2021
DOI: 10.3390/su132112226
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Time-Use and Spatio-Temporal Variables Influence on Physical Activity Intensity, Physical and Social Health of Travelers

Abstract: Using a multi-dimensional three-week household time-use and activity diary, this study aims to investigate the interaction between time-use and activity travel participation, built environment, leisure-time physical activity, travel parameters, and physical intensity on physical and social health. The relationship between time-use and activity travel participation is complex. Therefore, physical activity (PA) intensity is assumed to intermediate the relationship between endogenuous and exogenous variables. Thi… Show more

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Cited by 26 publications
(14 citation statements)
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“…Most of the solutions mentioned above, however, rely heavily on pre-existing assumptions. Machine learning (ML) techniques have more flexibility than traditional statistical models in that they can analyse noisy data, outliers, and missing data, without or with minimal previous assumptions about inputs [12][13][14][15][16][17][18]. In addition, ML methods are notable instances of data-driven techniques that strive to improve the efficiency and precision of accident data processing and forecasting.…”
Section: Introductionmentioning
confidence: 99%
“…Most of the solutions mentioned above, however, rely heavily on pre-existing assumptions. Machine learning (ML) techniques have more flexibility than traditional statistical models in that they can analyse noisy data, outliers, and missing data, without or with minimal previous assumptions about inputs [12][13][14][15][16][17][18]. In addition, ML methods are notable instances of data-driven techniques that strive to improve the efficiency and precision of accident data processing and forecasting.…”
Section: Introductionmentioning
confidence: 99%
“…MQTT is a universal transfer protocol that offers an easy and lightweight method of transmitting messages [49]. It uses a bandwidth that allows remote connections to quickly exchange data between clients, thus making it an efficient transfer protocol commonly used in IoT applications [50].…”
Section: Automated Yogurt Filling Processmentioning
confidence: 99%
“…ML approaches describe complicated connections between variables by data-driven learning, as opposed to establishing strong assertions beforehand [25]. Numerous studies in the transportation domain have shown that ML approaches are more accurate predictors than traditional statistical techniques [e.g., 19,26,[27][28][29].…”
Section: Introductionmentioning
confidence: 99%