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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.


Predictor variables importance plot.
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sensors-15-15974-f003: Predictor variables importance plot.

Mentions: Next to the standard error values, which serve as an indication of the overall model’s quality an pertinent insight into the decision making process is the calculated importance of predictors (Figure 3). The predictor importance value shows what predictors influenced the decision about the selection of the transportation mode the most. One can see that the decision about the transportation mode for any given trip and the selected individual is mainly based upon the information on the location, followed by the indication of the starting time for the trip. Correlation analysis gave the highest (and statistically significant) value (0.328828) for evening hours, meaning that this factor has a high influence on the decision about what transportation mode to choose. On the other hand, for the city, this can indicate that in the evening hours fewer public transportation lines, at certain locations, limit the mobility options and therefore result in a less sustainable mobility behaviour (usage of the car).


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

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

Predictor variables importance plot.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15974-f003: Predictor variables importance plot.
Mentions: Next to the standard error values, which serve as an indication of the overall model’s quality an pertinent insight into the decision making process is the calculated importance of predictors (Figure 3). The predictor importance value shows what predictors influenced the decision about the selection of the transportation mode the most. One can see that the decision about the transportation mode for any given trip and the selected individual is mainly based upon the information on the location, followed by the indication of the starting time for the trip. Correlation analysis gave the highest (and statistically significant) value (0.328828) for evening hours, meaning that this factor has a high influence on the decision about what transportation mode to choose. On the other hand, for the city, this can indicate that in the evening hours fewer public transportation lines, at certain locations, limit the mobility options and therefore result in a less sustainable mobility behaviour (usage of the car).

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.