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

Ivana Semanjski¹, Sidharta Gautama²

¹Department of Telecommunications and Information Processing, Gent University, St-Pietersnieuwstraat 41, Gent B-9000, Belgium. ivana.semanjski@ugent.be.

Sensors (Basel, Switzerland)

|July 8, 2015

Summary

Crowdsourced data, gathered via a smartphone app and web interface, can model urban travel choices. This enables personalized mobility management and promotes sustainable transportation in smart cities.

Area of Science:

Urban planning and transportation science
Data science and artificial intelligence

Background:

Effective urban mobility management is crucial for smart city quality of life.
Understanding individual travel behavior requires detailed data and communication channels.

Purpose of the Study:

To explore the use of crowdsourced data for modeling individual mobility decisions.
To develop a platform for personalized mobility management in smart cities.

Main Methods:

Applied a gradient boosting trees algorithm to predict transportation mode choice.
Utilized data from a smartphone app, a GIS-based web interface, and weather forecasts.
Collected data over a six-month period.

Main Results:

Successfully modeled individual mobility decision-making processes.
Demonstrated the potential of crowdsourced data for understanding travel behavior.

Conclusions:

The developed model can serve as a platform for personalized smart city mobility management.
Facilitates communication between city authorities and users, encouraging sustainable travel choices.

Keywords:

crowdsourcing gradient boosted trees mobility management modelling mobility decision making smart city

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