Distributed and adaptive traffic signal control within a realistic traffic simulation

McKenney, Dave; White, Tony

doi:10.1016/j.engappai.2012.04.008

Cited by 97 publications

(34 citation statements)

References 19 publications

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“…On the other hand, traffic flow prediction can cover larger regions based on historical traffic data [8]. The adaptive traffic signal control [9,10] is one of the most effective ways to improve the road environment. Adaptive traffic signal control takes the action of switching the traffic phase or adjusting the traffic signal length.…”

Section: Introductionmentioning

confidence: 99%

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Kim

Jeong

2019

Sensors

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As traffic congestion in cities becomes serious, intelligent traffic signal control has been actively studied. Deep Q-Network (DQN), a representative deep reinforcement learning algorithm, is applied to various domains from fully-observable game environment to traffic signal control. Due to the effective performance of DQN, deep reinforcement learning has improved speeds and various DQN extensions have been introduced. However, most traffic signal control researches were performed at a single intersection, and because of the use of virtual simulators, there are limitations that do not take into account variables that affect actual traffic conditions. In this paper, we propose a cooperative traffic signal control with traffic flow prediction (TFP-CTSC) for a multi-intersection. A traffic flow prediction model predicts future traffic state and considers the variables that affect actual traffic conditions. In addition, for cooperative traffic signal control in multi-intersection, each intersection is modeled as an agent, and each agent is trained to take best action by receiving traffic states from the road environment. To deal with multi-intersection efficiently, agents share their traffic information with other adjacent intersections. In the experiment, TFP-CTSC is compared with existing traffic signal control algorithms in a 4 × 4 intersection environment. We verify our traffic flow prediction and cooperative method.

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Section: Introductionmentioning

confidence: 99%

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Kim

Jeong

2019

Sensors

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“…The typical urban traffic signal control systems are TRAN-SYT (Wong et al 2002), SCOOT (Robertson, Bretherton 1991) and SCATS (Sims, Dobinson 1980). There are several difficulties and challenges in the previous traffic signal optimization research Kerner 2002;McKenney, White 2013):…”

Section: Introductionmentioning

confidence: 99%

A Multi-Intersection Model and Signal Timing Plan Algorithm for Urban Traffic Signal Control

Wang

et al. 2014

Transport

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Abstract. The urban traffic signal control system is complex, non-linear and non-equilibrium in real conditions. The existing methods could not satisfy the requirement of real-time and dynamic control. In order to solve these difficulties and challenges, this paper proposes a novel Multi-Intersection Model (MIM) based on Cellular Automata (CA) and a Multi-Intersection Signal Timing Plan Algorithm (MISTPA), which can reduce the delay time at each intersection and effectively alleviate the traffic pressure on each intersection in the urban traffic network. Our work is divided into several parts: (1) a multi-intersection model based on CA is defined to build the dynamic urban traffic network; (2) MISTPA is proposed, which truly reflects the real-time demand degree to green time of the traffic flow at each intersection. The MISTPA is composed Single Intersection Volume Algorithm (SIVA), Single-Lane Volume Algorithm (SLVA) and single intersection signal timing plan algorithm (SISTPA). Extensive experiments show that when the saturation is greater than 0.3, the MIM and the MISTPA achieve good performance, and can significantly reduce the vehicle delay time at each intersection. The average delay time of the traffic flow at each intersection can obviously be reduced. Finally, a practical case study demonstrates that the proposed model and the corresponding algorithm are correct and effective.

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“…These maps can later be used to optimize traffic light cycles [12], to study the placement of LED Panels throughout the city [14], to reroute vehicles with the aim of reducing travel times [13], and for many other uses in smart mobility [1,5,6,9].…”

Section: Introductionmentioning

confidence: 99%

An Evolutionary Algorithm to Generate Real Urban Traffic Flows

Stolfi

Alba

2015

Advances in Artificial Intelligence

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Abstract. In this article we present a strategy based on an evolutionary algorithm to calculate the real vehicle flows in cities according to data from sensors placed in the streets. We have worked with a map imported from OpenStreetMap into the SUMO traffic simulator so that the resulting scenarios can be used to perform different optimizations with the confidence of being able to work with a traffic distribution close to reality. We have compared the results of our algorithm to other competitors and achieved results that replicate the real traffic distribution with a precision higher than 90%.

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Distributed and adaptive traffic signal control within a realistic traffic simulation

Cited by 97 publications

References 19 publications

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

Cooperative Traffic Signal Control with Traffic Flow Prediction in Multi-Intersection

A Multi-Intersection Model and Signal Timing Plan Algorithm for Urban Traffic Signal Control

An Evolutionary Algorithm to Generate Real Urban Traffic Flows

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