Dynamic traffic routing in a network with adaptive signal control

Chai, Huajun; Zhang, H.M.; Ghosal, Dipak; Chuah, Chen‐Nee

doi:10.1016/j.trc.2017.08.017

Cited by 64 publications

(33 citation statements)

References 35 publications

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“…To tackle this limitation, an approach based on Approximate Dynamic Programming has been proposed [28], yet all of the solutions above do not integrate with adaptive signal control. Recent researches considered adaptive signal control and dynamic vehicles routing [29], [30]. However, they only focused on providing adaptive route guidance for individual vehicles, not coordinating different vehicles.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Traffic Signal Control Scheme Based on Back-pressure with Global Information

Maipradit

Kawakami

Liu

et al. 2021

Journal of Information Processing

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Nowadays traffic congestion has increasingly been a significant problem, which results in a longer travel time and aggravates air pollution. Available works showed that back-pressure based traffic control algorithms can effectively reduce traffic congestion. However, those works 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 back-pressure 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 38% when compared with a state-of-the-art algorithm under tested scenarios.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Traffic Signal Control Scheme Based on Back-pressure with Global Information

Maipradit

Kawakami

Liu

et al. 2021

Journal of Information Processing

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“…This place is called checkpoints and should be located at a sufficient distance from the city center. [3], [4], [5] In the picture, we can see the red dots. These points indicate checkpoints that should be used to retrieve data.…”

Section: Dynamic Operation Control Designmentioning

confidence: 99%

Free Signal Management in Cities Using Density and Average Speed

Mrázek

2020

Wseas Transactions on Signal Processing

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The article focuses on the issue of congested roads in cities. The problems of heavy traffic both large and smaller towns. Some towns in the Czech Republic do not have built bypasses and so there is a heavy traffic congestion at favorite times. Road transport has become one of the most popular forms of transport. This trend is unlikely to change in the near future. The current market offers dynamic traffic management but not with the appropriate effect. Our proposed method belongs to dynamic traffic control based on real traffic control. The proposed method works in real time with the light signaling device behind the monitoring sections. The function of the proposed method is to extend the free passage of the light-signaling device.

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“…Similarly, Aslani et al (2017) exploit actor-critic method to perform adaptive signal traffic control, and show how their approach is able to cope with different traffic distruption events. The approach proposed by Chai et al (2017) deals with exceptional and unexpected events by dynamically rerouting traffic; similar approaches are also developed for usual traffic conditions (Barthélemy & Carletti 2017). As it is apparent, the two lines of research (controlling traffic lights and routing traffic) are complementary, and it is envisaged that they will be combined in the future.…”

Section: Related Workmentioning

confidence: 99%

Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

Antoniou

Batsakis

Davies

et al. 2019

Transportation Research Part C: Emerging Technologies

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Improving a city's infrastructure is seen as a crucial part of its sustainability, leading to efficiencies and opportunities driven by technology integration. One significant step is to support the integration and enrichment of a broad variety of data, often using state of the art linked data approaches. Among the many advantages of such enrichment is that this may enable the use of intelligent processes to autonomously manage urban facilities such as traffic signal controls. In this paper we document an attempt to integrate sets of sensor and historical data using a data hub and a set of ontologies for the data. We argue that access to such high level integrated data sources leads to the enhancement of the capabilities of an urban transport operator. We demonstrate this by documenting the development of a planning agent which uses such data as inputs in the form of logic statements, and when given traffic goals to achieve, outputs complex traffic signal strategies which help transport operators deal with exceptional events such as road closures or road traffic saturation. The aim is to create an autonomous agent which reacts to commands from transport operators in the face of exceptional events involving saturated roads, and creates, executes and monitors plans to deal with the effects of such events. We evaluate the intelligent agent in a region of a large urban area, under the direction of urban transport operators.

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Dynamic traffic routing in a network with adaptive signal control

Cited by 64 publications

References 35 publications

An Adaptive Traffic Signal Control Scheme Based on Back-pressure with Global Information

An Adaptive Traffic Signal Control Scheme Based on Back-pressure with Global Information

Free Signal Management in Cities Using Density and Average Speed

Enabling the use of a planning agent for urban traffic management via enriched and integrated urban data

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