hemminkimentioning

confidence: 99%

“…Such travel information becomes even more important for traffic management when a fast response is required for the allocation of one specific mode of public transportation. For example, a football match, a national festival or a bad weather condition may change the regular public transportation demands in a specific period of time [5]. Furthermore, monitoring patients travel behaviour may contribute to control the spread of a disease during a pandemic.…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: A Survey on Local Transport Mode Detection on The Edge

Kamalian

Ferreira

2020

Preprint

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We present a survey of solutions for smartphone-based transport mode detection. These are divided into local and remote approaches being the first ones addressed in this article. A local approach performs the following steps in the smartphone (and not in some far away cloud server): 1) data collection or sensing, 2) preprocessing, 3) feature extraction, and 4) classification (with a previous machine learning based training phase). Solutions are presented taking into account the above mentioned four steps, and analyzed according to the most relevant requirements (accuracy, delay taken to detect a transport mode, resources consumption, and generalization).

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Section: Reddymentioning

confidence: 99%

Section: Hemminkimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: A Survey on Local Transport Mode Detection on The Edge

Kamalian

Ferreira

2020

Preprint

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“…Typical tasks that are performed with the models implemented with AutoML are classification tasks. For example, it was used in the health sector [28,30,31], the corporate sector [32], the environmental sector [33,34], the energy sector [35,36], and others [37][38][39]. There are many AutoML tools and solutions available today to help data scientists.…”

Section: Automated Machine Learningmentioning

confidence: 99%

Using a DEA–AutoML Approach to Track SDG Achievements

Singpai

2020

Sustainability

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Each country needs to monitor progress on their Sustainable Development Goals (SDGs) to develop strategies that meet the expectations of the United Nations. Data envelope analysis (DEA) can help identify best practices for SDGs by setting goals to compete against. Automated machine learning (AutoML) simplifies machine learning for researchers who need less time and manpower to predict future situations. This work introduces an integrative method that integrates DEA and AutoML to assess and predict performance in SDGs. There are two experiments with different data properties in their interval and correlation to demonstrate the approach. Three prediction targets are set to measure performance in the regression, classification, and multi-target regression algorithms. The back-propagation neural network (BPNN) is used to validate the outputs of the AutoML. As a result, AutoML can outperform BPNN for regression and classification prediction problems. Low standard deviation (SD) data result in poor prediction performance for the BPNN, but does not have a significant impact on AutoML. Highly correlated data result in a higher accuracy, but does not significantly affect the R-squared values between the actual and predicted values. This integrative approach can accurately predict the projected outputs, which can be used as national goals to transform an inefficient country into an efficient country.

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Section: Introductionmentioning

confidence: 99%