Estimating traffic conditions from smart work zone systems. Issue 6 (2nd November 2018)

• Record Type: Journal Article
• Authors: Li, Yanning; Martínez Mori, Juan C.; Work, Daniel B.
• Is Part Of: Journal of intelligent transportation systems. Volume 22:Issue 6(2018)
• Page Start: 490
• Page End: 502
• Publication Date: 2018-11-02
• Subjects: microsimulation -- smart work zone -- traffic estimation; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2018.1438274
• Languages: English
• ISSNs: 1547-2450

Abstract

ABSTRACT: 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.