Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities
Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack o...
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| Format: | Article |
| Language: | English |
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Wiley
2018-01-01
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| Series: | Journal of Advanced Transportation |
| Online Access: | http://dx.doi.org/10.1155/2018/9317291 |
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| _version_ | 1849696042480566272 |
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| author | Volker Lücken Nils Voss Julien Schreier Thomas Baag Michael Gehring Matthias Raschen Christian Lanius Rainer Leupers Gerd Ascheid |
| author_facet | Volker Lücken Nils Voss Julien Schreier Thomas Baag Michael Gehring Matthias Raschen Christian Lanius Rainer Leupers Gerd Ascheid |
| author_sort | Volker Lücken |
| collection | DOAJ |
| description | Traffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations. |
| format | Article |
| id | doaj-art-c97a87d00afd43e2bc55236118d75663 |
| institution | DOAJ |
| issn | 0197-6729 2042-3195 |
| language | English |
| publishDate | 2018-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Advanced Transportation |
| spelling | doaj-art-c97a87d00afd43e2bc55236118d756632025-08-20T03:19:34ZengWileyJournal of Advanced Transportation0197-67292042-31952018-01-01201810.1155/2018/93172919317291Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart CitiesVolker Lücken0Nils Voss1Julien Schreier2Thomas Baag3Michael Gehring4Matthias Raschen5Christian Lanius6Rainer Leupers7Gerd Ascheid8Institute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyInstitute for Communication Technologies and Embedded Systems (ICE), RWTH Aachen University, Aachen, GermanyTraffic routing is a central challenge in the context of urban areas, with a direct impact on personal mobility, traffic congestion, and air pollution. In the last decade, the possibilities for traffic flow control have improved together with the corresponding management systems. However, the lack of real-time traffic flow information with a city-wide coverage is a major limiting factor for an optimum operation. Smart City concepts seek to tackle these challenges in the future by combining sensing, communications, distributed information, and actuation. This paper presents an integrated approach that combines smart street lamps with traffic sensing technology. More specifically, infrastructure-based ultrasonic sensors, which are deployed together with a street light system, are used for multilane traffic participant detection and classification. Application of these sensors in time-varying reflective environments posed an unresolved problem for many ultrasonic sensing solutions in the past and therefore widely limited the dissemination of this technology. We present a solution using an algorithmic approach that combines statistical standardization with clustering techniques from the field of unsupervised learning. By using a multilevel communication concept, centralized and decentralized traffic information fusion is possible. The evaluation is based on results from automotive test track measurements and several European real-world installations.http://dx.doi.org/10.1155/2018/9317291 |
| spellingShingle | Volker Lücken Nils Voss Julien Schreier Thomas Baag Michael Gehring Matthias Raschen Christian Lanius Rainer Leupers Gerd Ascheid Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities Journal of Advanced Transportation |
| title | Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities |
| title_full | Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities |
| title_fullStr | Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities |
| title_full_unstemmed | Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities |
| title_short | Density-Based Statistical Clustering: Enabling Sidefire Ultrasonic Traffic Sensing in Smart Cities |
| title_sort | density based statistical clustering enabling sidefire ultrasonic traffic sensing in smart cities |
| url | http://dx.doi.org/10.1155/2018/9317291 |
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