A survey of model predictive control methods for traffic signal control

Ye, Bao-Lin; Wu, W.; Ruan, Keyu; Li, Lingxi; Chen, Tehuan; Gao, H. Oliver; Chen, Yaobin

doi:10.1109/jas.2019.1911471

Cited by 76 publications

(39 citation statements)

References 118 publications

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“…Moreover, based on the combined K-means Gaussian interval type-2 fuzzy sets forecasting method, Li et al 18 devoted themselves to the study of long-term traffic prediction. Ye et al 19 denoted that a suitable traffic flow model is convenient in modeling and analyzing traffic dynamics, and they described the dynamics of traffic flow in networks with a discrete-time system.…”

Section: Introductionmentioning

confidence: 99%

Individual traffic prediction in cellular networks based on tensor completion

Liu

et al. 2021

Int J Communication

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SummaryAccurate individual (per‐user) traffic prediction in cellular networks is considered a critical capability for the system performance improvement in terms of dynamic bandwidth allocation and network optimization. However, existing methods have limitations in capturing the characteristics of individual traffic, because the observed traffic data are usually incomplete and traffic consumption patterns significantly differ among users. In this paper, to fully exploit the inherent temporal‐spatial correlations of individual traffic data, this work models the traffic data as a three‐dimensional traffic tensor. Different from the “three‐step” strategies (i.e., “data processing + decomposition + prediction”) in the existing methods, we propose a novel tensor completion (TC)‐based “two‐step” strategy, that is, “data processing & decomposition + prediction” for individual traffic prediction, which can recover and decompose the traffic data simultaneously. Furthermore, an efficient algorithm based on the alternating direction method of multipliers (ADMM) framework is proposed to solve the resulting model. To the best of our knowledge, this is the first work to apply the TC‐based “two‐step” strategy for individual traffic prediction in cellular networks. Experiments conducted on a real cellular traffic dataset empirically validate the superiority of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Individual traffic prediction in cellular networks based on tensor completion

Liu

et al. 2021

Int J Communication

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“…e purpose is to achieve the balanced utilization of road network resources. Although current traffic signal control research is trending toward networking [8] and intellectualization [9][10][11][12][13], for specific traffic scenarios, the referenced studies still cannot provide satisfactory solutions. One such scenario is asymmetric traffic flow at signalized intersections, which is still being studied.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Phase Signal Control Method for Unstable Asymmetric Traffic Flow at Intersections

Jiang

Jin

2021

Journal of Advanced Transportation

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This paper addresses the limitations that the phases proposed in variable phase sequencing studies for stochastic traffic flow are all predetermined and that the variable phase sequencing is only suitable for low traffic volume environment. It presents a dynamic phase signal control method for unstable asymmetric traffic flow with two primary operational objectives: the realization of a dynamic phase scheme in each cycle and optimization of signal control parameters. First, an asymmetric state of traffic flow at signalized intersections is defined, rules governing the generation of the dynamic phase of each cycle based on asymmetric state are proposed, and the delay variations of intersections adopting dynamic phase schemes are modeled. Next, a signal control parameter adjustment algorithm for the dynamic phase is constructed to maximize the positive benefits of delay variation. Last, the operational performance of the proposed method is validated using data collected from an intersection in Harbin, China, by VISSIM simulation. Furthermore, it is found that a higher asymmetric coefficient leads to lower efficiency of a symmetrical release phase scheme at intersections, and the increase of average delay becomes significant when the asymmetric coefficient threshold is greater than 0.2.

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“…Phasing traffic light using a reasonable method reduces the delay time and increases the speed of vehicles and pedestrians on urban networks. e traffic signal strategies are mainly categorized as fixed-timed, coordinated traffic responsive [1], and predictive control strategies. Simple implementation and low management cost are two main reasons to use the fixed-timed strategy in lots of current urban traffic systems.…”

Section: Introductionmentioning

confidence: 99%

“…e drawbacks of the fixed-timed model are that the resulting settings are based on the historical data rather than real-time data. SCOOT [1,2] and SCATS [1,3] are two well-known methods of the traffic responsive strategy that control traffic light based on the current traffic status. e shortcoming of this strategy is ignoring future traffic.…”

Section: Introductionmentioning

confidence: 99%

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

Soltani-Sarvestani

Azimifar

Wong

et al. 2020

Journal of Advanced Transportation

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This paper introduces two traffic light strategies to control traffic and avoid traffic jam in urban networks. One strategy is a new traffic light scheduling system, which controls traffic light using local variables (waiting time and number of vehicle on links) but has a global impact on the traffic, using shared variables between neighbour intersections. The proposed traffic light scheduling system is designed based on eigenvector centrality of intersection relation matrix. The intersection relation matrix is a new representation of a junction which indicates the traffic relation between intersection’s links and adjacent intersections. The second contribution is expanding a new dual mode traffic light strategy (namely, Exit Status Traffic Light (ETL)), which notifies the drivers whether they are allowed to exit a street or not. In other words, vehicles are allowed to enter a street in both red and green ETL, but they are not allowed to exit the street for a long time in red ETL (while traffic is heavy in the subnetwork). The ETL gives a chance to relax traffic in a subnetwork and avoid traffic jam. The effectiveness of the proposed strategy is analysed and evaluated by a number of simulations on three-way grid networks. Two-way rectangular grid networks are modelled via a cell transmission model (CTM). The macroscopic fundamental diagram (MFD) and the number of jammed cells are compared with two state-of-the-art methods.

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A survey of model predictive control methods for traffic signal control

Cited by 76 publications

References 118 publications

Individual traffic prediction in cellular networks based on tensor completion

Individual traffic prediction in cellular networks based on tensor completion

Dynamic Phase Signal Control Method for Unstable Asymmetric Traffic Flow at Intersections

An Innovative Eigenvector-Based Method for Traffic Light Scheduling

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