Nonlinear gating control for urban road traffic network using the network fundamental diagram

Csikós, Alfréd András; Tettamanti, Tamás; Varga, István

doi:10.1002/atr.1291

Cited by 17 publications

(11 citation statements)

References 35 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In case of macroscopic approach, the traditional vehicleconservation law remains the same of course. However, the characteristic values of the macroscopic fundamental diagram (speed-flow-density relationship) and therefore the shape of that will significantly alter as well (Csikós et al, 2015). On the one hand, critical density (the maximum point of flow-density diagram where traffic system obtain the maximal throughput) and the whole polynomial curve will shift to right (see Fig.…”

Section: Impact Of Autonomous Vehicles To Classical Traffic Modelsmentioning

confidence: 99%

Impacts of Autonomous Cars from a Traffic Engineering Perspective

Tettamanti

Varga²,

Szalay³

2016

Period. Polytech. Transp. Eng.

Self Cite

View full text Add to dashboard Cite

The era of autonomous vehicles infer new challenges in several fields. When autonomous vehicles take over the road in a large volume, consumer preferences around car-ownership will transform, traffic modeling and control will need correction, and moreover hackers will appear. These are just a few impacts which are expectable in the near future. Accordingly, the paper's aim is to enlighten trends and upcoming challenges of driverless vehicles and automated transportation system from a transportation engineering perspective.

show abstract

Section: Impact Of Autonomous Vehicles To Classical Traffic Modelsmentioning

confidence: 99%

Impacts of Autonomous Cars from a Traffic Engineering Perspective

Tettamanti

Varga²,

Szalay³

2016

Period. Polytech. Transp. Eng.

Self Cite

View full text Add to dashboard Cite

show abstract

“…The conventional model-based approaches for the optimization of perimeter control have been effectively applied to some relatively small-sized areas, such as the gating control of a single protected network (PN) [7][8][9] or one-to-one transfer flow control for two regions [10,11]. However, there has been a growing interest in applying perimeter control to a widened urban area in recent studies [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation

et al. 2020

View full text Add to dashboard Cite

Perimeter control is an emerging alternative for traffic signal control, which regulates the traffic flows on the periphery of a road network. Some model-based approaches have been suggested earlier for the optimization of perimeter control based on macroscopic fundamental diagrams (MFDs). However, there are several limitations when considering their application to a large-scale urban area because the model-based approaches may not be scalable to multiple regions and inappropriate for handling various effects caused by the shape change of MFDs. Therefore, we propose a model-free and data-driven approach that combines reinforcement learning (RL) with the macroscopic traffic simulation based on the recently developed network transmission model. First, we design four perimeter control models with different macroscopic traffic variables and parametrizations. Then, we validate the proposed models by evaluating their performances with the test demand scenarios at different levels. The validation results show that the model containing travel demand information adapts to a new demand scenario better than the model containing only density-related factors.

show abstract

“…The researchers hope that the model of the control system is based on the data model identification rather than the existing mechanism model [4]. Besides, they hope that the system is based on realtime monitoring data rather than the traffic forecast data [5] and the control system can automatically adjust the control strategy instead of the manual intervention [6].…”

Section: Introductionmentioning

confidence: 99%

A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

Wang

Yang

Liang

et al. 2018

Journal of Advanced Transportation

110

View full text Add to dashboard Cite

The self-adaptive traffic signal control system serves as an effective measure for relieving urban traffic congestion. The system is capable of adjusting the signal timing parameters in real time according to the seasonal changes and short-term fluctuation of traffic demand, resulting in improvement of the efficiency of traffic operation on urban road networks. The development of information technologies on computing science, autonomous driving, vehicle-to-vehicle, and mobile Internet has created a sufficient abundance of acquisition means for traffic data. Great improvements for data acquisition include the increase of available amount of holographic data, available data types, and accuracy. The article investigates the development of commonly used self-adaptive signal control systems in the world, their technical characteristics, the current research status of self-adaptive control methods, and the signal control methods for heterogeneous traffic flow composed of connected vehicles and autonomous vehicles. Finally, the article concluded that signal control based on multiagent reinforcement learning is a kind of closed-loop feedback adaptive control method, which outperforms many counterparts in terms of real-time characteristic, accuracy, and self-learning and therefore will be an important research focus of control method in future due to the property of “model-free” and “self-learning” that well accommodates the abundance of traffic information data. Besides, it will also provide an entry point and technical support for the development of Vehicle-to-X systems, Internet of vehicles, and autonomous driving industries. Therefore, the related achievements of the adaptive control system for the future traffic environment have extremely broad application prospects.

show abstract

Nonlinear gating control for urban road traffic network using the network fundamental diagram

Cited by 17 publications

References 35 publications

Impacts of Autonomous Cars from a Traffic Engineering Perspective

Impacts of Autonomous Cars from a Traffic Engineering Perspective

Design of reinforcement learning for perimeter control using network transmission model based macroscopic traffic simulation

A Review of the Self-Adaptive Traffic Signal Control System Based on Future Traffic Environment

Contact Info

Product

Resources

About