Smart City Gnosys
Smart city article details
| Title | An Interpretable Detection Of Transportation Mode Considering Gps, Spatial, And Contextual Data Based On Ensemble Machine Learning |
|---|---|
| ID_Doc | 8615 |
| Authors | Sowlati S.; Ali Abbaspour R.; Chehreghan A. |
| Year | 2023 |
| Published | Explainable Artificial Intelligence for Intelligent Transportation Systems |
| DOI | http://dx.doi.org/10.1201/9781003324140-10 |
| Abstract | Constructing and developing intelligent transportation is one of the requirements for smart cities. Transportation mode detection is a prerequisite for many transportation structures and scheduling. Nowadays, with the development of GPS technology and the use of this system in tools such as smartphones as well as the existence of open-source data, vast sources of data can be accessed. This study aims to predict the transportation modes of the bike, bus, car, train, and walking by GPS recorded points, spatial data, and contextual data. Until now, we have found no study that has used kinematic, spatial, and contextual group features together while handling the interpretability of transportation modes based on the feature group and checking each important feature from the separate classification models. In the proposed approach of this paper, after extracting all three group features, a hybrid feature selection is implemented to reduce the complexity. Then, RF, GB, XGBoost, CatBoost, LightGBoost, SVM, and KNN classifications are analyzed by F-Score evaluation and the SHAP method. Finally, for the first time in transportation mode detection, supervised machine learning classification models are applied along with ensemble learning techniques, including Stacking, MVE, and WAPVE. This allows to avoid one-sided prediction and increases reliability. After the Implementation of the proposed methods, it is found that using spatial features along with kinematic features increases the accuracy of forecasting all transportation modes. Moreover, using contextual and kinematic features together have the most significant impact on car, bus, and bike modes. Employing a feature selection algorithm reduces training time in all classification models and increases the F-score in the KNN, SVM, and CatBoost models relative to all the features. Among the models, the stacking algorithm for the bike, train, and walking modes, as well as the LightGBoost algorithm for the bus and car modes, shows a higher F-score. A stacking algorithm with an F-score weighted average of 91.32% and the micro average of 91.42% is the best classification model for predicting all the transportation modes. © 2024 selection and editorial matter, Amina Adadi & Afaf Bouhoute. |
| Author Keywords |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
58921  | 0.89 | Zhagyparova K.; Bader A.; Kouzayha N.; Elsawy H.; Al-Naffouri T. | Transportation Mode Recognition Based On Cellular Network Data | 2023 IEEE International Conference on Smart Mobility, SM 2023 (2023) |
| 44349 | 0.886 | Karite A.; Gentner C. | Real-Time Detection Of Transport Modes And Movement States Via Smartphone Data | 2025 IEEE/ION Position, Location and Navigation Symposium, PLANS 2025 (2025) |
| 17273 | 0.878 | Zhu Y.; Luo H.; Guo S.; Zhao F. | Data Mining For Transportation Mode Recognition From Radio-Data | UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers (2021) |
| 2581 | 0.874 | Yang Z.; Xie Z.; Hou Z.; Ji C.; Deng Z.; Li R.; Wu X.; Zhao L.; Ni S. | A Method Of User Travel Mode Recognition Based On Convolutional Neural Network And Cell Phone Signaling Data | Electronics (Switzerland), 12, 17 (2023) |
| 14989 | 0.865 | Bhosle N.; Jagtap J.; Shivakrishna D. | Comparative Analysis Of Different Machine Learning Techniques For Travel Mode Prediction | 2024 Smart Cities Symposium Prague, SCSP 2024 - Proceedings (2024) |
| 26385 | 0.862 | Cavalcante I.C.; Meneguette R.I.; Torres R.H.; Mano L.Y.; Gonçalves V.P.; Ueyama J.O.; Pessin G.; Amvame Nze G.D.; Rocha Filho G.P. | Federated System For Transport Mode Detection | Energies, 15, 23 (2022) |
| 25549 | 0.853 | Kong X.; Wang K.; Hou M.; Xia F.; Karmakar G.; Li J. | Exploring Human Mobility For Multi-Pattern Passenger Prediction: A Graph Learning Framework | IEEE Transactions on Intelligent Transportation Systems, 23, 9 (2022) |
| 32637 | 0.851 | Alazeb A.; Khan D.; Jalal A. | Intelligent Transportation Activity Recognition Using Deep Belief Network | Interdisciplinary Conference on Electrics and Computer, INTCEC 2024 (2024) |
| 30695 | 0.851 | Pandey M.K.; Saini A.; Subbiah K.; Chintalapudi N.; Battineni G. | Improved Carpooling Experience Through Improved Gps Trajectory Classification Using Machine Learning Algorithms | Information (Switzerland), 13, 8 (2022) |