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Smart city mobility application: gradient boosting trees for mobility prediction and analysis based on crowdsourced data

Ivana Semanjski (UGent) and Sidharta Gautama (UGent)
(2015) SENSORS. 15(7). p.15974-15987
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Abstract
Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process).
Keywords
mobility management, gradient boosted trees, crowdsourcing, smart city, modelling mobility decision making, CITIES

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MLA
Semanjski, Ivana, and Sidharta Gautama. “Smart City Mobility Application: Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.” SENSORS, edited by Antonio Puliafito et al., vol. 15, no. 7, MDPI, 2015, pp. 15974–87, doi:10.3390/s150715974.
APA
Semanjski, I., & Gautama, S. (2015). Smart city mobility application: gradient boosting trees for mobility prediction and analysis based on crowdsourced data. SENSORS, 15(7), 15974–15987. https://doi.org/10.3390/s150715974
Chicago author-date
Semanjski, Ivana, and Sidharta Gautama. 2015. “Smart City Mobility Application: Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.” Edited by Antonio Puliafito, Symeon Papavassiliou, and Dario Bruneo. SENSORS 15 (7): 15974–87. https://doi.org/10.3390/s150715974.
Chicago author-date (all authors)
Semanjski, Ivana, and Sidharta Gautama. 2015. “Smart City Mobility Application: Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.” Ed by. Antonio Puliafito, Symeon Papavassiliou, and Dario Bruneo. SENSORS 15 (7): 15974–15987. doi:10.3390/s150715974.
Vancouver
1.
Semanjski I, Gautama S. Smart city mobility application: gradient boosting trees for mobility prediction and analysis based on crowdsourced data. Puliafito A, Papavassiliou S, Bruneo D, editors. SENSORS. 2015;15(7):15974–87.
IEEE
[1]
I. Semanjski and S. Gautama, “Smart city mobility application: gradient boosting trees for mobility prediction and analysis based on crowdsourced data,” SENSORS, vol. 15, no. 7, pp. 15974–15987, 2015.
@article{6883719,
  abstract     = {{Mobility management represents one of the most important parts of the smart city concept. The way we travel, at what time of the day, for what purposes and with what transportation modes, have a pertinent impact on the overall quality of life in cities. To manage this process, detailed and comprehensive information on individuals’ behaviour is needed as well as effective feedback/communication channels. In this article, we explore the applicability of crowdsourced data for this purpose. We apply a gradient boosting trees algorithm to model individuals’ mobility decision making processes (particularly concerning what transportation mode they are likely to use). To accomplish this we rely on data collected from three sources: a dedicated smartphone application, a geographic information systems-based web interface and weather forecast data collected over a period of six months. The applicability of the developed model is seen as a potential platform for personalized mobility management in smart cities and a communication tool between the city (to steer the users towards more sustainable behaviour by additionally weighting preferred suggestions) and users (who can give feedback on the acceptability of the provided suggestions, by accepting or rejecting them, providing an additional input to the learning process).}},
  author       = {{Semanjski, Ivana and Gautama, Sidharta}},
  editor       = {{Puliafito, Antonio and Papavassiliou, Symeon and Bruneo, Dario}},
  issn         = {{1424-8220}},
  journal      = {{SENSORS}},
  keywords     = {{mobility management,gradient boosted trees,crowdsourcing,smart city,modelling mobility decision making,CITIES}},
  language     = {{eng}},
  number       = {{7}},
  pages        = {{15974--15987}},
  publisher    = {{MDPI}},
  title        = {{Smart city mobility application: gradient boosting trees for mobility prediction and analysis based on crowdsourced data}},
  url          = {{http://doi.org/10.3390/s150715974}},
  volume       = {{15}},
  year         = {{2015}},
}
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