Position weighted backpressure intersection control for urban networks

Li, Li; Jabari, Saif Eddin

doi:10.1016/j.trb.2019.08.005

Cited by 62 publications

(29 citation statements)

References 54 publications

(80 reference statements)

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“…Most traffic state estimation techniques in the literature have focused on reproducing traffic conditions at aggregate levels (averaged over segment lengths greater than 100 meters and time intervals greater than one minute [5], [6]). Advanced traffic management tools (e.g., adaptive traffic signal control) require knowledge of traffic conditions at a much finer scale [7]- [13]; see [14] for a comprehensive review of past traffic state estimation methods.…”

Section: Introductionmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

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We propose a statistical learning-based traffic speed estimation method that uses sparse vehicle trajectory information. Using a convolutional encoder-decoder based architecture, we show that a well trained neural network can learn spatio-temporal traffic speed dynamics from time-space diagrams. We demonstrate this for a homogeneous road section using simulated vehicle trajectories and then validate it using real-world data from the Next Generation Simulation (NGSIM) program. Our results show that with probe vehicle penetration levels as low as 5%, the proposed estimation method can provide a sound reconstruction of macroscopic traffic speeds and reproduce realistic shockwave patterns, implying applicability in a variety of traffic conditions. We further discuss the model's reconstruction mechanisms and confirm its ability to differentiate various traffic behaviors such as congested and free-flow traffic states, transition dynamics, and shockwave propagation. We also provide a comparison against a widely used adaptive smoothing technique used for the same purpose and demonstrate the superiority of the proposed approach, even with probe vehicle lower penetration levels.

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Section: Introductionmentioning

confidence: 99%

Traffic Data Imputation Using Deep Convolutional Neural Networks

et al. 2020

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“…An extension is a cyclic MP-based algorithm proposed by Le et al (2015) to provide a minimum phase in a cycle for the movements with low traffic demand. Further, Li and Jabari (2019) proposed a decentralized MP-based method, called position-weighted backpressure (PWBP) that captures the spatial distribution of vehicles along the links, and consequently, spillback conditions. The results showed that PWBP method outperforms the standard and the capacity-aware MP methods, specifically with higher demand levels.…”

Section: Introductionmentioning

confidence: 99%

“…It should be pointed out that the PWBP method proposed by Li and Jabari (2019) employs a capacity-aware version of MP which additionally accounts for the possibility of spillbacks by considering spatial distribution of vehicles through applying higher weights to queues that extend to the ingress of the link. In the method, a small number of phases (i.e.…”

Section: Introductionmentioning

confidence: 99%

Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks

Noaeen

Mohajerpoor

Far

et al. 2021

Transportation Research Part C: Emerging Technologies

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This paper proposes a decentralized network-level traffic signal control method addressing the effects of queue spillbacks. The method is traffic-responsive, does not require data communication between intersections' controllers, uses lane-based queue measurements, and is acyclic. Each traffic controller operating at an intersection aims at maximizing the effective outflow rate locally and independently with the goal of maximizing global throughput of the entire network. At each intersection, the signal control method estimates and adopts the maximum possible phase time in which all active movements discharge at their full capacity. This is modeled using a shockwave based queue length estimation model while capturing the spillback at the downstream links. The method demands real-time data including, the queue lengths, the arrival flows, and the downstream queue lengths in all the lanes at the control decision times. The proposed method results in a feasible solution in all conditions in the entire network with any scale within a short amount of time. A stability concept for the traffic network is defined, and asymptotic stability of the controlled traffic network are verified. Moreover, a sufficient condition for the optimality of the proposed control algorithm for maximizing the instantaneous total throughput of the network intersections is demonstrated. Numerical results show that the proposed method outperforms benchmark methods in both isolated intersection and network configurations.

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“…This is consistent with the definition used by queuing theorists, the vast majority of BP models in traffic control and, more importantly, it is consistent with the way queues are treated in the dynamical models used to produce the network‐wide performance guarantees. It has been established that this is a deficiency in BP models since control decisions should distinguish stopped or “queued” vehicles (near the stoplines) from those moving freely near the entrances of the network links [9]. Many variants of BP have appeared in the literature (e.g.…”

Section: Introductionmentioning

confidence: 99%

Backpressure control with estimated queue lengths for urban network traffic

Okoth²,

Jabari

2021

IET Intelligent Trans Sys

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Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, a fully connected environment in the real world is still far. This paper tests the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. BP control is combined with a speed/density field estimation module suitable for a partially connected environment. The proposed system is referred to as a BP with estimated queue lengths (BP-EQ). The robustness of BP-EQ is tested to varying levels of connected vehicle penetration, and BP-EQ is compared with the original BP (i.e. assuming accurate knowledge of traffic conditions), a real-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. These results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.

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Position weighted backpressure intersection control for urban networks

Cited by 62 publications

References 54 publications

Traffic Data Imputation Using Deep Convolutional Neural Networks

Traffic Data Imputation Using Deep Convolutional Neural Networks

Real-time decentralized traffic signal control for congested urban networks considering queue spillbacks

Backpressure control with estimated queue lengths for urban network traffic

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