A signal control scheme implementation optimization method applicable to multiple devices and its system and device

Abstract

The invention relates to a signal control scheme implementation optimization system applicable to multiple devices, comprising: a terminal detection device for detecting vehicle passing signals; a data processing module for analyzing signal control scheme data and headway time distribution and proportion data; The logical processing module for judging the headway shape model of the lane and marking the lane features; the optimization and testing module for the optimization and testing of the signal control scheme data; the control center that receives the optimized signal control scheme data and controls the signal control machine; the signal The control machine receives and executes the optimized signal control scheme data; the communication module used for data reception and transmission among various components. The invention can be applied to a variety of terminal detection equipment, the algorithm involves simple parameters, the calculation speed is fast, the signal control scheme is adjusted in time, the adjustment method is more scientific, and the advanced data mining algorithm is integrated to deeply excavate the distribution meaning of the headway time distance to guide the optimization of the signal control scheme , strong scientific and reliable optimization results.

Description

technical field [0001] The invention relates to the field of control technology, in particular to a method for implementing and optimizing a signal control scheme applicable to multiple devices, and a system and a device thereof. Background technique [0002] At present, most of the signal-controlled intersections in my country adopt the control method of segmented planning throughout the day. Based on historical data and according to the traffic flow characteristics of each period of the day, several fixed timing schemes are formulated and used in different periods. The disadvantages of this control method are as follows: [0003] 1. The signal control intersection management plan is a fixed plan based on historical data, which has poor adaptability and cannot respond to real-time traffic changes, resulting in low efficiency or ineffective signal control programs; [0004] 2. The adjustment of the signal control scheme needs to rely on manual experience and lacks scientifi...

AI Technical Summary

Problems solved by technology

[0003] 1. The signal control intersection management plan is a fixed plan based on historical data, which has poor adaptability and cannot respond to real-time traffic changes, resulting in low efficiency or ineffective signal control programs;

[0004] 2. The adjustment of the signal control scheme needs to rely on manual experience and lacks scientificity. Due to the lack of intelligent means, the level of intelligent transportation construction in many places is not high. The optimization of the signal control scheme depends on manual fine-tuning by the traffic management department, which not only requires a lot of manpower, but also lacks Scientific, less effective;

[0005] 3. There are restrictions on the selection of detection facilities, and the optimization of signal control schemes depends on the detection of traffic flow conditions. Most of the existing methods are limited to specific detection equipment, such as detection coils, video equipment, etc., and cannot be widely used in multiple applications. Due to the different levels of intelligent transportation construction and the difference in facility supply capacity in different regions, the application of these methods is very limited

At present, the common detection method is to use the signal control machine to work with the ground sense coil, and the ground sense coil is easy to be damaged due to frequent traffic flow, and the replacement needs to break the road and seal the road, which not only affects the traffic flow but also affects the appearance of the road, and the reliability is not good;

[0006] 4. The algorithm involves complex indicators and is difficult to understand. Most of the current algorithms have limited reliability and insufficient grasp of the essence of traffic flow. A large number of reference indicators are introduced to correct each other, and the formed algorithm is very complicated, and sometimes there are even contradictions between indicators. , leading to a decrease in the reliability of the algorithm;

[0007] 5. Insufficient consideration of data mining-related algorithms. In recent years, with the continuous development of big data analysis and mining technology, the original traffic data processing method is facing expansion. This advantage has not been fully utilized in signal control optimization. Existing methods Simple statistical analysis methods and traffic engineering algorithms are still mostly used, and the efficiency and analysis dimensions need to be improved;

[0008] 6. Insufficient collaboration between artificial intelligence and intelligence. At present, urban traffic control is increasingly dependent on intelligent methods. However, intelligent equipment or algorithms may cause error problems due to data acquisition equipment failures or insufficient learning depth, which need to be compared with actual data. Checking and artificially assisted training models, but most current methods ignore this premise, relying entirely on immature intelligent algorithms prematurely, and lacking a verification process

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