Robust vehicle count estimation on urban signalized links

Horváth, Márton Tamás; Tettamanti, Tamás

doi:10.1016/j.measurement.2021.109581

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…The number of measurement devices at road sections is limited to employ the minimal number of fixed sensors to curtail costs while maximising data collection (Yao and Tang, 2019). Nevertheless, this reduced sensor count yields fewer data points, amplifying uncertainty within the estimation model (Horváth and Tettamanti, 2021). Addressing this uncertainty effectively entails the application of robust methodologies, exemplified by the approach employed in this current study.…”

Section: Literature Reviewmentioning

confidence: 99%

Road traffic queue length estimation with artificial intelligence (AI) methods

Ferencz,

Zöldy

2023

CogSust

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Sustainable monitoring of traffic has always been a significant problem for engineers, queue length being one of the most important metrics required for the performance assessment of signalized intersections. The authors of the present study propose a novel approach to estimating cycle-by-cycle queue lengths at a given signalized intersection. Focusing on the examination of shock wave phenomena and the traffic model, this study first elucidates the definitions and assumptions it employs. Subsequently, it delves into the creation of the queuing model, alongside the utilization of a machine-learning (ML) based Kalman Filter (KF) algorithm for estimation. The information contained within the output files is visualized on distinct graphs, along with the velocities at various time intervals derived from virtual simulations involving a queue of 12 vehicles. This graphical representation serves as a conclusive validation, demonstrating a strong correlation between the simulation and the estimation achieved through the KF approach. The method presented yielded dependable and resilient estimates for the simulated queue lengths, even in the presence of noisy measurements.

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Section: Literature Reviewmentioning

confidence: 99%