Utility optimization framework for a distributed traffic control of urban road networks

Le, Tung; Vu, Hai L.; Walton, Neil; Hoogendoorn, Serge P.; Kovács, Péter; Queija, R. Núñez

doi:10.1016/j.trb.2017.10.004

Cited by 30 publications

(19 citation statements)

References 28 publications

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“…LV, AV) can be expected to further impact route choice and travel behavior altogether. This more general network design and control problem can be modeled using bilevel or simulation-based optimization wherein users' departure time and route choice can be accounted for based on traffic equilibrium theory [14]. Given that our study is focused on traffic control and assumes fixed route choice, we leave this investigation for future research.…”

Section: Discussion and Perspectivesmentioning

confidence: 99%

“…For more details on this conflict-point formulation, we refer the reader to Levin and Rey [18]. Let Z n B (x(t)) be the maximal local pressure that can be obtained using the blue phase based on the network state x(t) at intersection n. The MILP used to coordinate traffic during blue phases is summarized below in Formulation (14) and hereby referred to as the Blue.…”

Section: Blue Phasementioning

confidence: 99%

“…For each intersection n, if green phase is selected by the policy then lane services rates are given by the solution of Model (11), i.e. y i (t) = j∈A n l :(i,j)∈M n l y ij (t); otherwise, blue phase is selected and lane service rates are given by the solution of Model (14): y i (t) = v∈V n i (t) z v . By definition, policy π imposes y ij (t) ≤s ij : this constraint is explicitly imposed in Green and implicitly imposed in Blue through (15) with y ij (t) = v∈V n ij (t) z v .…”

Section: Recall Thatmentioning

confidence: 99%

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Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Rey

Levin

2019

Transportation Research Part B: Methodological

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With the forecasted emergence of autonomous vehicles in urban traffic networks, new control policies are needed to leverage their potential for reducing congestion. While several efforts have studied the fully autonomous traffic control problem, there is a lack of models addressing the more imminent transitional stage wherein legacy and autonomous vehicles share the urban infrastructure. We address this gap by introducing a new policy for stochastic network traffic control involving both classes of vehicles. We conjecture that network links will have dedicated lanes for autonomous vehicles which provide access to traffic intersections and combine traditional green signal phases with autonomous vehicle-restricted signal phases named blue phases. We propose a new pressure-based, decentralized, hybrid network control policy that activates selected movements at intersections based on the solution of mixed-integer linear programs. We prove that the proposed policy is stable, i.e. maximizes network throughput, under conventional travel demand conditions. We conduct numerical experiments to test the proposed policy under varying proportions of autonomous vehicles. Our experiments reveal that considerable trade-offs exist in terms of vehicle-class travel time based on the level of market penetration of autonomous vehicles. Further, we find that the proposed hybrid network control policy improves on traditional green phase traffic signal control for high levels of congestion, thus helping in quantifying the potential benefits of autonomous vehicles in urban networks.

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Section: Discussion and Perspectivesmentioning

confidence: 99%

Section: Blue Phasementioning

confidence: 99%

Section: Recall Thatmentioning

confidence: 99%

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Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Rey

Levin

2019

Transportation Research Part B: Methodological

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“…The cycle times and signal phasing structure at each intersection were maintained in their original structure. This delay data was used to calculate the green time for each phase using Eq (18). The new green times for the phases were then executed at the intersection by the signal controller.…”

Section: Methodsmentioning

confidence: 99%

“…The maximum stability property means that max-pressure control will stabilize the number of vehicles in the network if any signal control policy could stabilize the network. Extensions to max-pressure signal control have included policies for a cycle-based phase structure [6], unknown routing proportions [13,14], realistic finite queue buffers [15,16], and adaptive route guidance [17,18]. Previous work has exclusively relied on queue length information for the signal control, with the exception of [19] which uses delay information from the head-of-line vehicle in each queue.…”

Section: Introductionmentioning

confidence: 99%

A simple crowdsourced delay-based traffic signal control

Dixit¹,

Nair²,

Chand³

et al. 2020

PLoS ONE

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Current transportation management systems rely on physical sensors that use traffic volume and queue-lengths. These physical sensors incur significant capital and maintenance costs. The ubiquity of mobile devices has made possible access to accurate and cheap traffic delay data. However, current traffic signal control algorithms do not accommodate the use of such data. In this paper, we propose a novel parsimonious model to utilize real-time crowdsourced delay data for traffic signal management. We demonstrate the versatility and effectiveness of the data and the proposed model on seven different intersections across three cities and two countries. This signal system provides an opportunity to leapfrog from physical sensors to low-cost, reliable crowdsourced data.

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Design Parameters of Residential Building for Improving Performance of RHS: Evidence from Fuzhou, Fujian Province, China

Xinyi

Shen

Huang

2018

Urban Planning and Water-Related Disaster Management

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The network's admissible demand region (ADR), which is a key index to characterize a network's ability to handle incoming demands, is shaped by each movement's saturation flow rate (SFR). Existing backpressure (BP) traffic control policies commonly assumed a fixed and/or completely known SFR when calculating the pressure for decision-making. On one hand, since real-time traffic conditions can significantly influence the traffic supply, the fixed mean SFR (M-SFR) assumption could result in a mismatch between dynamic demand and supply. On the other hand, accurately predicting the imminent SFR (I-SFR) is challenging because of the complicated interactions between traffic participants. Hence, the completely known SFR assumption is impractical in real-world settings. Our paper demonstrates that, compared with only using the constant M-SFR information, using more knowledge of I-SFR can enlarge the upper bound of ADR. In addition, we theoretically prove that the BP with predicted I-SFR can guarantee network stability as long as the demand is interior to the ADR.The proposed theory is validated by a calibrated simulation model in the experiments. Three I-SFR prediction methods with different accuracies are adopted: the M-SFR method, the heuristic estimation method, and the deep neural network method. They are tested in three BP-based control policies to investigate whether our findings are robust. The simulation results show that: a higher prediction accuracy of I-SFR can effectively help all three BP-based policies enlarge the network ADR, and more accurate I-SFR can productively reduce the average vehicle delay.

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Utility optimization framework for a distributed traffic control of urban road networks

Abstract: through simulation and numerical results that the new policy outperforms the original BackPressure-based distributed control scheme both in terms of network throughput and other congestion measures such as travel time.

Cited by 30 publications

References 28 publications

Blue phase: Optimal network traffic control for legacy and autonomous vehicles

Blue phase: Optimal network traffic control for legacy and autonomous vehicles

A simple crowdsourced delay-based traffic signal control

Design Parameters of Residential Building for Improving Performance of RHS: Evidence from Fuzhou, Fujian Province, China

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