Sensors (Basel, Switzerland), 2020-04, Vol.20 (8), p.2342
2020
Details
- Autor(en) / Beteiligte
- Titel
- Real-Time Queue Length Detection with Roadside LiDAR Data
- Ist Teil von
- Sensors (Basel, Switzerland), 2020-04, Vol.20 (8), p.2342
- Ort / Verlag
- Switzerland: MDPI AG
- Erscheinungsjahr
- 2020
- Link zum Volltext
- Quelle
- Free E-Journal (出版社公開部分のみ）
- Beschreibungen/Notizen
- Real-time queue length information is an important input for many traffic applications. This paper presents a novel method for real-time queue length detection with roadside LiDAR data. Vehicles on the road were continuously tracked with the LiDAR data processing procedures (including background filtering, point clustering, object classification, lane identification and object association). A detailed method to identify the vehicle at the end of the queue considering the occlusion issue and package loss issue was documented in this study. The proposed method can provide real-time queue length information. The performance of the proposed queue length detection method was evaluated with the ground-truth data collected from three sites in Reno, Nevada. Results show the proposed method can achieve an average of 98% accuracy at the six investigated sites. The errors in the queue length detection were also diagnosed.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 1424-8220eISSN: 1424-8220DOI: 10.3390/s20082342Titel-ID: cdi_doaj_primary_oai_doaj_org_article_58c40335c38840f39703ad32ada35a29
- Format
- –
- Schlagworte
- Accuracy, Algorithms, Clustering, Data collection, Data processing, Global positioning systems, GPS, Markov analysis, Methods, Neural networks, Occlusion, queue length, Queues, Queuing theory, Real time, Roads & highways, roadside sensor, Roadsides, Sensors, vehicle detection, Vehicle identification, Vehicles, Wavelet transforms