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.
Estimating traffic conditions from smart work zone systems
Journal of Intelligent Transportation Systems ; 22 , 6 ; 490-502
2018-11-02
13 pages
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
NTIS | 1979
|Online Contents | 1998
Synchronized work zone traffic management systems and methods
Europäisches Patentamt | 2023
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