Detecting transportation modes using smartphone data and GIS information: evaluating alternative algorithms for an integrated smartphone-based travel diary imputation

ABSTRACT

Smartphones with embedded Global Positioning System (GPS) technology provide an opportunity to passively collect individuals’ travel trajectory data, which can be utilized to identify critical aspects of travel behavior such as transport modes. This paper presents a mode detection algorithm developed to infer modes from smartphone GPS data. The algorithm is designed to classify single-mode trip segments into one of six modes. There are 31 features selected for the algorithm, which are divided into two categories: general features and transit-specific features. General features are needed to classify all available modes. Transit-specific features representing proximity to the nearest transit lines enhance the classification of the three transit modes. Four tree-based ensemble learning models are evaluated: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). The results show that LightGBM performs the best in terms of detection accuracy and computation time.

KEYWORDS:

• Mode detection
• machine learning
• smartphone-based household travel survey
• GPS technology
• lightgbm
• tree-based ensemble learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Government of Ontario, Ministry of Transportation (CA).