Local ramp metering with distant downstream bottlenecks: A comparative study

Kan, Yuheng; Wang, Yibing; Papageorgiou, Markos; Papamichail, Ioannis

doi:10.1016/j.trc.2015.08.016

Cited by 58 publications

(23 citation statements)

References 15 publications

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“…However, bottlenecks may be further away from the merging areas in real world scenarios, where ALINEA cannot lead to high efficiency. Thus, PI-ALINEA, a Proportional-Integral (PI) extension of ALINEA has been proposed and proved to be an efficient ramp-metering algorithm in the presence of far-downstream bottlenecks [26].…”

Section: B Simulation Resultsmentioning

confidence: 99%

“…Recent years, researchers pay their attention to advanced tuning approaches for feedback gains and operational parameters in ALINEA [23], [24], integration of ALINEA with Iterative Learning Control (ILC) [25], and the derivatives of ALINEA, e.g. PI-ALINEA [26]. In addition to ramp metering control, work in [27] presented an integration of local ramp metering with dynamic speed limits to reduce the Total Time Spent (TTS).…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Optimal Control for Time-Efficient Highway Traffic Management

Liu

Dai

et al. 2018

2018 Annual American Control Conference (ACC)

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This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods. Abstract-This article examines the hybrid traffic control problem to minimize total travel time (TTT) of a highway network through traffic management infrastructures, including dynamic speed limit signs, ramp metering, and information board.We first build the traffic flow model based on the Moskowitz function for each highway link to predict traffic status within a control horizon. The traffic density is predicted based on the flow dynamic model and corrected periodically by measured traffic flow data. The minimum TTT traffic control problem is then formulated as a mixed-integer quadratic programming problem with quadratic constraints. Numerical simulation of a real world highway network is provided to demonstrate significant reduction of TTT and alleviation of traffic congestion compared to results obtained from ALINEA and PI-ALINEA methods.

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Section: B Simulation Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

Liu

Dai

et al. 2018

2018 Annual American Control Conference (ACC)

View full text Add to dashboard Cite

show abstract

“…Although ALINEA is a local ramp metering algorithm, it draws much attention due to its simplicity, stability, and efficiency. Therefore, a large number of variations of ALINEA have been proposed to adapt to more complicated scenarios (e.g., METALINE, FL-ALINEA, UP-ALINEA, X-ALINEA/Q, PI-ALINEA) [20], [30]- [33] or to extend for achieving system-wide benefits (HERO) [34].…”

Section: B Control-based Approachesmentioning

confidence: 99%

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

Zhao

Wang

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Ramp metering, a traditional traffic control strategy for conventional vehicles, has been widely deployed around the world since the 1960s. On the other hand, the last decade has witnessed significant advances in connected and automated vehicle (CAV) technology and its great potential for improving safety, mobility and environmental sustainability. Therefore, a large amount of research has been conducted on cooperative ramp merging for CAVs only. However, it is expected that the phase of mixed traffic, namely the coexistence of both humandriven vehicles and CAVs, would last for a long time. Since there is little research on the system-wide ramp control with mixed traffic conditions, the paper aims to close this gap by proposing an innovative system architecture and reviewing the state-of-the-art studies on the key components of the proposed system. These components include traffic state estimation, ramp metering, driving behavior modeling, and coordination of CAVs. All reviewed literature plot an extensive landscape for the proposed system-wide coordinated ramp control with mixed traffic conditions.

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“…Field results showed their effectiveness [6]. Some extension versions were also proposed such as PI-ALINEA [7] and ITC-ALINEA [8]. They belong to local ramp metering strategies.…”

Section: Introductionmentioning

confidence: 99%

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

Zhang

Wang

Lai

et al. 2019

Journal of Advanced Transportation

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Ramp metering is an effective measure to alleviate freeway congestion. Traditional methods were mostly based on fixed-sensor data, by which origin-destination (OD) patterns cannot be directly collected. Nowadays, trajectory data are available to track vehicle movements. OD patterns can be estimated with weaker assumptions and hence closer to reality. Ramp metering can be improved with this advantage. This paper extracts OD patterns with historical trajectory data. A validation test is proposed to guarantee the sample representativeness of vehicle trajectories and then implement coordinated ramp metering based on the contribution of on-ramp traffic to downstream bottleneck sections. The contribution is determined by the OD patterns. Simulation experiments are conducted under real-life scenarios. Results show that ramp metering with trajectory data increases the throughput by another 4% compared with traditional fixed-sensor data. The advantage is more significant under heavier traffic demand, where traditional control can hardly relieve the situation; in contrast, our control manages to make congestion dissipate earlier and even prevent its forming in some sections. Penetration of trajectory data influences control effects. The minimum required penetration of 4.0% is determined by a t-test and the Pearson correlation coefficient. When penetration is less than the minimum, the correlation between the estimation and the truth significantly drops, OD estimation tends to be unreliable, and control performance becomes more sensitive. The proposed approach is effective in recurrent freeway congestion with steady OD patterns. It is ready for practice and the analysis supports the real-world application.

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Local ramp metering with distant downstream bottlenecks: A comparative study

Cited by 58 publications

References 15 publications

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

Hybrid Optimal Control for Time-Efficient Highway Traffic Management

The State-of-the-Art of Coordinated Ramp Control with Mixed Traffic Conditions

Extracting Origin-Destination with Vehicle Trajectory Data and Applying to Coordinated Ramp Metering

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