Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

Marsetič, Rok; Šemrov, Darja; Žura, Marijan

doi:10.7307/ptt.v26i2.1318

Cited by 10 publications

(6 citation statements)

References 13 publications

(12 reference statements)

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“…Due to the characteristics of discrete and limited action space in traffic signal control, Q-learning becomes the most common algorithm of RL used in this area. Related works [13][14][15][16][17][18][19] using this algorithm all achieve satisfying results. However, with an increase in the complexity of the environment, a computer may run out of memory; further, searching for a certain state from a large Qtable is time-consuming.…”

Section: Introductionmentioning

confidence: 89%

“…With regard to state space, action space, and reward function, people's choices vary. In general terms, the definitions and representations of state space in existing papers (e.g., total number of queued vehicles [12, 19-21, 27, 29], length of queued vehicles [12], speed of vehicles [11,18,23,27], or traffic flow [15,30]) can be modified to relay more effective information about the environment, which leads to more accurate judgments about the actions. e action space has been defined as all available signal phases [11,18,20,27,30,31], or alternatively, it has been defined to maintain a sequence [22].…”

Section: Introductionmentioning

confidence: 99%

“…e action space has been defined as all available signal phases [11,18,20,27,30,31], or alternatively, it has been defined to maintain a sequence [22]. As for the definition of a reward function, most studies choose a reduction in the travel time of a vehicle [11,22,23], length of a vehicle queue [13,15], or the time delay in queuing [11,19,20,26,28,30]. Others [18] use the increase of throughput as reward, or the difference in queue length in different directions [24,25,27].…”

Section: Introductionmentioning

confidence: 99%

“…However, the current research has common problems and still requires improvements. First, most of these works [11,13,15,[22][23][24][25] focus on improvements at a single intersection and will not be satisfying enough when used in reallife situations. ey may not result in an overall performance improvement, as such a policy only focuses on a small range and can cause congestions in upstream and downstream roads.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

et al. 2020

Journal of Advanced Transportation

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Controlling traffic signals to alleviate increasing traffic pressure is a concept that has received public attention for a long time. However, existing systems and methodologies for controlling traffic signals are insufficient for addressing the problem. To this end, we build a truly adaptive traffic signal control model in a traffic microsimulator, i.e., “Simulation of Urban Mobility” (SUMO), using the technology of modern deep reinforcement learning. The model is proposed based on a deep Q-network algorithm that precisely represents the elements associated with the problem: agents, environments, and actions. The real-time state of traffic, including the number of vehicles and the average speed, at one or more intersections is used as an input to the model. To reduce the average waiting time, the agents provide an optimal traffic signal phase and duration that should be implemented in both single-intersection cases and multi-intersection cases. The co-operation between agents enables the model to achieve an improvement in overall performance in a large road network. By testing with data sets pertaining to three different traffic conditions, we prove that the proposed model is better than other methods (e.g., Q-learning method, longest queue first method, and Webster fixed timing control method) for all cases. The proposed model reduces both the average waiting time and travel time, and it becomes more advantageous as the traffic environment becomes more complex.

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Section: Introductionmentioning

confidence: 89%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

et al. 2020

Journal of Advanced Transportation

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“…This work aims to enable complex problem modeling and resolution in order to achieve a distributed control; The MAS paradigm alone is not able to achieve capabilities specifically dedicated to EVs management and the reason behind that is because of their generic conceptual framework and lack of built-in adaptation mechanisms [7]; Several computational intelligence techniques were combined with MAS to achieve reactive and adaptive control of traffic signals [8,33]; Despite their success and popularity, these approaches still face some limitations, including control of disturbances related to EVs and assessment of performance under real life situations [21].…”

Section: An Overview On Intelligent Control Approachesmentioning

confidence: 99%

Multi-agent preemptive longest queue first system to manage the crossing of emergency vehicles at interrupted intersections

Louati

Elkosantini

Darmoul³

et al. 2018

Eur. Transp. Res. Rev.

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Favouring the crossing of Emergency Vehicles (EVs) through intersections in urban cities is very critical for people lives. There have been several efforts toward developing Traffic Signal Control Systems (TSCS) dedicated to control efficiently the traffic flow, but few are the efforts toward developing Traffic Signal Priority Systems (TSPS) dedicated to favour the crossing of EVs (such as ambulances, firefighters, police cars, etc.). Multi-Agent Systems were considered to develop several distributed TSCS, while very few works have developed distributed TSPS. Such systems lack on dealing with the EVs crossing issues while maintaining a fluid state of the traffic. In the literature, the Longest Queue First-Maximal Weight Matching (LQF-MWM) is proved to guarantee a stable TSCS. Recently, the LQF-MWM technique is increasingly used to benchmarck and assess adaptive TSCS. Moreover, the preemption is one of the most effective techniques used to prioritise the crossing of EVs. This paper is the first to rely on LQF-MWM assumptions, preemption technique, and Multi-Agent Systems to design a distributed TSPS. The suggested system has two main purposes, which are the guidance of EVs and the control of traffic signals. Nine agents are implemented to govern a network of nine intersections, where each agent uses the Multi Agent System based Preemptive Longest Queue First-Maximal Weight Matching. We have referred to VISSIM traffic simulation software for benchmarking and analysis. To assess the suggested system, we have developed a distributed and preemptive version of VISSIM Optimized Stage-Based Fixed-Time algorithm. Python is considered to develop the suggested systems, and Spade platform is considered as agents' platform. Several Key Performance Indicators are considered to assess the performance of all controllers including delay time, travel time, vehicles queue occupancy, number of stops, distance traversed, and speed. Experimental results show a competitive performance of the developed system to maintain a fluid traffic and guide efficiency EVs.

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A hybridization of deep learning techniques to predict and control traffic disturbances

Louati

2020

Artif Intell Rev

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Road Artery Traffic Light Optimization with Use of the Reinforcement Learning

Abstract: ABSTRACT

Cited by 10 publications

References 13 publications

Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

Adaptive Traffic Signal Control Model on Intersections Based on Deep Reinforcement Learning

Multi-agent preemptive longest queue first system to manage the crossing of emergency vehicles at interrupted intersections

A hybridization of deep learning techniques to predict and control traffic disturbances

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