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We present a modified cellular automaton model to study the traffic flow on a signal controlled ring road with velocity guidance. The velocity guidance is such a strategy that when vehicles approach the traffic light, suggested velocities are provided for avoiding the vehicles' sharp brakes in front of red light. Simulation results show that this strategy may significantly reduce the vehicles' stopping rate and the effect size is dependent upon the traffic density, the detector position, the signal's cycle time and the obedience rate of vehicles to the guidance.

The traffic of vehicles from downtown to suburban areas is investigated numerically. We propose a cellular automaton to simulate the traffic of vehicles within a city regulated by traffic lights. Both traffic flow and travel time are presented and we discuss some strategies to optimize these quantities.

Based on the two-lane highway traffic model with a work zone presented previously, a new traffic model with a work zone under the control of traffic lights is proposed. The length of the waiting area for vehicles before traffic lights is recommended cautiously after numerical simulation. The relationship between the vehicles’ queuing time and the cycle of traffic lights is studied, and the cycle time of traffic lights is obtained also considering people’s endurance to the red light. It is found that the traffic lights are effective to ease the traffic congestion in the work zone when the density is medium, and help to eliminate the inducement of traffic accidents. On the other hand, the simulation results show that traffic lights are not needed in the work zone when the traffic density is small. It indicates that the traffic flow in the work zone area can be optimized by using appropriate traffic management when the traffic density varies.

A cellular automaton model with open boundary condition for a crossroad system controlled by a traffic light is presented. The traffic flow and speed of the first part of the road are quite different from those of the second part behind the crossing. The impact of turning probabilities and the cycle times of traffic light on the flow are investigated.

This paper proposed an algorithm for a deep learning network for identifying circular traffic lights (CTL-DNNet). The sample labeling process uses translation to increase the number of positive samples, and the similarity is calculated to reduce the number of negative samples, thereby reducing overfitting. We use a dataset of approximately 370000 samples, with approximately 20000 positive samples and approximately 350000 negative samples. The datasets are generated from images taken at the Beijing Garden Expo. To obtain a very robust method for the detection of traffic lights, we use different layers, different cost functions and different activation functions of the depth neural network for training and comparison. Our algorithm has evaluated autonomous vehicles in varying illumination and gets the result with high accuracy and robustness. The experimental results show that CTL-DNNet is effective at recognizing road traffic lights in the Beijing Garden Expo area.

A probabilistic cellular automata is used for investigating how a traffic light placed on a street intersection influences vehicle velocities. Numerical simulations reveal that above a critical density of cars, the traffic light improves the flow in both directions.

In this research, we employ the technology of computer vision to recognize traffic light. We manipulate the color information of traffic light under HSI color space. And then, adding motor tracking technology, we can trap the traffic light which is subjected to a region of interest (ROI) to the center area of monitor. Meanwhile, we assign different frequency (peach) according to red light or green light to inform the people what state the light is.

We develop the algorithm at PC with MATLAB. Then, we port the whole system to DSP platform TI TMS320 DM642 EVM. The result is that in the condition of non-complex environment, the system can distinguish the red light from green light, and also can output audio signal by means of speaker.

This paper presents hardware and software designs of an experiment rig for traffic light control system based on Freescale MC9S12D64 MCU. In the design process, time counting of lights is controlled by comparison of adjusted output pulse signal and interrupt subroutines with the help of the enhanced capture timer (ECT). The circuits of traffic lights linking to MCU and the circuits of five nixie tube display are designed. The control programs are performed and the main codes are analyzed. The efficiency of the system is verified using the designed experiment rig. The system can be able to simulate the control process of actual traffic light.

We propose a remote control using radio waves of new traffic lights traffic command mode, to solve the irrational problem of traffic congestion crossing when the vehicle from different directions. In the paper, we propose the traffic lights remote control system based on the use of DF data transceiver module, using PT2262 / PT2272 as codec chip for long distance control of traffic signals. Traffic police could change the traffic lights countdown time timely using their remote controls based on the changes in traffic flows at different times. This approach can replace the sign directing traffic, but more simple and convenient. It uses a micro control unit MCU, and the DF data transceiver module to transmit and receive instruction. This is a kind of radio wave communication in the 100m range farther than the infrared remote control, which could satisfy the actual needs. This system enables automatic operation and temporary intervention of traffic lights combined with stable and reliable. This system can drive lights and timing display circuit directly, it has a strong practical value.