Optimization Algorithm of Real-Timing Signal Control for Isolated Intersections

2014 ◽  
Vol 1037 ◽  
pp. 322-326
Author(s):  
Hui Hua Shi ◽  
Hui jin Chen ◽  
Qing He
Keyword(s):  
Optimization Algorithm ◽  
Queue Length ◽  
Fixed Time ◽  
Utilization Rate ◽  
Time Signal ◽  
Signal Control ◽  
Average Delay ◽  
Simulation Results ◽  
Time Utilization ◽  
Isolated Intersection

To make full use of green time of each phase to alleviate congestion in urban isolated intersections, an optimization algorithm of real-timing signal control is proposed. By monitoring traffic flow at the isolated intersection, the average utilization rate of green time of each phase is calculated. The green time of each phase is then adjusted automatically to maintain a balanced green time utilization rate with the presented optimization algorithm. Simulation results indicate that the average delay and the queue length of the presented algorithm are less than the fixed-time signal plan at peak times, and the efficiency of the intersection traffic is improved.

scholarly journals Deep Q-network-based traffic signal control models

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0256405
Author(s):  
Sangmin Park ◽  
Eum Han ◽  
Sungho Park ◽  
Harim Jeong ◽  
Ilsoo Yun
Keyword(s):  
Reinforcement Learning ◽  
Urban Areas ◽  
Control Method ◽  
Fixed Time ◽  
Traffic Signal ◽  
Time Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Control Models ◽  
Isolated Intersection

Traffic congestion has become common in urban areas worldwide. To solve this problem, the method of searching a solution using artificial intelligence has recently attracted widespread attention because it can solve complex problems such as traffic signal control. This study developed two traffic signal control models using reinforcement learning and a microscopic simulation-based evaluation for an isolated intersection and two coordinated intersections. To develop these models, a deep Q-network (DQN) was used, which is a promising reinforcement learning algorithm. The performance was evaluated by comparing the developed traffic signal control models in this research with the fixed-time signal optimized by Synchro model, which is a traffic signal optimization model. The evaluation showed that the developed traffic signal control model of the isolated intersection was validated, and the coordination of intersections was superior to that of the fixed-time signal control method.

Comparison of NETSIM, NETFLO I, and NETFLO II Traffic Simulation Models for Fixed-Time Signal Control

1996 ◽  
Vol 1566 (1) ◽  
pp. 20-30 ◽  
Author(s):  
Sunil Taori ◽  
Ajay K. Rathi
Keyword(s):  
Adaptive Systems ◽  
Traffic Simulation ◽  
Simulation Models ◽  
Fixed Time ◽  
Time Signal ◽  
Signal Control ◽  
Urban Street ◽  
Street Networks ◽  
Real Time Traffic ◽  
Measures Of Effectiveness

This paper documents a comparative analysis of the NETSIM, NETFLO I, and NETFLO II traffic simulation models when simulating traffic networks with fixed-time signal control. The objective was to find out whether the results of simulating the same traffic network with the three models were compatible. The three models employ different approaches to simulate traffic flow on urban street networks. Four different scenarios with varying network configurations and intersection geometries were simulated for three volume levels. The average speed and delay measures of effectiveness generated by the three models were compared for each scenario. Analysis of variance techniques were used for statistical analyses of the simulation output data. The execution speeds of the models were also compared. The quickness of simulation will be very important in the ITS applications, real-time traffic adaptive systems, and so forth. The results of this study indicated that the estimated measures of effectiveness from the three models were statistically significantly different at each level of comparison. The speed values generated by NETSIM were found to be the lowest; NETFLO II values were highest. NETFLO I values in all cases were between NETFLO II and NETSIM values. In most cases, NETFLO I values were closer to NETFLO II than to NETSIM values. Identical results were obtained from the analysis of delay values, with NETSIM delay estimates always being highest and NETFLO II values lowest. The program execution time for NETSIM and NETFLO I increased significantly as the volume level increased, whereas it practically did not change for NETFLO II. The execution times for NETSIM were found to be about 20 to 45 times higher than for NETFLO II. Even NETFLO I was found to be about 5 to 10 times faster than NETSIM.

scholarly journals A real-time signal control strategy at an isolated pedestrian crossing based on radar data

2019 ◽  
Vol 11 (3) ◽  
pp. 168781401982590 ◽  
Author(s):  
Xu Qu ◽  
Tangyi Guo ◽  
Jin Guo ◽  
Yi Lin ◽  
Bin Ran
Keyword(s):  
Real Time ◽  
Control Strategy ◽  
Fixed Time ◽  
Radar Data ◽  
Traffic Signal ◽  
Time Signal ◽  
Signal Control ◽  
Traffic Signal Control ◽  
Traffic Delay ◽  
Pedestrian Crossing

Fixed-time traffic signal control strategy in an isolated pedestrian crossing tends to reduce traffic capacity and expose vulnerable road users to more danger. To mitigate the negative impact of previous control strategy, this study proposed an optimal real-time signal timing strategy to protect pedestrian crossing and at the same time minimize the system-wide traffic delay. With the application of a wide-area radar data, the features of vehicles, pedestrians, and the passing time of non-motor vehicles and pedestrian were captured considering conflicts and traffic delay. The support vector machine for regression was utilized to hypothesize traffic delay by training. The discrete values of hypothetical passing time will be tested. The minimum value of delay can be recognized and the corresponding hypothetical passing time will be recommended as the green time for crossing. The performance of the proposed ORSTS outperformed the fixed-time traffic signal control strategy in reducing traffic delay by 22.3%.

scholarly journals Resilience Analysis of Urban Road Networks Based on Adaptive Signal Controls: Day-to-Day Traffic Dynamics with Deep Reinforcement Learning

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Wen-Long Shang ◽  
Yanyan Chen ◽  
Xingang Li ◽  
Washington Y. Ochieng
Keyword(s):  
Reinforcement Learning ◽  
Road Networks ◽  
Flow Distribution ◽  
Fixed Time ◽  
Time Signal ◽  
Signal Control ◽  
Dynamic Learning ◽  
Urban Road ◽  
Adaptive Signal Control ◽  
Adaptive Signal

Improving the resilience of urban road networks suffering from various disruptions has been a central focus for urban emergence management. However, to date the effective methods which may mitigate the negative impacts caused by the disruptions, such as road accidents and natural disasters, on urban road networks is highly insufficient. This study proposes a novel adaptive signal control strategy based on a doubly dynamic learning framework, which consists of deep reinforcement learning and day-to-day traffic dynamic learning, to improve the network performance by adjusting red/green time split. In this study, red time split is regarded as extra traffic flow to discourage drivers to use affected roads, so as to reduce congestion and improve the resilience when urban road networks are subject to different levels of disruptions. In addition, we utilize the convolution neural network as Q-network to approximate Q values, link flow distribution and link capacity are regarded as the state space, and actions are denoted as red/green time split. A small network is utilized as a numerical example, and a fixed time signal control and other two adaptive signal controls are employed for the comparisons with the proposed one. The results show that the proposed adaptive signal control based on deep reinforcement learning can achieve better resilience in most of the cases, particularly in the scenarios of moderate and severe disruptions. This study may shed light on the advantages of the proposed adaptive signal control dealing with major emergencies compared to others.

scholarly journals REVIEW AND THE STATE OF THE ART PADA PERSIMPANGAN BERSINYAL DENGAN MODEL FUZZY LOGIC

2016 ◽  
Vol 18 (2) ◽  
Author(s):  
Moch. Duddy Studyana
Keyword(s):  
Fuzzy Logic ◽  
Delay Time ◽  
State Of The Art ◽  
Fixed Time ◽  
The State ◽  
Time Signal ◽  
Traffic Light ◽  
Isolated Intersection

Masalah klasik kondisi lalu-lintas perkotaan adalah sering timbulnya kemacetan (jam), antrian (queue), penurunan kapasitas simpang (drop capacity) dan waktu tunggu (delay time) yang lama saat melewati persimpangan. Keberhasilan dalam menangani suatu persimpangan akan menjadikan tolok ukur guna mengevaluasi kinerja simpang (intersection performance). Kenyataan yang terjadi dilapangan pengaturan sinyal lalu-lintas (traffic light) seringkali dilakukan dengan melibatkan berdasarkan variabel yang bersifat numerik atau kuantitatif, padahal variabel yang bersifat linguistik atau kualitatif sering diabaikan dan ada kecenderungan tidak pernah dipertimbangkan. Ternyata penggunaan model fuzzy logic dapat memberikan kontribusi cukup besar dalam menangani kondisi persimpangan bersinyal, terutama pada penelitian ini dievaluasi terhadap waktu sinyal tetap (fixed time signal) dan simpang yang terisolasi (isolated intersection), karena dapat melibatkan analisa kombinasi variabel numerik dan linguistik. Hasil review dan penelaahan secara the state of the art dari beberapa penelitian terdahulu, diharapkan akan menjadi terobosan baru untuk memberikan solusi terbaik bagi pengaturan sinyal lalu-lintas, khususnya simpang terisolasi yang berada di Indonesia.

Development and Evaluation of Cooperative Intersection Management Algorithm under Connected and Automated Vehicles Environment

2021 ◽  
pp. 036119812199458
Author(s):  
Slobodan Gutesa ◽  
Joyoung Lee ◽  
Dejan Besenski
Keyword(s):  
Travel Time ◽  
Information Exchange ◽  
Fixed Time ◽  
Optimization Method ◽  
Time Signal ◽  
Market Penetration ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Concept Evaluation ◽  
Intersection Management

Recent technological advancements in the automotive and transportation industry established a firm foundation for development and implementation of various connected and automated vehicle solutions around the globe. Wireless communication technologies such as the dedicated short-range communication protocol are enabling information exchange between vehicles and infrastructure. This research paper introduces an intersection management strategy for a corridor with automated vehicles utilizing vehicular trajectory-driven optimization method. Trajectory-Driven Optimization for Automated Driving provides an optimal trajectory for automated vehicles based on current vehicle position, prevailing traffic, and signal status on the corridor. All inputs are used by the control algorithm to provide optimal trajectories for automated vehicles, resulting in the reduction of vehicle delay along the signalized corridor with fixed-time signal control. The concept evaluation through microsimulation reveals that, even with low market penetration (i.e., less than 10%), the technology reduces overall travel time of the corridor by 2%. Further increase in market penetration produces travel time and fuel consumption reductions of up to 19.5% and 22.5%, respectively.

scholarly journals Latency-Optimal Computational Offloading Strategy for Sensitive Tasks in Smart Homes

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2347
Author(s):  
Yanyan Wang ◽  
Lin Wang ◽  
Ruijuan Zheng ◽  
Xuhui Zhao ◽  
Muhua Liu
Keyword(s):  
Queue Length ◽  
Optimization Problem ◽  
Time Slot ◽  
Smart Homes ◽  
Back Pressure ◽  
Smart Devices ◽  
Smart Device ◽  
Simulation Results ◽  
Computational Offloading ◽  
The Stability

In smart homes, the computational offloading technology of edge cloud computing (ECC) can effectively deal with the large amount of computation generated by smart devices. In this paper, we propose a computational offloading strategy for minimizing delay based on the back-pressure algorithm (BMDCO) to get the offloading decision and the number of tasks that can be offloaded. Specifically, we first construct a system with multiple local smart device task queues and multiple edge processor task queues. Then, we formulate an offloading strategy to minimize the queue length of tasks in each time slot by minimizing the Lyapunov drift optimization problem, so as to realize the stability of queues and improve the offloading performance. In addition, we give a theoretical analysis on the stability of the BMDCO algorithm by deducing the upper bound of all queues in this system. The simulation results show the stability of the proposed algorithm, and demonstrate that the BMDCO algorithm is superior to other alternatives. Compared with other algorithms, this algorithm can effectively reduce the computation delay.

A Small World Routing Protocol in Wireless Sensor Networks

2011 ◽  
Vol 474-476 ◽  
pp. 828-833
Author(s):  
Wen Jun Xu ◽  
Li Juan Sun ◽  
Jian Guo ◽  
Ru Chuan Wang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Routing Protocol ◽  
Data Transmission ◽  
Small World ◽  
Wireless Sensor ◽  
Small World Network ◽  
Average Delay ◽  
Network Diameter ◽  
Simulation Results

In order to reduce the average path length of the wireless sensor networks (WSNs) and save the energy, in this paper, the concept of the small world is introduced into the routing designs of WSNs. So a new small world routing protocol (SWRP) is proposed. By adding a few short cut links, which are confined to a fraction of the network diameter, we construct a small world network. Then the protocol finds paths through recurrent propagations of weak and strong links. The simulation results indicate that SWRP reduces the energy consumption effectively and the average delay of the data transmission, which leads to prolong the lifetime of both the nodes and the network.

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