Queue Profile Estimation in Congested Urban Networks with Probe Data

Ramezani, Mohsen; Geroliminis, Nikolas

doi:10.1111/mice.12095

Cited by 134 publications

(70 citation statements)

References 47 publications

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“…There are a few different definitions of critical points. [15] estimated the critical points as the first trajectory points with speed lower than a threshold; [30] and [31] fitted the vehicle trajectory into a piece-wise linear function, and identified the critical points as the intersection between each two pieces; [32] determined the critical points using both speed and acceleration information; [33] defined the critical points as the first calculated x-t point with zero speed. In contrast with the existing research, and in order to retrieve flow information from the shockwaves, this paper proposes another way to retrieve the critical points that is coherent with the kinematic wave theory.…”

Section: Introductionmentioning

confidence: 99%

“…In contrast with the existing research, and in order to retrieve flow information from the shockwaves, this paper proposes another way to retrieve the critical points that is coherent with the kinematic wave theory. In the second step, the existing literature has obtained the BoQ curve from the critical points using variational theory [15], [34], fundamental diagram [31], linear regression [30], [32], or piecewise linear fitting [33].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the other works, e.g. [15], [33] and [34] aimed to estimate a non-linear BoQ and proposed complex non-convex models, which can be time consuming to find the global optimum. [31] directly obtained the shockwave for each trajectory point using the fundamental diagram, which works for very low penetration rates, but might be sensitive to GPS noises.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Yang

Menéndez

2019

IEEE Trans. Intell. Transport. Syst.

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This paper proposes a convex optimization based algorithm for queue profile estimation in a connected vehicle environment, which can also be used for trajectory reconstruction, delay evaluation, etc. This algorithm generalizes the widelyadopted assumption of a linear back of queue (BoQ) curve to a piecewise linear BoQ curve to consider more practical scenarios. The piecewise linear BoQ curve is estimated via a convex optimization model, ensuring efficient computation. Moreover, this paper explicitly handles cases with low penetration rates and low sampling rates, as well as measurement noises. Additionally, the proposed methodology is extended to an urban arterial, reusing the estimated departure information from the upstream intersections to further improve the estimation accuracy. Finally, two online implementation approaches are presented to perform real-time queue estimation.The proposed methodology is tested with two datasets: the Lankershim dataset in the NGSIM project and the simulated dataset of Wehntalerstrasse, Zurich, Switzerland. Results show that the error is less than 1.5 cars in undersaturated scenarios and 5.2 cars in oversaturated scenarios if the penetration rates are larger than 0.1 and sampling rates are higher than 0.05s −1 . It is demonstrated that by considering a piecewise linear BoQ curve, the estimation accuracy can be improved by up to 16%. Incorporating flow successfully can also reduce the estimation error by up to 16%. Results further show that the proposed methodology is robust to measurement errors. It is finally shown that the proposed framework can be solved within a reasonable time (0.8s), which is sufficient for most real-time applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Yang

Menéndez

2019

IEEE Trans. Intell. Transport. Syst.

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“…The average arrival rate was inferred from the Bayesian network by applying the Expectation-Maximization algorithm. There is also a stream of literature that applies the shockwave theory to probe vehicle data (Ban et al, 2011;Cetin, 2012;Ramezani and Geroliminis, 2015;Li et al, 2017;Rompis et al, 2018), or combines probe vehicle data and loop detector data (Badillo et al, 2012;Cai et al, 2014;Wang et al, 2017;Shahrbabaki et al, 2018), to estimate or predict the queue lengths. Since these studies are not closely related to this paper methodologically, they will not be introduced in detail.…”

Section: Introductionmentioning

confidence: 99%

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

Zhao

Zheng²,

Wong

et al. 2019

Transportation Research Part C: Emerging Technologies

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The rapid development of connected vehicle technology and the emergence of ride-hailing services have enabled the collection of a tremendous amount of probe vehicle trajectory data. Due to the large scale, the trajectory data have become a potential substitute for the widely used fixed-location sensors in terms of the performance measures of transportation networks. Specifically, for traffic volume and queue length estimation, most of the trajectory data based methods in the existing literature either require high market penetration of the probe vehicles to identify the shockwave or require the prior information about the queue length distribution and the penetration rate, which may not be feasible in the real world. To overcome the limitations of the existing methods, this paper proposes a series of novel methods based on probability theory. By exploiting the stopping positions of the probe vehicles in the queues, the proposed methods try to establish and solve a single-variable equation for the penetration rate of the probe vehicles. Once the penetration rate is obtained, it can be used to project the total queue length and the total traffic volume. The validation results using both simulation data and real-world data show that the methods would be accurate enough for assistance in performance measures and traffic signal control at intersections, even when the penetration rate of the probe vehicles is very low.

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“…To identify traffic congestion and understand traffic dynamics, transportation researchers prefer a spa-tiotemporal traffic diagram, in which x-axis is time, yaxis is space, and the color inside (or z-axis) represents speed. The information-rich spatiotemporal diagram is a popular and powerful tool in the study and practice of transportation, such as fusing data (Treiber and Helbing, 2002;Van Lint and Hoogendoorn, 2010;Treiber et al, 2011), understanding traffic characteristics (Wilson, 2008;Kerner, 2009), and proposing and validating models (Duret et al, 2011;Ramezani and Geroliminis, 2015;He et al, 2015b). Traditionally, it is constructed by using stationary detector data, such as Chen et al (2004), Kerner et al (2004), Laval et al (2009), Wieczorek et al (2009, and Yildirimoglu and Geroliminis (2013).…”

Section: Introductionmentioning

confidence: 99%

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

Zheng

Chen

et al. 2017

Computer aided Civil Eng

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In the era of big data, mining data instead of collecting data are a new challenge for researchers and engineers. In the field of transportation, extracting traffic dynamics from widely existing probe vehicle data is meaningful both in theory and practice. Therefore, this article proposes a simple mapping‐to‐cells method to construct a spatiotemporal traffic diagram for a freeway network. The method partitions a network region into small square cells and represents a real network inside the region by using the cells. After determining the traffic flow direction pertaining to each cell, the spatiotemporal traffic diagram colored according to traffic speed can be well constructed. By taking the urban freeway in Beijing, China, as a case study, the mapping‐to‐cells method is validated, and the advantages of the method are demonstrated. The method is simple because it is completely based on the data themselves and without the aid of any additional tool such as Geographic Information System software or a digital map. The method is efficient because it is based on discrete space‐space and time‐space homogeneous cells that allow us to match the probe data through basic operations of arithmetic. The method helps us understand more about traffic congestion from the probe data, and then aids in carrying out various transportation researches and applications.

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Queue Profile Estimation in Congested Urban Networks with Probe Data

Cited by 134 publications

References 47 publications

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Queue Estimation in a Connected Vehicle Environment: A Convex Approach

Various methods for queue length and traffic volume estimation using probe vehicle trajectories

Mapping to Cells: A Simple Method to Extract Traffic Dynamics from Probe Vehicle Data

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