Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning
<p dir="ltr">Traffic flow, number of vehicles passing a particular point over a given period of time, is an essential indicator for evaluating the performance and condition of road networks, detecting congestion, and predicting traffic trends. Accurate and reliable measurement of tra...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513527508631552 |
|---|---|
| author | Fatima Tayeb (17949116) |
| author2 | Hamadi Chihaoui (17949119) Fethi Filali (12646471) |
| author2_role | author author |
| author_facet | Fatima Tayeb (17949116) Hamadi Chihaoui (17949119) Fethi Filali (12646471) |
| author_role | author |
| dc.creator.none.fl_str_mv | Fatima Tayeb (17949116) Hamadi Chihaoui (17949119) Fethi Filali (12646471) |
| dc.date.none.fl_str_mv | 2023-06-20T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3287981 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Bluetooth-Based_Vehicle_Counting_Bridging_the_Gap_to_Ground-Truth_With_Machine_Learning/25205162 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Bluetooth Sensors Road traffic Cameras Transportation Sensor systems Real-time systems Machine learning Traffic control Smart transportation Vehicle count estimation real-time traffic analysis bluetooth-based road sensing smart mobility |
| dc.title.none.fl_str_mv | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Traffic flow, number of vehicles passing a particular point over a given period of time, is an essential indicator for evaluating the performance and condition of road networks, detecting congestion, and predicting traffic trends. Accurate and reliable measurement of traffic flow in urban roads is challenging due to the dynamic nature of intersection signals and comes with high equipment and maintenance cost. WaveTraf is a Bluetooth-based Intelligent Traffic System solution widely deployed in the State of Qatar which detects and monitors the movement of Bluetooth-enabled devices anonymously using their unique MAC addresses. Systems such as WaveTraf allow for real-time, low-cost, scalable and non-intrusive traffic flow measurement; however, they could suffer from low detection and sampling rates leading to uncertain and unreliable estimates. In this research, we investigate various machine learning techniques such as Random Forrest, Support Vector Regression Machines and XGBoost to model the relationship between the ground-truth traffic flow based on video cameras and Bluetooth-based traffic flow. We utilized these techniques to enhance the dependability of Bluetooth-based traffic flow measurements, making it a more desirable and cost-effective solution for real-time traffic flow measurement.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3287981" target="_blank">https://dx.doi.org/10.1109/access.2023.3287981</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_d4f68e98d33a16170df141ade7a605ab |
| identifier_str_mv | 10.1109/access.2023.3287981 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25205162 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine LearningFatima Tayeb (17949116)Hamadi Chihaoui (17949119)Fethi Filali (12646471)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringBluetoothSensorsRoad trafficCamerasTransportationSensor systemsReal-time systemsMachine learningTraffic controlSmart transportationVehicle count estimationreal-time traffic analysisbluetooth-based road sensingsmart mobility<p dir="ltr">Traffic flow, number of vehicles passing a particular point over a given period of time, is an essential indicator for evaluating the performance and condition of road networks, detecting congestion, and predicting traffic trends. Accurate and reliable measurement of traffic flow in urban roads is challenging due to the dynamic nature of intersection signals and comes with high equipment and maintenance cost. WaveTraf is a Bluetooth-based Intelligent Traffic System solution widely deployed in the State of Qatar which detects and monitors the movement of Bluetooth-enabled devices anonymously using their unique MAC addresses. Systems such as WaveTraf allow for real-time, low-cost, scalable and non-intrusive traffic flow measurement; however, they could suffer from low detection and sampling rates leading to uncertain and unreliable estimates. In this research, we investigate various machine learning techniques such as Random Forrest, Support Vector Regression Machines and XGBoost to model the relationship between the ground-truth traffic flow based on video cameras and Bluetooth-based traffic flow. We utilized these techniques to enhance the dependability of Bluetooth-based traffic flow measurements, making it a more desirable and cost-effective solution for real-time traffic flow measurement.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3287981" target="_blank">https://dx.doi.org/10.1109/access.2023.3287981</a></p>2023-06-20T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3287981https://figshare.com/articles/journal_contribution/Bluetooth-Based_Vehicle_Counting_Bridging_the_Gap_to_Ground-Truth_With_Machine_Learning/25205162CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252051622023-06-20T06:00:00Z |
| spellingShingle | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning Fatima Tayeb (17949116) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Bluetooth Sensors Road traffic Cameras Transportation Sensor systems Real-time systems Machine learning Traffic control Smart transportation Vehicle count estimation real-time traffic analysis bluetooth-based road sensing smart mobility |
| status_str | publishedVersion |
| title | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| title_full | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| title_fullStr | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| title_full_unstemmed | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| title_short | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| title_sort | Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Bluetooth Sensors Road traffic Cameras Transportation Sensor systems Real-time systems Machine learning Traffic control Smart transportation Vehicle count estimation real-time traffic analysis bluetooth-based road sensing smart mobility |