On feature selection and evaluation of transportation mode prediction strategies

TL;DR

This paper investigates feature selection methods for transportation mode prediction, demonstrating that a carefully chosen feature subset can outperform deep learning approaches, with insights into cross-validation impacts.

Contribution

It introduces a framework using wrapper and information retrieval methods for feature selection, achieving better performance than existing deep learning models.

Findings

Wrapper and information retrieval methods effectively select features.

The proposed framework outperforms deep learning approaches.

Random cross-validation yields optimistic performance estimates.

Abstract

Transportation modes prediction is a fundamental task for decision making in smart cities and traffic management systems. Traffic policies designed based on trajectory mining can save money and time for authorities and the public. It may reduce the fuel consumption and commute time and moreover, may provide more pleasant moments for residents and tourists. Since the number of features that may be used to predict a user transportation mode can be substantial, finding a subset of features that maximizes a performance measure is worth investigating. In this work, we explore wrapper and information retrieval methods to find the best subset of trajectory features. After finding the best classifier and the best feature subset, our results were compared with two related papers that applied deep learning methods and the results showed that our framework achieved better performance. Furthermore, two types of cross-validation approaches were investigated, and the performance results show that the random cross-validation method provides optimistic results.