scholarly journals Internet of Vehicles and Cost-Effective Traffic Signal Control

Sensors ◽  
2019 ◽  
Vol 19 (6) ◽  
pp. 1275 ◽  
Author(s):  
Sanghyun Ahn ◽  
Jonghwa Choi
Keyword(s):  
Queue Length ◽  
Cost Effective ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Vehicular Communication ◽  
Message Transmission ◽  
Length Estimation ◽  
Transmission Overhead ◽  
Control Traffic

The Internet of Vehicles (IoV) is attracting many researchers with the emergence of autonomous or smart vehicles. Vehicles on the road are becoming smart objects equipped with lots of sensors and powerful computing and communication capabilities. In the IoV environment, the efficiency of road transportation can be enhanced with the help of cost-effective traffic signal control. Traffic signal controllers control traffic lights based on the number of vehicles waiting for the green light (in short, vehicle queue length). So far, the utilization of video cameras or sensors has been extensively studied as the intelligent means of the vehicle queue length estimation. However, it has the deficiencies like high computing overhead, high installation and maintenance cost, high susceptibility to the surrounding environment, etc. Therefore, in this paper, we propose the vehicular communication-based approach for intelligent traffic signal control in a cost-effective way with low computing overhead and high resilience to environmental obstacles. In the vehicular communication-based approach, traffic signals are efficiently controlled at no extra cost by using the pre-equipped vehicular communication capabilities of IoV. Vehicular communications allow vehicles to send messages to traffic signal controllers (i.e., vehicle-to-infrastructure (V2I) communications) so that they can estimate vehicle queue length based on the collected messages. In our previous work, we have proposed a mechanism that can accomplish the efficiency of vehicular communications without losing the accuracy of traffic signal control. This mechanism gives transmission preference to the vehicles farther away from the traffic signal controller, so that the other vehicles closer to the stop line give up transmissions. In this paper, we propose a new mechanism enhancing the previous mechanism by selecting the vehicles performing V2I communications based on the concept of road sectorization. In the mechanism, only the vehicles within specific areas, called sectors, perform V2I communications to reduce the message transmission overhead. For the performance comparison of our mechanisms, we carry out simulations by using the Veins vehicular network simulation framework and measure the message transmission overhead and the accuracy of the estimated vehicle queue length. Simulation results verify that our vehicular communication-based approach significantly reduces the message transmission overhead without losing the accuracy of the vehicle queue length estimation.

scholarly journals A neural network algorithm for queue length estimation based on the concept of k-leader connected vehicles

2019 ◽  
Vol 27 (4) ◽  
pp. 341-354 ◽  
Author(s):  
Azadeh Emami ◽  
Majid Sarvi ◽  
Saeed Asadi Bagloee
Keyword(s):  
Neural Network ◽  
Queue Length ◽  
Travel Demand ◽  
Penetration Rate ◽  
Estimation Method ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Market Penetration ◽  
Length Estimation

AbstractThis paper presents a novel method to estimate queue length at signalised intersections using connected vehicle (CV) data. The proposed queue length estimation method does not depend on any conventional information such as arrival flow rate and parameters pertaining to traffic signal controllers. The model is applicable for real-time applications when there are sufficient training data available to train the estimation model. To this end, we propose the idea of “k-leader CVs” to be able to predict the queue which is propagated after the communication range of dedicated short-range communication (the communication platform used in CV system). The idea of k-leader CVs could reduce the risk of communication failure which is a serious concern in CV ecosystems. Furthermore, a linear regression model is applied to weigh the importance of input variables to be used in a neural network model. Vissim traffic simulator is employed to train and evaluate the effectiveness and robustness of the model under different travel demand conditions, a varying number of CVs (i.e. CVs’ market penetration rate) as well as various traffic signal control scenarios. As it is expected, when the market penetration rate increases, the accuracy of the model enhances consequently. In a congested traffic condition (saturated flow), the proposed model is more accurate compared to the undersaturated condition with the same market penetration rates. Although the proposed method does not depend on information of the arrival pattern and traffic signal control parameters, the results of the queue length estimation are still comparable with the results of the methods that highly depend on such information. The proposed algorithm is also tested using large size data from a CV test bed (i.e. Australian Integrated Multimodal Ecosystem) currently underway in Melbourne, Australia. The simulation results show that the model can perform well irrespective of the intersection layouts, traffic signal plans and arrival patterns of vehicles. Based on the numerical results, 20% penetration rate of CVs is a critical threshold. For penetration rates below 20%, prediction algorithms fail to produce reliable outcomes.

scholarly journals Intelligent Traffic Signal Phase Distribution System Using Deep Q-Network

2022 ◽  
Vol 12 (1) ◽  
pp. 425
Author(s):  
Hyunjin Joo ◽  
Yujin Lim
Keyword(s):  
Control System ◽  
Standard Deviation ◽  
Traffic Congestion ◽  
Queue Length ◽  
Smart Cities ◽  
Traffic Signal ◽  
Optimal Order ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Smart Transportation

Traffic congestion is a worsening problem owing to an increase in traffic volume. Traffic congestion increases the driving time and wastes fuel, generating large amounts of fumes and accelerating environmental pollution. Therefore, traffic congestion is an important problem that needs to be addressed. Smart transportation systems manage various traffic problems by utilizing the infrastructure and networks available in smart cities. The traffic signal control system used in smart transportation analyzes and controls traffic flow in real time. Thus, traffic congestion can be effectively alleviated. We conducted preliminary experiments to analyze the effects of throughput, queue length, and waiting time on the system performance according to the signal allocation techniques. Based on the results of the preliminary experiment, the standard deviation of the queue length is interpreted as an important factor in an order allocation technique. A smart traffic signal control system using a deep Q-network , which is a type of reinforcement learning, is proposed. The proposed algorithm determines the optimal order of a green signal. The goal of the proposed algorithm is to maximize the throughput and efficiently distribute the signals by considering the throughput and standard deviation of the queue length as reward parameters.

scholarly journals Continuous residual reinforcement learning for traffic signal control optimization

2018 ◽  
Vol 45 (8) ◽  
pp. 690-702 ◽  
Author(s):  
Mohammad Aslani ◽  
Stefan Seipel ◽  
Marco Wiering
Keyword(s):  
Reinforcement Learning ◽  
Fixed Time ◽  
Traffic Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Microscopic Traffic Simulation ◽  
Control Optimization ◽  
Large State Space ◽  
Average Travel Time ◽  
Control Traffic

Traffic signal control can be naturally regarded as a reinforcement learning problem. Unfortunately, it is one of the most difficult classes of reinforcement learning problems owing to its large state space. A straightforward approach to address this challenge is to control traffic signals based on continuous reinforcement learning. Although they have been successful in traffic signal control, they may become unstable and fail to converge to near-optimal solutions. We develop adaptive traffic signal controllers based on continuous residual reinforcement learning (CRL-TSC) that is more stable. The effect of three feature functions is empirically investigated in a microscopic traffic simulation. Furthermore, the effects of departing streets, more actions, and the use of the spatial distribution of the vehicles on the performance of CRL-TSCs are assessed. The results show that the best setup of the CRL-TSC leads to saving average travel time by 15% in comparison to an optimized fixed-time controller.

scholarly journals BACKPRESSURE BASED TRAFFIC SIGNAL CONTROL CONSIDERING CAPACITY OF DOWNSTREAM LINKS

Transport ◽  
2020 ◽  
Vol 35 (4) ◽  
pp. 347-356
Author(s):  
Shenxue Hao ◽  
Licai Yang ◽  
Yunfeng Shi ◽  
Yajuan Guo
Keyword(s):  
Queue Length ◽  
Control Algorithm ◽  
Mesh Networks ◽  
Traffic Signal ◽  
Urban Traffic ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Network ◽  
Maximum Throughput ◽  
Urban Traffic Network

Congestion is a kind of expression of instability of traffic network. Traffic signal control keeping traffic network stable can reduce the congestion of urban traffic. In order to improve the efficiency of urban traffic network, this study proposes a decentralized traffic signal control strategy based on backpressure algorithm used in Wi-Fi mesh networks for packets routing. Backpressure based traffic signal control algorithm can stabilize urban traffic network and achieve maximum throughput. Based on original backpressure algorithm, the variant parameter and penalty function are considered to balance the queue differential and capacity of downstream links in urban traffic network. For each traffic phase of intersections, phase weight is computed using queue differential and capacity of downstream links, which fixed the deficiency of infinite queue capacity in original backpressure algorithm. It is proved that the extended backpressure traffic signal control algorithm can maintain stability of urban traffic network, and also can prevent queue spillback, so as to improve performance of whole traffic network. Simulations are carried out in Vissim using Vissim COM programming interface and Visual Studio development tools. Evaluation results illuminate that it can get better performance than the backpressure algorithm just based on queue length differential in average queue length and delay of traffic network.

scholarly journals Traffic Signal Control via Reinforcement Learning for Reducing Global Vehicle Emission

2021 ◽  
Vol 13 (20) ◽  
pp. 11254
Author(s):  
Bálint Kővári ◽  
Lászlo Szőke ◽  
Tamás Bécsi ◽  
Szilárd Aradi ◽  
Péter Gáspár
Keyword(s):  
Reinforcement Learning ◽  
Queue Length ◽  
Emission Reduction ◽  
Control Method ◽  
Traffic Signal ◽  
Vehicle Emission ◽  
Signal Control ◽  
Traffic Signal Control ◽  
The Core ◽  
Reward Shaping

The traffic signal control problem is an extensively researched area providing different approaches, from classic methods to machine learning based ones. Different aspects can be considered to find an optima, from which this paper emphasises emission reduction. The core of our solution is a novel rewarding concept for deep reinforcement learning (DRL) which does not utilize any reward shaping, hence exposes new insights into the traffic signal control (TSC) problem. Despite the omission of the standard measures in the rewarding scheme, the proposed approach can outperform a modern actuated control method in classic performance measures such as waiting time and queue length. Moreover, the sustainability of the realized controls is also placed under investigation to evaluate their environmental impacts. Our results show that the proposed solution goes beyond the actuated control not just in the classic measures but in emission-related measures too.

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