Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

Wu, Jian; Ghosal, Dipak; Zhang, Michael; Chuah, Chen‐Nee

doi:10.1109/tvt.2017.2760820

Cited by 61 publications

(29 citation statements)

References 24 publications

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“…Given the relationship between queue length and travel times, we propose a novel pressurebased policy for allocating green time. We do not use the policy of [19] because they choose the optimal phase per time step, whereas we must allocate green time to each phase at each time step to maintain the signal cycle structure. Consider an individual junction n with turning movements A 2 n .…”

Section: Max-pressure Control Policymentioning

confidence: 99%

“…Extensions to max-pressure signal control have included policies for a cycle-based phase structure [6], unknown routing proportions [13,14], realistic finite queue buffers [15,16], and adaptive route guidance [17,18]. Previous work has exclusively relied on queue length information for the signal control, with the exception of [19] which uses delay information from the head-of-line vehicle in each queue. The objective of this paper is quite similar.…”

Section: Introductionmentioning

confidence: 99%

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A simple crowdsourced delay-based traffic signal control

Dixit¹,

Nair²,

Chand³

et al. 2020

PLoS ONE

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Current transportation management systems rely on physical sensors that use traffic volume and queue-lengths. These physical sensors incur significant capital and maintenance costs. The ubiquity of mobile devices has made possible access to accurate and cheap traffic delay data. However, current traffic signal control algorithms do not accommodate the use of such data. In this paper, we propose a novel parsimonious model to utilize real-time crowdsourced delay data for traffic signal management. We demonstrate the versatility and effectiveness of the data and the proposed model on seven different intersections across three cities and two countries. This signal system provides an opportunity to leapfrog from physical sensors to low-cost, reliable crowdsourced data.

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Section: Max-pressure Control Policymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A simple crowdsourced delay-based traffic signal control

Dixit¹,

Nair²,

Chand³

et al. 2020

PLoS ONE

View full text Add to dashboard Cite

show abstract

“…Applying a better understanding of the traffic throughput at an intersection, many researchers had developed efficient ways to improve traffic throughputs, such as Liu and Chang who worked on an arterial signal optimization for intersections [6] and Li and Elefteriadou who worked on maximizing the throughput of turn bays at a signalized intersection [7]. Additional works may include the work from Zhou et al [8], Wu et al [9], and Chunhui et al [10]. As advanced traffic-related and automobile technologies are rapidly growing and 1 , 0 (2019) MATEC Web of Conferences https://doi.org/10.1051/matecconf/20192 25 5902008 0 9 ICTLE 2018 increasingly play an important role in our present society, new research related to the reduction of vehicle delay at intersections has become broader.…”

Section: Literature Reviewmentioning

confidence: 99%

Empirical Study on Maximum Traffic Throughputs at Intersections

Cheevarunothai

Kaewpikul

2019

MATEC Web Conf.

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Traffic congestion in big cities of Thailand has been a major problem seriously deterring economy growth. An average travel speed has become lower than 15 km/hour during peak-hour periods. One of the current major questions is how to find the most optimum green time that minimizes vehicle delay at an intersection. Since the identification of this optimum green time significantly depends on the knowledge of how fast vehicles can pass through an intersection, the information of the maximum traffic throughputs at intersections at different locations is indispensable. In this study, therefore, traffic counts were extracted manually from the videos recorded at 4 major intersections in Saensuk city. After the data were analyzed, the maximum traffic throughputs for different lane numbers and configurations were calculated and summarized. A better understanding of this maximum traffic throughput at intersections will lead to more accurate estimation of optimal green time.

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“…Signal timing is a collaborative optimization of spatiotemporal resources. The state-of-art methods are generally carried out under a predetermined scheme, with which the intersection capacity is usually hard to be fully exploited [7]. Few of them focused on the conflict-avoidance while improving the capacity of intersections [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

Liao

Liu

et al. 2020

IEEE Access

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The conflict between limited road resources and rapid car ownership makes the traffic signal timing become a pivotal challenge. Emerging studies have been carried out on adaptive signal timing, but most of them still focus on the throughput of intersections, leaving safety and travel experience unconsidered. This paper proposes a time difference penalized traffic signal timing method by reinforcement learning technique to balance safety and throughput capacity in traffic control system. Firstly, a microcosmic state representation is proposed to integrate the dynamics of both traffic lights and road vehicles, including driver behaviors of lane changing, car-following, previous phase of traffic light and its duration. Secondly, an action space, including 8 signal phases, and a behavior-aware reward function are designed to resist the red-light overflow. Finally, a partial long short term memory (LSTM) network is trained to balance traffic efficiency and traveling experience. In the network training, a parallel sampling method is adopted to obtain experience from multiple environments to accelerate the training convergence in practical application. Experimental results show that the proposed method improves the intersection efficiency up to 14.28% compared to the fixed signal timing and 5.26% compared to DQN while getting rid of red-light overflow time.

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Delay-Based Traffic Signal Control for Throughput Optimality and Fairness at an Isolated Intersection

Cited by 61 publications

References 24 publications

A simple crowdsourced delay-based traffic signal control

A simple crowdsourced delay-based traffic signal control

Empirical Study on Maximum Traffic Throughputs at Intersections

Time Difference Penalized Traffic Signal Timing by LSTM Q-Network to Balance Safety and Capacity at Intersections

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