A Simulation Study on Max Pressure Control of Signalized Intersections

Sun, Xiaotong; Yin, Yafeng

doi:10.1177/0361198118786840

Cited by 46 publications

(22 citation statements)

References 21 publications

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“…Based on real-time measurement of queue lengths, at each decision point, MP selects the phase pair with the maximum pressure to be activated for a specific time interval, e.g., 5 s, and then MP is called again. "Algorithm 3: MP with minimum green time" in [30] is implemented as MP in this case, and MP is called every 5 s which is also suggested in [30]. e queue length input to MP is provided by the QSE module, which means that the data source and the data processing of MP, QIA-Lite, and QIA are identical.…”

Section: Comparison Of Different Methodsmentioning

confidence: 99%

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Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

Yin

Chen

Tang

et al. 2021

Journal of Advanced Transportation

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With recent development of mobile Internet technology and connected vehicle technology, vehicle trajectory data are readily available and exhibit great potential to be used as an alternative data source for urban traffic signal control. In this study, a Queue Intensity Adaptive (QIA) algorithm is proposed, using vehicle trajectory data as the only input to perform adaptive signal control. First, a Kalman filter-based method is employed to estimate real-time queue state with vehicle trajectories. Then, based on queue intensity that quantifies queuing pressure, five control situations are defined, and different min-max optimization models are designed correspondingly. Last, a situation-aware signal control optimization procedure is developed to adapt intersection’s queue intensity. QIA algorithm optimizes phase sequence and green time simultaneously. One case study was conducted at a field intersection in Shenzhen, China. The results show that provided with 7.4% penetrated vehicle trajectories, QIA algorithm effectively prevented queue spillback by constraining temporal percentage of queue spillback under 2.4%. The performance of QIA was also compared with the algorithm in Synchro and Max Pressure (MP) method. It was found that compared with Synchro, the extreme queue intensity, temporal percentage of queue spillback, delay, and stops were decreased by 54.7%, 97%, 22.3%, and 45.1%, respectively, and compared with MP the above four indices were decreased by 16%, 61.5%, −1.8%, and 49.4%, respectively.

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Section: Comparison Of Different Methodsmentioning

confidence: 99%

“…Step 9: find the set Λ S of phase sequences with the highest CS, as shown in equation (29); then, the optimal phase sequence S * ∈ Λ S has the minimum objective value, as shown in equation (30), and the corresponding optimal solution is g * (S * ):…”

Section: Optimizationmentioning

confidence: 99%

Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

Yin

Chen

Tang

et al. 2021

Journal of Advanced Transportation

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“…Urban transport systems usually depend on traffic signal control for facilitating traffic flow and preventing extensive queuing and delays. Improving the performance of signalised intersections through development of management strategies relies on a range of detection mechanisms and control objectives [1][2][3][4][5][6][7]. The main detection mechanism in conventional traffic signal control consists dominantly of discrete point or area detection, using technologies such as inductive loops or video image processing [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

“…The travel time between vehicle position and the stop-bar is calculated by considering current vehicle speed (4), whereas a comparison between travel time and signal timing duration for the assigned phase determines the arrival conditions (3). For the vehicles assigned to the first phase, the travel time is compared with the assigned green time for the phase while for the other phases, the amber and red time duration before changing to green is also considered (5). Note that we assume each vehicle moves at a constant speed, equal to its current speed.…”

mentioning

confidence: 99%

Signalised intersection control in a connected vehicle environment: User throughput maximisation strategy

Mohammadi

Roncoli

Mladenović

2021

IET Intelligent Trans Sys

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Connected vehicle (CV) technology is expected to bring additional opportunities to share, collect, and exploit various information on vehicles and their occupants. Assuming that CVs are able to transmit on-board users and vehicle data, a user-based signal timing optimisation (UBSTO) strategy is proposed, designed to optimise user throughput for signalised intersections. In the CV environment, the inputs of the proposed algorithm consist of position and speed of CVs, as well as the number of passengers travelling in each vehicle, while the output is the optimum green time duration for each signal phase. In addition, authors' proposed strategy is able to adapt the cycle length to the traffic volume condition. In case of missing users data, the same strategy can also operate in vehicle-based mode, where the objective is vehicle-throughput maximization. The performance of the proposed strategy is compared with a fully actuated controller (FAC) in microscopic simulation, for several scenarios, including different CV penetration rates. Authors' findings show that UBSTO can effectively increase user throughput and decrease average user delay in comparison with FAC, while also prioritising vehicles with higher number of users on-board. These findings have implications for further development of prioritization strategies for public transport and ride-sharing vehicles.

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“…Therefore, Ioslovich et al, Lin et al, Diakaki et al, Coogan et al, Sun and Yin, and Sun et al developed algorithms for on-line control of queue lengths at oversaturated intersections(19)(20)(21)(22)(23)(24)(25). Queue lengths can directly reflect traffic congestions meaning that the results output under queue-based rule-logic adaptive…”

mentioning

confidence: 99%

Macroscopic Fundamental Diagram Approach to Evaluating the Performance of Regional Traffic Controls

Liu

Mao

et al. 2020

Transportation Research Record

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Evaluating a regional traffic control system requires understanding both the advantages and disadvantages of control schemes as well as the interrelated characteristics of the system. To assess the efficiency of regional signal control schemes in a road network, this study, which is based on the macroscopic fundamental diagram (MFD) concept, proposes four evaluative indicators: maximum throughput, critical accumulation, gridlock accumulation and the degree of homogeneity. The maximum throughput and gridlock accumulation can be used to reflect the road network capacity and load capacity, respectively. The degree of homogeneity quantifies the spatial variations of traffic flows in the network. Combined with the gridlock accumulation, the critical accumulation values the durability of a regional control system in managing congestion in the network. This study used the regional road network in Qingyang District of Chengdu, China, as a real-world example to demonstrate the proposed MFD-based approach. In the demonstration, the MFD-based evaluation method was compared to the traditional travel time-based method. The demonstration evaluated the control effect and characteristic values of the network under four control modes: fixed-time, actuated, adaptive and adaptive coordinated control.

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A Simulation Study on Max Pressure Control of Signalized Intersections

Cited by 46 publications

References 21 publications

Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

Queue Intensity Adaptive Signal Control for Isolated Intersection Based on Vehicle Trajectory Data

Signalised intersection control in a connected vehicle environment: User throughput maximisation strategy

Macroscopic Fundamental Diagram Approach to Evaluating the Performance of Regional Traffic Controls

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