An optimization model for arterial coordination control based on sampled vehicle trajectories: The STREAM model

Yao, Jiarong; Tan, Chaopeng; Tang, Keshuang

doi:10.1016/j.trc.2019.10.014

Cited by 29 publications

(7 citation statements)

References 30 publications

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“…Here, the root mean square error (RMSE) between the theoretical and actual acceleration per time step is selected as the performance index. m * will be the optimal number of inserted RVs when the RMSE is the minimum, which can be calculated by Equation (6).…”

Section: Estimating the Number Of Inserted Rvsmentioning

confidence: 99%

“…Vehicular trajectory provides massive spatial-temporal traffic information, which has been used in many transportation research, for example, transportation fuel consumption and emissions [1][2][3], traffic signal optimization [4][5][6], traffic flow modelling, validation, and calibration [7][8][9][10][11], driving behaviour analysis [12,13], traffic state estimation [14][15][16][17] in terms of volume [18,19], queue length [20][21][22], and fundamental diagram [23,24]. Therefore, it is of great significance to obtain more vehicle trajectory data [25,26].…”

Section: Introductionmentioning

confidence: 99%

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Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

Yao

Jiang

et al. 2022

IET Intelligent Trans Sys

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Vehicular trajectory data collected by connected automated vehicles (CAVs) is minimal due to the low penetration rates (PRs) of CAVs, and fail to capture the characteristics of traffic flow. This study proposes a fully sampled trajectory reconstruction method for mixed traffic flow with regular vehicles (RVs), connected vehicles (CVs), and CAVs based on car‐following behaviour. Firstly, considering the minimum safety distance constraints between vehicles, an optimization model for minimizing the impact on the acceleration of the known vehicles is developed to obtain the number of inserted RVs. Secondly, the speed of the inserted RVs is estimated based on the traffic flow model. Then, an optimization model is proposed to determine the position of each inserted RV. Finally, numerical simulation is designed to investigate the influence of traffic density and PRs of CAVs and CVs. Results show that the proposed method can better reconstruct the vehicle trajectory on the freeway under the different traffic densities in a congested state. The MAPE of the number and position of inserted RVs is less than 10.7% and 0.37%, respectively. In addition, the proposed method performs well even if the PRs of CAVs and CVs are extremely low.

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Section: Estimating the Number Of Inserted Rvsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

Yao

Jiang

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…A mixed-integer linear programming (MILP) formulation was proposed. Yao [33] proposed a new arterial coordination control model. Sum of the delays of all the sampled trajectories ever travelling on the mainline of the arterial is selected to be the optimization objective.…”

Section: Research On Coordination Of Lanes and Signal Timing Atmentioning

confidence: 99%

An Interactive Traffic Signal Optimization Approach with Dynamic Variable Guidance Lane Control

Zhao

Pan

et al. 2022

Journal of Advanced Transportation

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The purpose of setting up variable guidance lanes is to ease the traffic pressure on lanes with more traffic under the condition of ensuring the traffic capacity of the intersection. This study proposes a bi-level model-interactive relationship between variable guidance lane design and signal control and carry out a case study for a road in Wuhan, China. The design plan for the goal, combined with the design plan of the variable guidance lane, optimizes the signal timing plan of the intersection. A real case is modeled in VISSIM to simulate the design scheme of variable guidance lanes to verify the reliability and effectiveness of the bi-level model. The results showed that the average vehicle delay at the intersection was reduced by 20.65% after the bi-level model was optimized. The average error between model calculation results and simulation results is 9.88%. Moreover, the influencing factors of the proposed model are also carried out. The results show that when the traffic flow is greater than 1,000 pcu/h, the optimization effect of the model is more significant. When the traffic capacity gradually increases, the average vehicle delay calculated by the model is smaller. The design scheme and signal timing scheme proposed by the bi-level model can ensure the overall traffic efficiency of the intersection, improve the traffic efficiency of the traffic-stressed lanes, and further promote the space-time resource utilization of the intersection, optimize the space-time resources of the road network, and provide a scientific basis and new ideas and methods.

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“…Then the traffic flow data are aggregated from these obtained stationary stationbased data. Considering the emerging CV technology, it gives us a new opportunity to investigate the potential of the finegrained spatial and temporal CV data for signal coordination [5,19,20]. Furthermore, their implementation is hampered by high installation and maintenance costs [4,18].…”

Section: Classic Signal Coordinationmentioning

confidence: 99%

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

Peng

Qiu

2021

IET Intelligent Trans Sys

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The connected vehicle (CV) technology can benefit signal coordination with fine-grained spatial and temporal vehicle and infrastructure data via real-time communication. Although CV-based signal coordination systems have been investigated from offline and online strategic perspectives, existing works have yet to address certain coordination performance issues, including the dynamic platoon dispersion effect and low penetration impact. Targeting at resolving these issues, this work proposes a real-time predictive coordination method consisting of a probabilistic single-vehicle-based dynamic platoon dispersion model, an extended link performance function, and a real-time model predictive control (MPC)-based coordination framework. The proposed coordination method was comprehensively investigated by a software-in-loop simulation platform with different practical corridor scenarios in the ACTIVE CV testbed in Canada. Results show the proposed coordination control continuously outperformed existing signal control with lower delays for major streets with different demand profiles and different CV penetration rates, even in low penetration conditions. In conclusion, the proposed CV MPC-based coordination can offer significant potential to further improve the system performance of signal coordination in a low penetration environment; therefore, it has the potential to enhance other CV-based signal control applications in the initial deployment stage of CV technology.

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An optimization model for arterial coordination control based on sampled vehicle trajectories: The STREAM model

Cited by 29 publications

References 30 publications

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

Trajectory reconstruction for mixed traffic flow with regular, connected, and connected automated vehicles on freeway

An Interactive Traffic Signal Optimization Approach with Dynamic Variable Guidance Lane Control

Real‐time predictive coordination based on vehicle‐triggered platoon dispersion in a low penetration connected vehicle environment

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