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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.


Distribution of trips (kilometres) made by mode (Left) and time of day (Right).
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sensors-15-15974-f001: Distribution of trips (kilometres) made by mode (Left) and time of day (Right).

Mentions: Based on these data sources over 4000 trips were recorded during a period of six months (Table 1). Considering the distribution of the recorded trips, the least of them were made during the evening hours (after 19 h), whereas in general the most kilometres were travelled by car, followed by foot and bike (Figure 1).


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

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

Distribution of trips (kilometres) made by mode (Left) and time of day (Right).
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15974-f001: Distribution of trips (kilometres) made by mode (Left) and time of day (Right).
Mentions: Based on these data sources over 4000 trips were recorded during a period of six months (Table 1). Considering the distribution of the recorded trips, the least of them were made during the evening hours (after 19 h), whereas in general the most kilometres were travelled by car, followed by foot and bike (Figure 1).

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.