An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus
Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service; however, it is challenging....
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10971389/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850144843985059840 |
|---|---|
| author | Tarak Nandy Raenu Kolandaisamy Rafidah Md Noor Sananda Bhattacharyya |
| author_facet | Tarak Nandy Raenu Kolandaisamy Rafidah Md Noor Sananda Bhattacharyya |
| author_sort | Tarak Nandy |
| collection | DOAJ |
| description | Vehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service; however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to lower accuracy and mismatched pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of its use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions. |
| format | Article |
| id | doaj-art-0b591b71ccfc4125ba5dc65d8df98d77 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-0b591b71ccfc4125ba5dc65d8df98d772025-08-20T02:28:15ZengIEEEIEEE Access2169-35362025-01-0113782907830210.1109/ACCESS.2025.356278510971389An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle BusTarak Nandy0https://orcid.org/0000-0002-6370-0640Raenu Kolandaisamy1https://orcid.org/0000-0002-6712-0459Rafidah Md Noor2https://orcid.org/0000-0001-6266-2390Sananda Bhattacharyya3Institute of Computer Science and Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, MalaysiaInstitute of Computer Science and Digital Innovation (ICSDI), UCSI University, Kuala Lumpur, MalaysiaDepartment of Computer Systems and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, MalaysiaDepartment of Information Technology, Maldives Business School, Malé, MaldivesVehicle location prediction and the use of vehicle location tracking are increasingly important topics of discussion among connected vehicle researchers. Location tracking for mobile users is essential due to the correlated services and to improve the quality of service; however, it is challenging. On the other hand, the stateless predictive model is unsuitable due to lower accuracy and mismatched pattern analysis. This paper proposed a vehicle location prediction model using machine learning based on a case study of a campus shuttle bus scenario. Firstly, this paper comprehensively analyzes the recent research on vehicle location prediction models and provides a complete taxonomy. The proposed model uses the Support Vector Regression (SVR) based vehicle location predictive model in the implementation phase. In addition, a significant time-series predictive model, ARIMA, is configured and tested. Moreover, the seasonal ARIMA model is experimented with to predict the location of a mobile vehicle. The whole experiment is performed on a real dataset based on the university shuttle bus service. The results of the experiments, analysis, and discussions on the proposed model show the accuracy and effectiveness of its use on others. As a result, seasonal ARIMA outperformed ARIMA and SVR infractions.https://ieeexplore.ieee.org/document/10971389/Location predictionintelligent transportation systemsmachine learningSVRARIMASARIMA |
| spellingShingle | Tarak Nandy Raenu Kolandaisamy Rafidah Md Noor Sananda Bhattacharyya An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus IEEE Access Location prediction intelligent transportation systems machine learning SVR ARIMA SARIMA |
| title | An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus |
| title_full | An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus |
| title_fullStr | An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus |
| title_full_unstemmed | An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus |
| title_short | An Adaptive Vehicle Location Prediction Using Machine Learning: A Case Study of Campus Shuttle Bus |
| title_sort | adaptive vehicle location prediction using machine learning a case study of campus shuttle bus |
| topic | Location prediction intelligent transportation systems machine learning SVR ARIMA SARIMA |
| url | https://ieeexplore.ieee.org/document/10971389/ |
| work_keys_str_mv | AT taraknandy anadaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT raenukolandaisamy anadaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT rafidahmdnoor anadaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT sanandabhattacharyya anadaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT taraknandy adaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT raenukolandaisamy adaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT rafidahmdnoor adaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus AT sanandabhattacharyya adaptivevehiclelocationpredictionusingmachinelearningacasestudyofcampusshuttlebus |