Vehicle loop detectors or other equipment installed on highway sections are commonly used for monitoring traffic flow conditions on road networks. For operational analysis, it is essential to be able to distinguish low levels of service due to over-saturated conditions from those caused by extraordinary events such as incidents. In the case of incidents, prompt responses are crucial for activating any required countermeasures, such as rescue activation or traffic detours. Automatic Incident Detection methods for basic freeway segments are widely reported in the literature, but their application to freeway ramp merging zones is limited. This work introduces a control system which can identify incidents from vehicle loop detector data on freeway ramp merging zones. The system was developed with fuzzy logic concepts and calibrated with data from micro-simulation experiments. The main finding of this study is that the detection system, despite its simplicity, shows excellent False Alarm Rate (FAR) and satisfactory Detection Rate (DR) and Mean Time To Detection (MTTD), generally better than those obtained with the traditional California#7 comparative algorithm.

Automatic incident detection on freeway ramp junctions. A fuzzy logic-based system using loop detector data

Rossi, Riccardo;Gastaldi, Massimiliano;Gecchele, Gregorio
2018

Abstract

Vehicle loop detectors or other equipment installed on highway sections are commonly used for monitoring traffic flow conditions on road networks. For operational analysis, it is essential to be able to distinguish low levels of service due to over-saturated conditions from those caused by extraordinary events such as incidents. In the case of incidents, prompt responses are crucial for activating any required countermeasures, such as rescue activation or traffic detours. Automatic Incident Detection methods for basic freeway segments are widely reported in the literature, but their application to freeway ramp merging zones is limited. This work introduces a control system which can identify incidents from vehicle loop detector data on freeway ramp merging zones. The system was developed with fuzzy logic concepts and calibrated with data from micro-simulation experiments. The main finding of this study is that the detection system, despite its simplicity, shows excellent False Alarm Rate (FAR) and satisfactory Detection Rate (DR) and Mean Time To Detection (MTTD), generally better than those obtained with the traditional California#7 comparative algorithm.
978-3-319-57104-1
978-3-319-57105-8
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3248829
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