Estimating traffic conditions from smart work zone systems
This article evaluates the effectiveness of sensor network systems for work zone traffic estimation. The comparative analysis is performed on a work zone modeled in microsimulation and calibrated with field data from an Illinois work zone. Realistic error models are used to generate noisy measurements corresponding to Doppler radar sensors, remote traffic microwave sensors (RTMSs), and low energy radars. The velocity, queue length, and travel time are estimated with three algorithms based on (i) interpolation, (ii) spatio-temporal smoothing, and (iii) a flow model–based Kalman filter. A total of 396 sensor and algorithm configurations are evaluated and the accuracy of the resulting traffic estimates is compared to the true traffic state from the microsimulation. The nonlinear Kalman filter provides up to 30% error reduction over other velocity estimators when the RTMS sensor spacing exceeds two miles, and generally offers the best performance for queue and travel time estimation.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/15472450
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Supplemental Notes:
- Abstract reprinted with permission of Taylor & Francis.
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Authors:
- Li, Yanning
- Mart�nez Mori, Juan C
- Work, Daniel B
- Publication Date: 2018-11
Language
- English
Media Info
- Media Type: Web
- Features: References;
- Pagination: pp 490-502
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Serial:
- Journal of Intelligent Transportation Systems
- Volume: 22
- Issue Number: 6
- Publisher: Taylor & Francis
- ISSN: 1547-2450
- EISSN: 1547-2442
- Serial URL: http://www.tandfonline.com/loi/gits20
Subject/Index Terms
- TRT Terms: Microsimulation; Traffic characteristics; Traffic estimation; Traffic queuing; Travel time; Velocity; Work zones
- Geographic Terms: Illinois
- Subject Areas: Highways; Operations and Traffic Management; Planning and Forecasting;
Filing Info
- Accession Number: 01690349
- Record Type: Publication
- Files: TRIS
- Created Date: Dec 31 2018 9:05AM