Browsing by Subject "Queuing"

Now showing 1 - 2 of 2
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    Estimating and Measuring Arterial Travel Time and Delay
    (Intelligent Transportation Systems Institute, Center for Transportation Studies, University of Minnesota, 2012-08) Liu, Henry X.; Wu, Xinkai
    To estimate arterial travel time/delay, the key element is to estimate intersection queue length, since travel time, delay, and level of services can be easily derived from queue length information. In this study, we developed a new traffic flow model, named shockwave profile model (SPM), to describe queuing dynamics for congested arterial networks. Taking advantage of the fact that traffic states within a congested link can be simplified as free-flow, saturated, and jammed conditions, the SPM simulates traffic dynamics by analytically deriving the trajectories of four major shockwaves. This model is particularly suitable for simulating congested traffic especially with queue spillover. In the SPM, a novel approach is proposed as part of the SPM, in which queue spillover is treated as either extending a red phase or creating new cycles. Since only the essential features, i.e. queue build-up and dissipation, are considered, the SPM significantly reduces the computational load and improves the numerical efficiency. We further validated the SPM using real-world traffic signal data collected from a major arterial in the Twin Cities. The results clearly demonstrate its effectiveness and accuracy. This model can be applied to estimate arterial travel time and delay and optimize signal timing in real time.
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    Evaluation and Refinement of Minnesota Queue Warning Systems
    (Minnesota Department of Transportation, 2023-03) Hourdos, John; Robbennolt, Jake
    This study evaluates the first and a second implementations of the MN-QWARN queue warning algorithm developed by Hourdos et al. (1). This algorithm was developed to detect specific crash prone conditions created by traffic oscillations (shockwaves) on freeway systems. The MN-QWARN system was specifically calibrated for the freeway studied in Hourdos et al. (1) and was moved to a new location with minimal calibration. This evaluation found that the right-side model had a detection rate of 25% and a false alarm rate of 36%. The left-side model had a detection rate of 64% and a false alarm rate of 23%. We also note high over-warning rates on both lanes. Based on these findings, we recommend recalibrating the MN-QWARN algorithm at this location to examine improvements in performance.