Image projection clues for improved real-time vehicle tracking in tunnels

Jelaca, Vedran; Castañeda, Jorge Oswaldo Niño; Pižurica, Aleksandra; Philips, Wilfried

doi:10.1117/12.908873

Cited by 3 publications

(2 citation statements)

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“…By considering the temporal and spatial distribution of stopped data in the x‐t plane, the stopped data of two consecutive cycles can be linearly separated with a straight line with slope w that lies properly between the two cycles. Therefore, we propose a clustering technique based on the projection profile method (Jelaca et al., ) to cluster the stopped data into cycles. The purpose of the projection profile method is to extract a scalar feature from the (2D) dispersed data of stopped vehicles in the x‐t plane.…”

Section: Methodsmentioning

confidence: 99%

Queue Profile Estimation in Congested Urban Networks with Probe Data

Ramezani

Geroliminis

2014

Computer aided Civil Eng

130

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Section: Methodsmentioning

confidence: 99%

Queue Profile Estimation in Congested Urban Networks with Probe Data

Ramezani

Geroliminis

2014

Computer aided Civil Eng

130

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“…By scrutinizing the temporal and spatial distribution of stopped data in the x-t plane, we notice that the stopped data of two consecutive cycles can be linearly separated with a straight line with slope that lies properly between the two groups. Therefore, a clustering technique based on the projection profile method [24] is proposed to cluster the stopped data into cycles. The purpose of projection profile method is to extract a scalar feature from the (2-D) data of stopped vehicles in the x-t plane.…”

Section: B Clustering the Stopped Vehicles To Cyclesmentioning

confidence: 99%

Exploiting probe data to estimate the queue profile in urban networks

Ramezani

Geroliminis

2013

16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013)

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Queues at signalized intersections are one of the main causes of traffic delays and urban traffic state variability. Hence, a method to estimate queue characteristics provides a better understanding of urban traffic dynamics and a performance measurement of signalized arterials. In order to capture the evolution of queues, we aim at leveraging the collective effect of spatially and temporally dispersed probe data to identify the formation and dissipation of queues in the time-space plane. The queue profile characterizes the evolution of both queue front and back, which consequently can be separated in a two-step estimation process resulting to the queue profile polygon. The evolution of queue front, in the time-space diagram, based on the kinematic traffic shockwave theory is modeled as a line with the known slope of queuedischarging shockwave and estimated with a constrained optimization and a technique known as support vector machine. The evolution of back of queue is more challenging and modeled as a piecewise linear function where slope of segments is between the queue-discharging shockwave and zero. In the proposed method, the input data consists of position and velocity of probe vehicles. The queue profile estimation method does not require any explicit information of signal settings and arrival distribution. The proposed method is tested with various penetration rates and sampling intervals of probe data, which reveals promising results once compared to a uniform arrival queue profile estimation procedure. The proposed method could be beneficial for spillback identification, vehicle trajectory construction, and fuel consumption and emission estimation.

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