Back-Pressure Based Adaptive Traffic Signal Control and Vehicle Routing with Real-Time Control Information Update

Liu, Ying; Gao, Juntao; Ito, Minoru

doi:10.1109/icves.2018.8519601

Cited by 8 publications

(16 citation statements)

References 34 publications

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“…Back-pressure based adaptive traffic signal control and vehicle routing with real-time control information update (ARD-BP) [21].…”

Section: Simulation Setup and Resultsmentioning

confidence: 99%

“…In this work, we further improve [21] and propose an adaptive traffic control algorithm that controls traffic based on accurate real-time traffic information and global traffic information, where neighboring junction agents exchange traffic information to learn global traffic information.…”

Section: Contributionsmentioning

confidence: 99%

“…Duration of a slot is configured to be 15 seconds. Shadow vehicle generating probability is configured to be 0.02 and vehicle routing parameter β is configured to be 0.02 (routing parameter β is explained in [21]). We define vehicle traveling time to be the time it takes a vehicle to travel from its starting road to its destination.…”

Section: Task 3 Vehicle Routingmentioning

confidence: 99%

“…Some work also proposed back-pressure based algorithms to coordinate different vehicles [15,21]. In road network vehicles need time to travel from one road to another road which depends on vehicle speed and road length.…”

Section: Introductionmentioning

confidence: 99%

“…Directly applying back-pressure algorithm to control traffic as in [15] is not appropriate. Based on this observation, [21] proposed an adaptive traffic control algorithm which adapts back-pressure algorithm by considering vehicle traveling time on a road. Specifically, they control traffic signal and vehicle routes based on real-time traffic information, like vehicle speed and vehicle position.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning

Maipradit

Gao

Kawakami

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Nowadays traffic congestion has increasingly been a significant problem, which results in longer travel time and aggravates air pollution. Available work showed that back-pressure based traffic control algorithms can effectively reduce traffic congestion. However, those work control traffic based on either inaccurate traffic information or local traffic information, which causes inefficient traffic scheduling. In this paper, we propose an adaptive traffic control algorithm based on backpressure and Q-learning, which can efficiently reduce congestion. Our algorithm controls traffic based on accurate real-time traffic information and global traffic information learned by Q-learning. As verified by simulation, our algorithm significantly decreases average vehicle traveling time from 17% to 37% when compared with state-of-the-art algorithm under tested scenarios.

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“…Back-pressure based adaptive traffic signal control and vehicle routing with real-time control information update (ARD-BP) [21].…”

Section: Simulation Setup and Resultsmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Task 3 Vehicle Routingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning

Maipradit

Gao

Kawakami

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Self Cite

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Integrated real-time signal control and routing optimization: A two-stage rolling horizon framework with decentralized solution

Lin,

Hu,

et al. 2024

Transportation Research Part C: Emerging Technologies

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Backpressure control with estimated queue lengths for urban network traffic

Okoth²,

Jabari

2021

IET Intelligent Trans Sys

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Backpressure (BP) control was originally used for packet routing in communications networks. Since its first application to network traffic control, it has undergone different modifications to tailor it to traffic problems with promising results. Most of these BP variants are based on an assumption of perfect knowledge of traffic conditions throughout the network at all times, specifically the queue lengths (more accurately, the traffic volumes). However, it has been well established that accurate queue length information at signalized intersections is never available except in fully connected environments. Although connected vehicle technologies are developing quickly, a fully connected environment in the real world is still far. This paper tests the effectiveness of BP control when incomplete or imperfect knowledge about traffic conditions is available. BP control is combined with a speed/density field estimation module suitable for a partially connected environment. The proposed system is referred to as a BP with estimated queue lengths (BP-EQ). The robustness of BP-EQ is tested to varying levels of connected vehicle penetration, and BP-EQ is compared with the original BP (i.e. assuming accurate knowledge of traffic conditions), a real-world adaptive signal controller, and optimized fixed timing control using microscopic traffic simulation with field calibrated data. These results show that with a connected vehicle penetration rate as little as 10%, BP-EQ can outperform the adaptive controller and the fixed timing controller in terms of average delay, throughput, and maximum stopped queue lengths under high demand scenarios.

show abstract

Back-Pressure Based Adaptive Traffic Signal Control and Vehicle Routing with Real-Time Control Information Update

Cited by 8 publications

References 34 publications

Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning

Adaptive Traffic Control Algorithm Based on Back-Pressure and Q-Learning

Integrated real-time signal control and routing optimization: A two-stage rolling horizon framework with decentralized solution

Backpressure control with estimated queue lengths for urban network traffic

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