Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies
This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and sev...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10356725/ |
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| author | Lisa Kessler Klaus Bogenberger |
| author_facet | Lisa Kessler Klaus Bogenberger |
| author_sort | Lisa Kessler |
| collection | DOAJ |
| description | This paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from loop detectors, travel time measurements from Bluetooth sensors, and floating car data (FCD) collected from probe vehicles. Each combination of congestion pattern and detection technology is thoroughly examined and evaluated in terms of its capability and suitability for identifying specific traffic congestion patterns. For our experimental site, we selected the freeway A9 in Germany, which spans a length of <inline-formula> <tex-math notation="LaTeX">$\mathrm {157~km}$ </tex-math></inline-formula>. Our findings reveal that Bluetooth sensors, which record travel times between two locations, are barely suited for detecting short traffic incidents such as Jam Waves due to their downstream detection direction, contrasting with the upstream congestion propagation. Segment-based speed calculations prove more effective in identifying significant congestion events. FCD tend to recognize Stop and Go patterns more frequently than loop detectors but often underestimate severe congestion due to their sensitivity to penetration rates and data availability. |
| format | Article |
| id | doaj-art-e0f1ccb23b3f4cefa2680cddec5192a6 |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-e0f1ccb23b3f4cefa2680cddec5192a62025-01-24T00:02:32ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-015294010.1109/OJITS.2023.334163110356725Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor TechnologiesLisa Kessler0https://orcid.org/0000-0002-4100-4812Klaus Bogenberger1https://orcid.org/0000-0003-3868-9571Department of Mobility Systems Engineering, Chair of Traffic Engineering and Control, TUM School of Engineering and Design, Technical University of Munich, Munich, GermanyDepartment of Mobility Systems Engineering, Chair of Traffic Engineering and Control, TUM School of Engineering and Design, Technical University of Munich, Munich, GermanyThis paper investigates the detection rate of various freeway congestion patterns and compares them across different traffic sensor technologies. Congestion events can be categorized into multiple types, ranging from short traffic disruptions (referred to as Jam Wave) to Stop and Go patterns and severe congestion scenarios like Wide Jam. We analyze multiple traffic data sets, including speed data from loop detectors, travel time measurements from Bluetooth sensors, and floating car data (FCD) collected from probe vehicles. Each combination of congestion pattern and detection technology is thoroughly examined and evaluated in terms of its capability and suitability for identifying specific traffic congestion patterns. For our experimental site, we selected the freeway A9 in Germany, which spans a length of <inline-formula> <tex-math notation="LaTeX">$\mathrm {157~km}$ </tex-math></inline-formula>. Our findings reveal that Bluetooth sensors, which record travel times between two locations, are barely suited for detecting short traffic incidents such as Jam Waves due to their downstream detection direction, contrasting with the upstream congestion propagation. Segment-based speed calculations prove more effective in identifying significant congestion events. FCD tend to recognize Stop and Go patterns more frequently than loop detectors but often underestimate severe congestion due to their sensitivity to penetration rates and data availability.https://ieeexplore.ieee.org/document/10356725/Congestion patternsspeed reconstructiontraffic state estimation |
| spellingShingle | Lisa Kessler Klaus Bogenberger Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies IEEE Open Journal of Intelligent Transportation Systems Congestion patterns speed reconstruction traffic state estimation |
| title | Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies |
| title_full | Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies |
| title_fullStr | Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies |
| title_full_unstemmed | Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies |
| title_short | Detection Rate of Congestion Patterns Comparing Multiple Traffic Sensor Technologies |
| title_sort | detection rate of congestion patterns comparing multiple traffic sensor technologies |
| topic | Congestion patterns speed reconstruction traffic state estimation |
| url | https://ieeexplore.ieee.org/document/10356725/ |
| work_keys_str_mv | AT lisakessler detectionrateofcongestionpatternscomparingmultipletrafficsensortechnologies AT klausbogenberger detectionrateofcongestionpatternscomparingmultipletrafficsensortechnologies |