PAMSCOD: Platoon-based Arterial Multi-modal Signal Control with Online Data

He, Qing; Head, Larry; Ding, Jun

doi:10.1016/j.sbspro.2011.04.527

Cited by 24 publications

(19 citation statements)

References 24 publications

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“…He et al () developed a platoon‐based MILP to find future optimal signal status considering transit and passenger car modes in a connected vehicle environment. This was a complex problem due to presence of mixed integer variables and the large size of the problem.…”

Section: Literature Reviewmentioning

confidence: 99%

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Mehrabipour

Hajbabaie

2017

Computer aided Civil Eng

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This article develops an efficient methodology to optimize the timing of signalized intersections in urban street networks. Our approach distributes a network‐level mixed‐integer linear program (MILP) to intersection level. This distribution significantly reduces the complexity of the MILP and makes it real‐time and scalable. We create coordination between MILPs to reduce the probability of finding locally optimal solutions. The formulation accounts for oversaturated conditions by using an appropriate objective function and explicit constraints on queue length. We develop a rolling‐horizon solution algorithm and apply it to several case‐study networks under various demand patterns. The objective function of the optimization program is to maximize intersection throughput. The comparison of the obtained solutions to an optimal solution found by a central optimization approach (whenever possible) shows a maximum of 1% gap on a number of performance measures over different conditions.

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Section: Literature Reviewmentioning

confidence: 99%

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Mehrabipour

Hajbabaie

2017

Computer aided Civil Eng

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“…A multi-modal signal control formulation called PAMSCOD, which relies only on significant level penetration of Vehicle-to-Infrastructure (V2I) communications, is proposed to consider priorities of bus, pedestrian and passenger cars [15]. This formulation is further revised to be more practical by assuming only traffic modes with priority (such as emergency vehicles, buses, and pedestrians) are equipped with V2I communication systems [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences

Ding

et al. 2015

IEEE Trans. Intell. Transport. Syst.

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Traffic control agencies (TCAs), including police officers, firefighters, or other traffic law enforcement officers, can override automatic traffic signal control and manually control the traffic at an intersection. TCA-based traffic signal control is crucial to mitigate nonrecurrent traffic congestion caused by planned and unplanned events. Understanding and predicting TCA behaviors is significant to optimize event traffic management and operations. In this paper, we propose a pressure-based human behavior model to mimic TCA's decision-making behavior. The model calculates TCA's pressure based on two attributes: vehicle and pedestrian queue dynamics and the red time duration for each phase. When TCA's pressure on each phase meet certain criteria and the minimal green is satisfied, TCA will terminate the current phase and switch to another phase. In order to study TCA behavior systematically, we first build a manual signal control simulator based on a microscopic traffic simulation tool. Supported by the manual control simulator, a series of human subject experiments have been conducted with real-world TCAs. Experiment data are divided into training data and test data. The proposed behavior model is then calibrated by training data, and the model is validated by both offline segment-based phase and duration prediction and online VISSIM-based simulation. Further, we test the model with videotaped TCA behavior data at a real-world intersection. Both validation results support the effectiveness of proposed behavior model. Index Terms-Human behavior modeling, multimodal event traffic, traffic signal control.

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“…Despite the promising results obtained from simulation tests, these algorithms were based on a fixed end-of-queue threshold, essentially operating as when information is provided from fixed-location detectors, and they were not adjusted for different penetration rates of the equipped vehicles. He et al developed a platoon-based adaptive signal control system that uses information from vehicle-to-infrastructure communications to adjust the signal settings in arterial signalized networks in real time considering different types of vehicles (21). At each intersection, the system accounts for the capacity of the downstream links in order to design signal control settings that prevent the occurrence of a de facto red situation.…”

Section: Arterial Queue Spillback Detection and Signal Control Based On Connected Vehicle Technologymentioning

confidence: 99%

Arterial Queue Spillback Detection and Signal Control Based on Connected Vehicle Technology

Christofa

Argote

Skabardonis

2013

Transportation Research Record

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Queue spillbacks are a major problem in urban signalized arterials because such spillbacks can lead to gridlock and excessive delays. Several methods based on fixed-location detector data have been proposed to identify the occurrence of queue spillbacks and implement signal control strategies to mitigate their impacts. This paper presents two queue spillback detection methods based on connected vehicle (CV) or probe data. The first method requires only the use of CV data and is based on the notion that nonequipped vehicles in queue that arrive after the last CV-equipped vehicle can be modeled by using a geometric distribution. The second spillback detection method combines CV data with information about the signal settings at the upstream intersection and is based on a kinematic wave theory of traffic. The authors also developed a signal control strategy to mitigate queue spillbacks once they were detected. The proposed queue spillback detection methods and alternative signal control strategy were tested through simulation on a four-signal segment of San Pablo Avenue in Berkeley, California. The results show the penetration rate thresholds of CV-equipped vehicles required for accurate queue detection. The proposed signal control strategy improved traffic operations for the upstream cross streets without compromising traffic operations on either direction of the arterial traffic and substantially reduced the variation of the queue length on the critical arterial link.

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PAMSCOD: Platoon-based Arterial Multi-modal Signal Control with Online Data

Cited by 24 publications

References 24 publications

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

A Cell‐Based Distributed‐Coordinated Approach for Network‐Level Signal Timing Optimization

Modeling Traffic Control Agency Decision Behavior for Multimodal Manual Signal Control Under Event Occurrences

Arterial Queue Spillback Detection and Signal Control Based on Connected Vehicle Technology

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