Transportation Mode Recognition using Smartphone Sensor Data

1. Transportation Mode Recognition using Smartphone Sensor Data Arash Jahangiri & Hesham Rakha Presented by Hesham Rakha Samuel Reynolds Professor of Engineering, CEE Director of the Center for Sustainable Mobility, VTTI

2. Outline • Introduction • Literature Review • Data Collection • Methodology • Results • Conclusions VTTI | Center for Sustainable Mobility

3. Introduction • Objective: • Develop classifiers to identify transportation modes • Modes: • Car, Bus, Bike, Run, Walk • Methods: • Supervised machine learning techniques • Data: • Obtained from smartphone sensors • Developed a custom data acquisition system VTTI | Center for Sustainable Mobility

4. Introduction • Applications • Transportation Planning • Traditional Approach: Questionnaires/Travel Diaries/ Telephone Interviews • Environmental Applications • Carbon footprint / health monitoring • Safety Applications • Incorporating mode information into crash prediction models VTTI | Center for Sustainable Mobility

5. Literature Review • Methods Applied • Artificial Intelligence (AI) tools • Fuzzy Expert Systems, Decision Trees, Bayesian Networks, Support Vector Machine (SVM), etc. • Statistical Methods • Supporting Techniques • GIS maps • Discrete Hidden Markov Models, Bootstrap aggregating VTTI | Center for Sustainable Mobility

6. Literature Review VTTI | Center for Sustainable Mobility

7. Literature Review • Number of classes (3 - 8) • Sensor Data: Accelerometer/ GPS • GIS maps • Different motorized • Device Positioning • Window size (1 - 30 seconds) VTTI | Driving Transportation with Technology

8. Unique Contributions • Considered both motorized and non-motorized modes. • Did not depend on device positioning. • Did not use the information from GPS due to GPS sensor limitations. • Used data from gyroscope, accelerometer and rotation vector sensors. • Had travelers collect car and bus data on different road types with different speed limits. VTTI | Center for Sustainable Mobility

9. Unique Contributions • Had travelers collect data for situations similar to traffic jam conditions. • Applied all common machine learning procedures: • Complete model selection, regularization, feature selection, and feature scaling. • Created and assessed a large number of features. • Created the features based on statistical measures of dispersion as well as derivatives to incorporate feature time dependency. VTTI | Driving Transportation with Technology

10. Data Collection • Developed Smartphone App • Ten individuals / two different android phones • Car, bicycle, bus, walk, and run • About 25 hours of data • Sensors: Accelerometer, Gyroscope, and Rotation Vector • Preprocessing • Data Synchronization • Interpolation & resampling VTTI | Center for Sustainable Mobility

11. Methodology • Methods: • Support Vector Machine (SVM) • Tree-based Methods • K-Nearest Neighbor (K-NN) • Feature Selection: • Minimum Redundancy Maximum Relevance(mRMR) • Model Selection: • Five-fold Cross-Validation • Out-of-bag error for bagging and random forest methods VTTI | Center for Sustainable Mobility

12. Methodology – SVM • Large margin classifier • Single SVM model • Ensemble of SVM models VTTI | Center for Sustainable Mobility

13. Methodology – Tree based Models • Single Tree • Bagging : Ensemble of trees / all variables used in each tree • Random Forest: Ensemble of trees / restricted number of variables used in each tree • Criteria used to choose the best split at each node • Cross-Entropy. VTTI | Center for Sustainable Mobility

14. Methodology - KNN • Classifies test observations based on classes of the K-nearest neighbors VTTI | Center for Sustainable Mobility

15. Methodology – Feature Selection • Measures used to create features: VTTI | Driving Transportation with Technology

16. Methodology – Feature Selection • mRMR Used for feature selection • Maximize the relevance between the feature and the target class • Minimize the redundancy between that feature and the already selected features VTTI | Center for Sustainable Mobility

17. Results – Model Selection Number of neighbors KNN Regularization SVM Gaussian parameter SVM Number of features RF Number of trees Bag, RF VTTI | Center for Sustainable Mobility

18. Results - Comparison • Overall Accuracy: • Calculated by dividing the total number of correct detections by the total number of test data. • F-Score: • Combined measure of the Recall and the Precision • Youden’s index: • A measure to assess the ability of a model to avoid failure • Discriminant power: • Shows how well a model discriminates between different classes VTTI | Center for Sustainable Mobility

19. Results - Comparison • Overall Accuracy VTTI | Driving Transportation with Technology

20. Results – Feature Importance • Identified important features • Based on Mean Decrease Accuracy & Mean Decrease Gini VTTI | Center for Sustainable Mobility

21. Conclusions • Developed smartphone app to obtain sensor data • Used Accelerometer, Gyroscope, & Rotation Vector sensors • Transportation modes: Bike, Car, Walk, Run, and Bus • A time window of one second • Applied machine learning to develop detection models • Most difficult modes to distinguish: Car and Bus (motorized modes) • Best overall performance with Random Forest • SVM outperformed the RF in certain modes (walk and run) • Selected 80 features using mRMR, of which top 20 were identified VTTI | Center for Sustainable Mobility

22. Future Recommendations • Adding more data • Applying approaches to examine the data as a sequence • Considering other transportation modes (e.g. metro) • Conducting error analysis • incorporate that knowledge into the models to enhance the detection performance VTTI | Driving Transportation with Technology

23. Thank you! Questions/Comments ? VTTI | Driving Transportation with Technology