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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.


Classification matrix histogram.
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sensors-15-15974-f004: Classification matrix histogram.

Mentions: The classification matrix gives an overview of correctly classified and misclassified values or when the built model successfully predicted which transportation mode the user will select and when not. Figure 4 shows the histogram of the classification matrix, where the highest values on the diagonal of the histogram mean that these transportation modes were correctly classified or that the boosting trees were able to correctly model the user’s decision making process from the given dataset. Table 4 gives a more detailed overview of the classification results. The overall success of the boosted trees model to correctly recognize the transportation mode that user will select in the certain circumstances is 73%. The highest success was obtained for the transportation mode walk, followed by car and bike, while the user made the most of the trips by bike. The highest misclassification occurred between the car and the bike (28 trips), which corresponds to the 20% of all bike trips and the lowest among the walk and bike (4 trips or 6% of all walk trips).


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

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

Classification matrix histogram.
© Copyright Policy
Related In: Results  -  Collection

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

sensors-15-15974-f004: Classification matrix histogram.
Mentions: The classification matrix gives an overview of correctly classified and misclassified values or when the built model successfully predicted which transportation mode the user will select and when not. Figure 4 shows the histogram of the classification matrix, where the highest values on the diagonal of the histogram mean that these transportation modes were correctly classified or that the boosting trees were able to correctly model the user’s decision making process from the given dataset. Table 4 gives a more detailed overview of the classification results. The overall success of the boosted trees model to correctly recognize the transportation mode that user will select in the certain circumstances is 73%. The highest success was obtained for the transportation mode walk, followed by car and bike, while the user made the most of the trips by bike. The highest misclassification occurred between the car and the bike (28 trips), which corresponds to the 20% of all bike trips and the lowest among the walk and bike (4 trips or 6% of all walk trips).

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.