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Smart City Mobility Application--Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.

Semanjski I, Gautama S - Sensors (Basel) (2015)

Bottom Line: To manage this process, detailed and comprehensive information on individuals' behaviour is needed as well as effective feedback/communication channels.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Telecommunications and Information Processing, Gent University, St-Pietersnieuwstraat 41, Gent B-9000, Belgium. ivana.semanjski@ugent.be.

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).

No MeSH data available.


(a) An example of the simple tree for the transportation mode bike; (b) An example of the simple tree for the transportation mode walk; (c) An example of the simple tree for the transportation mode car; (d) Average multinomial deviance for boosted trees.
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sensors-15-15974-f002: (a) An example of the simple tree for the transportation mode bike; (b) An example of the simple tree for the transportation mode walk; (c) An example of the simple tree for the transportation mode car; (d) Average multinomial deviance for boosted trees.

Mentions: The first step in building a model was to compute a sequence of (very) simple decision trees, where each successive tree was built for the prediction residuals of the preceding tree. Figure 2 shows examples of some of those simple decision trees that were used in the building process.


Smart City Mobility Application--Gradient Boosting Trees for Mobility Prediction and Analysis Based on Crowdsourced Data.

Semanjski I, Gautama S - Sensors (Basel) (2015)

(a) An example of the simple tree for the transportation mode bike; (b) An example of the simple tree for the transportation mode walk; (c) An example of the simple tree for the transportation mode car; (d) Average multinomial deviance for boosted trees.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541863&req=5

sensors-15-15974-f002: (a) An example of the simple tree for the transportation mode bike; (b) An example of the simple tree for the transportation mode walk; (c) An example of the simple tree for the transportation mode car; (d) Average multinomial deviance for boosted trees.
Mentions: The first step in building a model was to compute a sequence of (very) simple decision trees, where each successive tree was built for the prediction residuals of the preceding tree. Figure 2 shows examples of some of those simple decision trees that were used in the building process.

Bottom Line: To manage this process, detailed and comprehensive information on individuals' behaviour is needed as well as effective feedback/communication channels.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).

View Article: PubMed Central - PubMed

Affiliation: Department of Telecommunications and Information Processing, Gent University, St-Pietersnieuwstraat 41, Gent B-9000, Belgium. ivana.semanjski@ugent.be.

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).

No MeSH data available.