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Transportation Mode Detection Using Learning Methods and Self-Contained Sensors: Review.

Ilhem Gharbi^1,2, Fadoua Taia-Alaoui^1,2,3, Hassen Fourati¹

¹GIPSA-Lab, Univ. Grenoble Alpes, CNRS, Inria, Grenoble INP, 38000 Grenoble, France.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This review explores smartphone-based transportation mode detection (TMD) using inertial measurement units (IMUs). It highlights challenges in data collection and classification, offering insights for improved travel modeling strategies.

Keywords:

classification inertial sensors machine learning smartphones transportation mode detection

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Area of Science:

Transportation Science
Computer Science
Signal Processing

Background:

Increasing traffic congestion necessitates advanced travel modeling.
Smartphones with inertial measurement units (IMUs) offer potential for transportation mode detection (TMD).
Challenges exist in handling diverse sensor data, dataset standardization, and selecting appropriate machine learning methods for TMD.

Purpose of the Study:

To provide an in-depth review of state-of-the-art transportation mode detection (TMD) systems.
To analyze current challenges in real-world data collection and classification for TMD using smartphone IMUs.
To evaluate existing methodologies for travel mode detection, focusing on datasets, sensor data, and feature extraction.

Main Methods:

Comprehensive literature review of recent advancements in TMD systems.
Analysis of datasets, sensor data types (accelerometers, magnetometers, gyroscopes, etc.), and feature extraction techniques.
Evaluation of classification methods applied to smartphone IMU data for travel mode identification.

Main Results:

Identified key challenges in data variety, standardization, and machine learning applicability for smartphone-based TMD.
Highlighted recent datasets and best practices for classification in TMD systems.
Provided an evaluation of current methodologies for detecting travel modes using smartphone IMUs.

Conclusions:

This review offers novel insights into TMD systems by focusing on practical data collection and classification issues.
Researchers can leverage this analysis to address critical problems and challenges in developing effective TMD strategies.
The findings aim to guide future research towards more robust and accurate transportation mode detection using smartphone sensors.