Academic Journal
Improved Carpooling Experience through Improved GPS Trajectory Classification Using Machine Learning Algorithms
| العنوان: | Improved Carpooling Experience through Improved GPS Trajectory Classification Using Machine Learning Algorithms |
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| المؤلفون: | Manish Kumar Pandey, Anu Saini, Karthikeyan Subbiah, Nalini Chintalapudi, Gopi Battineni |
| المصدر: | Information, Vol 13, Iss 8, p 369 (2022) |
| بيانات النشر: | MDPI AG, 2022. |
| سنة النشر: | 2022 |
| المجموعة: | LCC:Information technology |
| مصطلحات موضوعية: | carpooling, GPS Trajectory, SMAC, Random Forest, feature ranking, SDG 9, Information technology, T58.5-58.64 |
| الوصف: | Globally, smart cities, infrastructure, and transportation have led to a rise in vehicle numbers, resulting in an increasing number of problems. This includes problems such as air pollution, noise pollution, high energy consumption, and people’s health. A viable solution to these problems is carpooling, which involves sharing vehicles between people going to the same location. As carpooling solutions become more popular, they need to be implemented efficiently. Data analytics can help people make informed decisions when selecting a ride (Car or Bus). We applied machine learning algorithms to select the desired ride (Car or Bus) and used feature ranking algorithms to identify the foremost traits for selecting the desired ride. Based on the performance evaluation metric, 11 classifiers were used for the experiment. In terms of selecting the desired ride, Random Forest performs best. Using ten-fold cross-validation, we obtained a sensitivity of 87.4%, a specificity of 73.7%, an accuracy of 81.0%, a sensitivity of 90.8%, a specificity of 77.6%, and an accuracy of 84.7% using leave-one-out cross-validation. To identify the most favorable characteristics of the Ride (Car or Bus), the recursive elimination of features algorithm was applied. By identifying the factors contributing to users’ experience, the service providers will be able to rectify those factors to increase business. It has been determined that the weather can make or break the user experience. This model will be used to quantify and map intrinsic and extrinsic sentiments of the people and their interactions with locality, socio-economic conditions, climate, and environment. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 13080369 2078-2489 |
| Relation: | https://www.mdpi.com/2078-2489/13/8/369; https://doaj.org/toc/2078-2489 |
| DOI: | 10.3390/info13080369 |
| URL الوصول: | https://doaj.org/article/867073e1d0b941bf8bd6897c2e319258 |
| رقم الانضمام: | edsdoj.867073e1d0b941bf8bd6897c2e319258 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 13080369 20782489 |
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| DOI: | 10.3390/info13080369 |