Intelligent vehicle network system and smart city management based on genetic algorithms and image perception

Mechanical Systems and Signal Processing xxx (xxxx) xxx

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Mechanical Systems and Signal Processing journal homepage: www.elsevier.com/locate/ymssp

Intelligent vehicle network system and smart city management based on genetic algorithms and image perception Daming Li a,b, Lianbing Deng a,c, Zhiming Cai b,⇑ a

The Post-Doctoral Research Center of Zhuhai Da Hengqin Science and Technology Development Co., Ltd, Hengqin New Area, China Institute of Data Science, City University of Macau, China c Zhuhai Da Hengqin Science and Technology Development Co., Ltd, China b

a r t i c l e

i n f o

Article history: Received 1 October 2019 Received in revised form 8 December 2019 Accepted 5 January 2020 Available online xxxx Keywords: Smart city Sensor network Image perception Vehicle network

a b s t r a c t By using various Internet of Things technologies and communication technologies, smart cities can respond quickly and intelligently to various service requests of cities, thus realizing the intelligent operation and management of cities, strengthening the management of urban facilities and improving the quality of urban services. To improve the operational efficiency of smart car network systems and smart city management systems. In this paper, the authors analyse the intelligent vehicle network system and smart city management based on genetic algorithms and image perception. By using distributed and parallel computing, massive urban data can be quickly stored, processed and analyzed, useful information can be extracted, which can help smart cities make effective decisions and improve the efficiency of infrastructure and resources use. The simulation results show that the proposed coordination strategy can achieve the minimum energy consumption scheduling, thus maximizing the benefits of the data center, thus effectively improving the urban road traffic capacity and alleviating urban traffic congestion. Ó 2020 Elsevier Ltd. All rights reserved.

1. Introduction In the past, the traditional urban traffic control system had a long operating cycle, and its traditional computer technology was low. It has been difficult to adapt to the needs and development of traffic management in the process of urbanization. With the continuous improvement of the level of urbanization, the urban intelligent traffic control system can meet the development needs of the urban transportation industry to the greatest extent. In its system design scheme, the video image vehicle monitor is mainly used to automatically identify and classify the license plates of urban vehicles, and it can collect urban traffic information in an all-round and multi-angle field. Smart cities could sense, analyze, and integrate key data of the city’s core systems, by using the various Internet of Things (IoT) and emerging communication technologies (such as 5G technology) Service requests make quick and intelligent responses [1], including traffic management [2], environmental protection [3], health management [4], and urban hydrological evaluation etc. [5]. The goal of building a smart city is to use advanced information and communication technologies to realize intelligent operation and management of the city, strengthen urban facility management, improve the quality of urban services, meet the various requirements of the citizens, and promote the intelligent and sustainable growth of the city [6]. The economic growth and large-scale urbanization of smart cities promote the emergence and development of various ⇑ Corresponding author. E-mail address: [email protected] (Z. Cai). https://doi.org/10.1016/j.ymssp.2020.106623 0888-3270/Ó 2020 Elsevier Ltd. All rights reserved.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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new technologies. In the course of the operation of smart cities, a huge variety of urban data has been produced. These urban data have unprecedented capacity granularity, speed and complexity, which challenge the processing of data in smart cities [7]. At the same time, the accumulation of massive urban data can be used to support and promote effective decision-making management in various smart city systems [8]. Therefore, smart cities have become big data driven. Using a variety of Big Data technologies, such as distributed and parallel computing, artificial intelligence can quickly store, process and analyze massive amounts of urban data, extract useful information, and help smart cities make effective decisions. Improve the efficiency of the use of infrastructure and resources [9]. The traffic system is a complex system with randomness, ambiguity and uncertainty. For this purpose, fuzzy intelligent control can be used to control traffic lights [10]. First, the use of inductive sensors to detect the vehicle’s flow to get the vehicle pulse, and then this pulse input to the PLC. Taking into account the vehicle pulse frequency, the use of PLC high-speed counter pulse rising edge of the vehicle count, and according to certain fuzzy intelligent control principle automatically adjusts the traffic light length [11]. In the design, we can use sequential function diagrams and ladder diagrams to design the program. Urban intelligent traffic control system is the most important task in the field of transportation in China, occupies an unparalleled position [12]. It is necessary to make full use of information technology and network communication technologies and intelligent technologies to continuously promote the stable development of urban intelligent traffic control systems. This article mainly carries out in-depth research and analysis of the research and design of urban intelligent traffic control system, and puts forward several targeted optimization measures for the reference of relevant persons [13]. At present, the accelerating process of urbanization has, to a certain extent, increased the pressure on urban transportation and seriously affected the urban infrastructure. Based on this situation, it is urgent to further establish and improve the urban intelligent traffic control system. The construction of urban intelligent traffic control system has a long way to go [14]. It is necessary to base itself on the status quo of the development of urban transport in China, seek truth from facts, give full play to the advantages of intelligence and information, and make it fully applicable to urban traffic construction. And it is necessary to constantly adjust the urban intelligent traffic control system, and to improve and perfect it, in order to achieve the construction goals of the urban intelligent traffic control system. Big data and smart cities drive each other’s development. Sensor networks and mobile group intelligence in smart city construction can collect a large number of various types of urban data and become a source of big data. At the same time, big data is providing favorable decision support for all areas of smart cities, and is the core of smart cities. Big data spreads across all aspects of smart cities, to the management and operation of cities, all with the support of big data. Big data has become the intelligent engine of smart cities. Urban data collection is critical in smart city projects that use big data. Any smart city project that uses big data needs to collect, store, process, and analyze large amounts of data generated by multiple data sources, transforming the data into useful information for the decision-making process. Among them, the collection of urban data is the first and most important step. Urban data covers a wide range of areas and involves a wide variety of data. Therefore, a combination of various sensing technologies and communication technologies is required to complete the collection of urban data. Intelligent control does not require many traffic management personnel, saving a lot of manpower and energy. With the help of intelligent control system for corresponding design and design, the intelligent traffic control target can be achieved scientifically and efficiently [15]. To further alleviate the phenomenon of traffic congestion during peak hours, timely clearing of vehicles and personnel, and avoiding traffic accidents caused by traffic jams and congestion problems. Therefore, through intelligent control and management, the safety of traffic is greatly guaranteed. Urban traffic roads are mainly designed to facilitate the travel of travelers and vehicles [16]. They must strictly abide by a fixed opening rule. Intelligent traffic control systems are built on the basis of traffic rules. Mainly by means of intersection lights, special road warning lights and intelligent control signs, the vehicle and the traveler are promptly reminded. In addition, it also helps reduce the level of environmental pollution and reduce energy consumption. Urban intelligent traffic control system, based on reducing the number of vehicle stagnation, greatly reduces tailpipe emissions and energy consumption, and is in line with the sustainable development of ecological and environmental goals [17]. To realize the intelligent management of vehicle network system and smart city, In this paper, the authors analyse the intelligent vehicle network system and smart city management based on genetic algorithms and image perception. By using distributed and parallel computing, massive urban data can be quickly stored, processed and analysed, useful information can be extracted, which can help smart cities make effective decisions and improve the efficiency of infrastructure and resources use.

2. Relevant theory analysis 2.1. Data acquisition in smart cities In smart city projects using big data, urban data collection is the first and most important step. Urban data has the characteristics of many kinds, wide range and large quantity, and requires stable and efficient communication conditions to transmit the collected data back to the data center [18]. Therefore, a single acquisition method cannot support such a large and complex urban data acquisition. Fig. 1 shows a combination of various acquisition methods to collect urban data, including wireless sensor networks, and mobile swarm intelligence perception. Wireless sensor networks collect all kinds of urban Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 1. Data Acquisition in Smart City.

data in fixed areas through distributed fixed wireless sensor nodes. Mobile Group Intelligence Sensing (MGUI) encourages mobile users to use their smart mobile terminals to collect urban data around them [19]. Through the combination of static wireless sensor network and dynamic mobile group intelligence perception and other acquisition methods, the data of the whole city can be collected, and the effective data source can be provided for the intelligent city based on large data. As shown in Fig. 1. With the development of electronic technology and wireless communication technology, a variety of low-cost, lowpower, multi-functional wireless sensor nodes have emerged. These wireless sensor nodes are often small in size and support high-quality short-distance communication. Wireless sensor nodes generally include sensing, computing and communication modules. Sensing module supports wireless sensor nodes to perceive various complex information of the surrounding environment, such as temperature, humidity, location, light intensity, pressure, and even multimedia data such as images and videos [20]. Computing module supports wireless sensor nodes to organize data and make decisions on perception and communication behavior. Communication module supports short-distance communication in wireless sensor networks. WSN is a distributed network. Its network settings are relatively free and can vary according to the location of wireless sensor nodes. Self-organization and multi-hop are the main components of wireless sensor networks. Self-organization means that nodes in wireless sensor networks can change their communication connections to form different network topologies [21]. Multi-hop means that wireless sensor nodes can achieve long-distance data transmission through shortdistance communication between multiple nodes. Self-organization and multi-hop make wireless sensor networks adaptable, and can form reliable communication networks in various situations. Through wireless sensor networks, wireless sensor nodes can cooperate with each other to complete data acquisition in the region. However, the working time of wireless sensor networks is an important issue in practical applications. Because wireless sensor nodes need batteries to provide energy to maintain their data acquisition work, and the energy of batteries is limited. In the case of large number of wireless sensor nodes, it is difficult to supplement battery energy by wired way [22]. Therefore, once the battery energy is exhausted, the performance of wireless sensor networks will be greatly affected. How to prolong the working time has become a big challenge for the large-scale application of wireless sensor networks in urban data acquisition. In order to prolong the life cycle of wireless sensor networks, energy acquisition technology has been widely studied. 2.2. Neural network The multi-layer feedforward network with hidden layer can greatly improve the classification ability of the network, but the problem of weight adjustment has not been effectively solved for a long time. As shown in Fig. 2. BP network is one of the most widely used neural network models, and it is a typical multi-layer feedforward network. We assume that the BP neural network has n layers, the first layer is the input layer, and the last layer is the output layer. The intermediate layer has a single layer or multiple layers. Moreover, because they have no direct relationship with the outside world, they are also called hidden layers. The input layer acts on the left and right sides of the buffer memory, and the data source is added to the network. Therefore, the input-output relationship of the neurons of the input layer is generally a linear function. In contrast, the input-output relationship of individual neurons in the hidden layer is genPlease cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 2. Neural Network.

erally a nonlinear function. Although the hidden layer is not connected to the outside world, their state can affect the relationship between input and output. As shown in Fig. 3. Assume that the input layer has n number of neurons and the output layer has pn number of neurons. When the BP neural
T network input data X ¼ x1 ; x2 ;    xp1 passes from the input layer to each hidden layer node, the output data
n n  n T can be obtained. Therefore, the BP neural network can be thought of as a nonlinear mapping from Y ¼ y1 ; y2 ;    ypn input to output [23]. At the same time, it is also a supervised Learning. Next, the standard BP algorithm is derived.xi represents the neural network input, yi represents the neural network output; di represents the network expected output; wijk represents the connection weight of the k-th neuron from the i-th layer to the j-th neuron in the i þ 1-th layer. Moreover, the single neuron output of the i-layer of the neural network is denoted by oij , the neuron threshold is denoted by oij , netij denotes the total output of the j-th neuron of the i-th layer, and N i denotes the number of nodes of the i-th layer of neurons. (1) Calculating the forward propagation of BP networks

netij ¼

Ni1 X

ð1Þ

Oði1Þk K ði1Þkj

k¼1

  Oij ¼ f s netij ¼

1
  1 þ exp  netij  hij

ð2Þ

(2) Derivation of error back propagation algorithm [24]. First, the error is defined:

ej ¼ dj  yj

ð3Þ

The weight is corrected by the error between the actual output of the network and the desired output. Objective function E:

Fig. 3. Neural Network with an implicit layer.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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E¼

2 1 X dj  yj 2 J

5

ð4Þ

The weight of the network is corrected. Among them, the learning efficiency is represented by g, which is between 0 and 1.

DW ijk ¼ g

@E @W ijk

ð5Þ

The recursive relationship between DW ijk and neuron output:

4W ijk ¼ g

@netðiþ1Þk @netðiþ1Þk @E @E ¼ g  ¼ gdik  @W ijk @netðiþ1Þk @W ijk @W ijk

Among them, dik ¼  @net@E

ðiþ1Þk

@netðiþ1Þk @net ¼ @W ijk @W ijk

Ni X

oih W ihk

! ¼ Oij

ð7Þ

h¼1

4W ijk ¼ gdik Oij dik ¼ 

ð6Þ

ð8Þ

@Oðiþ1Þk @E @Oðiþ1Þk @netðiþ1Þk

ð9Þ

 @Oðiþ1Þk 0 ¼ f netðiþ1Þk @netðiþ1Þk eðxhÞ

0

f ð xÞ ¼

½1 þ eðxhÞ 

2

¼ f ðxÞ½1  f ðxÞ

     @Oðiþ1Þk 0 ¼ f netðiþ1Þk ¼ f netðiþ1Þk ð1  f Þ ¼ oðiþ1Þk 1  oðiþ1Þk @netðiþ1Þk

ð10Þ

@E @Oðiþ1Þk The output node is oðiþ1Þk .

@E ¼ yk  dk @Oðiþ1Þk dik ¼ 

ð11Þ

  @E @E ¼ ¼ ðdk  yk Þoðiþ1Þk 1  oðiþ1Þk @netðiþ1Þk @Oðiþ1Þk

ð12Þ

Hidden layer node is oðiþ1Þk N iþ2 N iþ2 X X @netðiþ2Þh @E @E ¼  ¼ dðiþ1Þh wðiþ1Þkh @Oðiþ1Þk h¼1 @netðiþ2Þh @Oðiþ1Þk h¼1

netij ¼ 

Ni1 X

ð13Þ

ð14Þ

oði1Þk wði1Þkj

k¼1

When the hidden layer node is oðiþ1Þk , it can only know that the total error has a certain correlation with the hidden layer output, but the correct output result cannot be known in advance. The output of the hidden layer node affects the output of the next hidden layer [25]. N iþ2  X dik ¼ Oðiþ1Þk 1  Oðiþ1Þk dðiþ1Þh wðiþ1Þkh

ð15Þ

h¼1

The weight formula of the BP algorithm is adjusted to:

gðdk  yk Þyk ð1  yk Þoij 4W ijk ¼ h

iþ2   NP goðiþ1Þk 1  oðiþ1Þk  dðiþ1Þh wðiþ1Þkh oij

ð16Þ

h¼1

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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i-th layer neuron is

dik ¼ h

oðiþ1Þk



ðdk  yk Þyk ð1  yk Þ iþ2  NP 1  oðiþ1Þk  dðiþ1Þh wðiþ1Þkh

ð17Þ

h¼1

2.3. CNn With the continuous development in recent years, CNN has been rooted in many fields, such as image fusion, image retrieval, face recognition, object detection, speech recognition, natural language processing, recommendation system, video recognition, biomedicine, medical EEG and so on. In the field of machine learning, convolution network is a deep artificial feedforward network. CNN uses a multi-layer perceptual variant designed to minimize processing, or is also called displacement invariant or space invariant artificial neural network. Compared with general neural networks, CNN is composed of multiple feature extractors, including volume base, sampling layer, etc. Compared with other image processing algorithms, CNN requires fewer features. CNN is quite similar to general neural networks. It obtains the output results by learning some calculations of parameters (weights and bias constants) and neurons [26]. The difference is that the default input of CNN is the whole image, so it does not need to consider the feature problem artificially. In this way, more information can be put into the network, which makes the feedforward propagation more efficient, and also greatly reduces the amount of parameters. Weight sharing strategy is implemented in convolutional networks, i.e. each region uses the same filter to reduce memory consumption and improve performance [27]. Convolutional neural network consists of convolution layer and pooling layer alternately. Among them, the convolution layer is responsible for extracting input features, and the pooling layer is responsible for integrating features. These two parts can be superimposed or fully connected network can be introduced after the network to form a deep convolution neural network. In this model, the input and output of the middle layer are two-dimensional matrices, while the final output layer is usually a vector. The input data is convoluted by several learnable convolution kernels to generate several two-dimensional feature maps, and then the output with smaller dimensions is generated by down sampling. After repeated convolution and down sampling operations, the input data is finally abstracted into multiple feature maps with lower dimensions. In the final output layer, several low-dimensional two-dimensional feature maps are expanded into one-dimensional vectors, which are spliced into a longer vector as the final output. As shown in Fig. 4, Structurally, the convolution core of the convolution layer connects the front and back layers of the network and converts the input data into multiple feature maps. In a layer network, neurons connect to a small part of the upper network through the convolution core, which is its local connection characteristic. The convolution core can slide on

Fig. 4. Local sensing field.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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the data, convolute in two dimensions, and extract the feature map. Therefore, all the values on a feature map correspond to the same convolution core, which is the property of shared parameters. The number of model parameters is greatly reduced and the trainability of the model is greatly improved, especially when the input of the model is an image signal [28]. 2.4. Multiple convolution kernel Convolution kernel is also called convolution matrix or spatial filter. In image processing, there are many application scenarios to convolute image with convolution core. It can be used to blur, sharpen and contour image. In the field of machine learning and image classification, convolution kernel is used to extract image features [29]. In the process of convolution, different contents in the image are activated differently under the function of convolution kernel, so this difference can be highlighted in the feature map. As a network parameter, convolution kernel can be trained by back propagation algorithm and labelled training data, and can be generated by other methods according to the characteristics of data itself. In addition, some fixed content convolution kernels or randomly generated convolution kernels can also be used for feature extraction. The visualization of convolution kernels is also instructive for model training [30]. The convolution core of a well-trained network is a smooth filter without noise points, while the convolution core with noise points indicates that the network is not fully trained and needs to increase the number of iterations or adjust the parameters of the regularization term to reduce the degree of over-fitting of the model. As shown in Fig. 5. The Fig. 6 shows convolution operations on four channels, with two convolutional kernels, generating two channels. It is important to note that each channel in these four channels corresponds to a convolution kernel [31]. k

hij ¼ tanhððW k  xÞij þ bk Þ

ð18Þ

3. Urban traffic control system 3.1. Sampling methods From the technical analysis, off-line point control mainly adopts timing signal timing technology. On-line point control is based on the actual distribution of each entrance traffic at the intersection, and the green time of each phase is reasonably set

(a) locally connected neural net

(b) convolutional net Fig. 5. Multiple convolution kernel.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 6. Convolution operation.

to meet the traffic demand. The traffic network in the urban network has a significant traffic load, and good trunk traffic has a great guarantee for the traffic conditions in the city. In the urban traffic network, if the distance between the two intersections is relatively short, it is difficult to clear the two adjacent intersections within a limited time [32]. At the same time, when a single intersection is set up for single-point signal control, vehicles will often encounter red lights, which will cause traffic jams and environmental pollution to some extent. Therefore, under the condition that the main line vehicles are relatively unobstructed, the control design of the adjacent intersections can adopt a coordinated control method, thereby effectively reducing the number of parking vehicles at each intersection. The object of control of regional traffic signals is the traffic signal of all intersections in the entire area [33]. At present, the continuous improvement and improvement of computer technology and vehicle detection technology, related personnel must combine the traffic signals of all intersections in a specific area and perform comprehensive coordinated control and processing [34]. This will reduce the loss of vehicles at an intersection. Based on this coordinated control method, the traffic signal can pass the traffic data to the host computer in time through the communication network. The host computer can continuously adjust the timing plan being executed according to the changing trend of the traffic volume of the road network. The control mode can divide urban road traffic control into four types: timing control, induction control, adaptive control, and intelligent control. As shown in Fig. 7.

3.2. The significance of urban intelligent traffic control system Intelligent control is a type of intelligent transportation system. A series of high and new technologies such as advanced information technology, detection and sensing technologies, and artificial intelligence are mainly applied to the transportation system to establish a scientific, systematic, and comprehensive transportation and comprehensive management system. The intelligent transportation system is an intelligent system integrating people, vehicles, roads, and the environment. The intelligent traffic system is in line with the concept of safe, efficient and environmentally friendly green traffic and is the

Fig. 7. FDM spectrum.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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development direction of intelligent traffic. The design of single agent and single intersection point control in urban intelligent traffic control system is not much different. However, single agent refers to the specific intelligent control of traffic at some intersections through simple intelligent control methods in urban transportation [35]. At the same time, the single agent is mainly used for traffic control of a single number of intersections. The concept of the single agent is to use a single control to pass through the vehicle. In addition, the disadvantage of the single agent is that when the number of intersections increases, it will cause great interference. One of the important manifestations of artificial intelligence at present is the multi-intelligence system. It is technically used to continuously decompose complex and cumbersome targets to form concrete individuals. Each individual controls and manages its goals and then uses multi-intelligent control to connect them together to form a unified organic whole. Its advantage lies in its ability to achieve comprehensive control of traffic at a large number of intersections, as well as data collection, comparison and analysis. The multi-intelligent control system can also be divided into two major categories: road segment agents and regional agents. As shown in Fig. 8. With the function of real-time updating of traffic in a single road segment, it can also provide traffic flow data for connected ports, and continuously realize the exchange of information collected on different road segments. In actual control design, multi-agent is also called regional intelligence system. With the function of collecting traffic information and signal control data, it can also independently formulate corresponding control strategies according to the traffic conditions, and then upload the traffic demand and control results to the decision-making layer of the upper layer. It plays an important role between the road segment agent and the central management agent. Its advantage lies in the fact that it can effectively control the running status of the entire traffic network and make scientific and reasonable assessments based on factors such as road network structure and traffic detection. Effectively solve the related problems in the non-linear teaching model, complete functions, especially in pattern recognition and data analysis play an incomparable role and advantage. The application of artificial neural network in road traffic control system mainly refers to the traffic flow prediction, blocking recognition, and the route selection system’s impact on the driver’s line selection model. It is widely used in the identification of congestion. The output of the two TSVR sub-equalizers is [36]: T

z1;i ðkÞ ¼ v 1;i Fi yk

ð19Þ

T

z2;i ðkÞ ¼ v 2;i Fi yk Defining the gradient descent indication of the cost function:

u1;i ðkÞ ¼ xi ðkÞ  z1;i ðkÞ

ð20Þ

u2;i ðkÞ ¼ z2;i ðkÞ  xi ðkÞ

Substituting the constraints in the quadratic programming problem into the objective function, introducing the loss function, constructs two cost functions: 

LP ðv 1;i Þ ¼

M c1 1 X Le ðu1;i ðkÞÞ k u1;i  Ie1 k2 þ M k¼1 2

ð21Þ

Fig. 8. OFDM spectrum.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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LP ðv 2;i Þ ¼

M c2 1 X Le ðu2;i ðkÞÞ k Ie2  u2;i k2 þ M k¼1 2

ð22Þ

Use the loss function to eliminate errors in data-insensitive areas:

( Le ðu1;i ðkÞÞ ¼



0 ; u1;i ðkÞ < e1 2 u1;i ðkÞ  e1 ; u1;i ðkÞ P e1

ð23Þ



; u2;i ðkÞ < e2 2 e2  u2;i ðkÞ ; u2;i ðkÞ P e2

ð24Þ

( Le ðu2;i ðkÞÞ ¼

0

Consider the numerical stability of the IRWLS algorithm:

( Le ðu1;i ðkÞÞ ¼



; u1;i ðkÞ < e1 2 u1;i ðkÞ  e1 ; u1;i ðkÞ P e1

ð25Þ



0 ; u2;i ðkÞ < e2  e2  u2;i ðkÞ 2 ; u2;i ðkÞ P e2

ð26Þ

( Le ðu2;i ðkÞÞ ¼

0

An intelligent transportation system must be provided to achieve maximum patency of urban roads:

ai ðkÞ ¼

1 M  uj1;i ðkÞ

 dLe ðu1;i Þ   du 1;i

j

u1;i ðkÞ

¼

8 > <0 > :

; uj1;i ðkÞ < e1 2ðuj1;i ðkÞe1 Þ j

Mu1;i ðkÞ

; uj1;i ðkÞ P e1

ð27Þ

It is developing in the direction of environmental protection and green:

8  > <0 dLe ðu2;i Þ  bi ðkÞ ¼ ¼  du2;i uj ðkÞ > M  uj2;i ðkÞ : 2;i 1

; uj2;i ðkÞ < e2 j 2ðu2;i ðkÞe2 Þ j Mu2;i ðkÞ

; uj2;i ðkÞ P e2

ð28Þ

1) Urban intelligent traffic control system design scheme: the main use of bionic principles, rapid search in the solution space, is widely used in large-scale combination of optimization problems. When solving the model problem of realtime traffic control system, it is possible to search globally and determine the public cycle by means of genetic algorithm. It is also possible to use genetic algorithms to solve the problems of the control schemes of various intersections in the panel system, so as to avoid the problems existing in the combination process of traffic signal schemes. Urban intelligent traffic control system hardware mainly consists of system host, communication data processor and signal and other elements. The system host uses a minicomputer or a microcomputer server. Its specific configuration is mainly based on the city’s development needs to optimize the formulation. The communication processing device is mainly based on an industrial control platform. Different interfaces of the system are required to meet the standards of different network transmission protocols in order to further ensure the compatibility of the system. 2) Urban intelligent traffic control software system design: the research and design of urban intelligent traffic control system is imperative, and it is an important guarantee for promoting China’s economic and social development and raising the level of urbanization. Urban intelligent control system is a relatively comprehensive management system with a relatively wide range of application requirements. Through the extensive application of artificial intelligence technology, it can effectively alleviate the current status of urban transport in China. This will effectively promote the coordinated development of the various traffic management departments in the city. At the same time, it is necessary to inject certain innovative factors into the urban intelligent traffic control system, increase the research and development of the system design, and use the combination of Internet to build a more systematic and comprehensive intelligent traffic control and management system. Only in this way can we further ease the pressure of urban transportation, improve the level of urban road traffic control in China, and realize the strategic requirements for the sustainable development of intelligent traffic control systems. 4. Traffic network coordination 4.1. Research tools and methods In the coordination and control related to the regional transportation network, the relevant traffic flow data is mainly uploaded to the host computer through traffic signals. Then the upper computer shall adjust the timing plan according to Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 9. PT-CMA and PT-MMA blind equalization algorithm curve.

the actual situation of the relevant road network traffic and refer to the relevant time step. In addition, the use of the upper computer can also control multiple intersections in the city road at the same time, so that multiple intersections in the area can be coordinated, thereby improving the operating efficiency of the road network. Timing control based on historical traffic flow data, can find out the main rules of traffic flow generation and change, and use manual methods and computer simulation to determine the timing plan in different time segments. This control method is open-loop control and cannot properly adjust the control scheme according to the traffic conditions. It can be seen that this control method does not require high traffic signals. Therefore, there is no need for real-time monitoring of traffic flow, which has become one of the more commonly used control methods in urban road traffic systems in China. Adaptive control can effectively collect traffic information of related roads, predict future traffic demand according to related models, and select the best traffic control program from the system’s signal database or adjust the optimal control plan in time to achieve automatic traffic control. Intelligent transportation system is the main way to solve traffic problems. As shown in Fig. 9. 4.2. Relevant design of urban intelligent traffic control system The urban intelligent traffic control system belongs to the frontier discipline in the current artificial intelligence, and can be applied to the urban traffic network control. As can be seen in the design diagram of this system, it is designed according to an intelligent system. The urban road intelligence system not only needs the ability to update the traffic information of a single road section in time, but also provides relevant traffic flow data for the connected signal ports. Refer to the traffic flow information in the upper-level area to control the flow of traffic and convert the intersections and other road sections to each other. Generate real-time signal control models and provide relevant road signals. The signals can be paired with each other to maintain a relatively balanced and coordinated state of traffic between the road sections. Intelligent transportation systems are systems that involve a wide range of areas and incorporate many high-tech technologies. Judging from the current development situation, the overall operational level of the intelligent transportation system should be improved and efforts should be made to achieve the coordinated development of related industries. Only in this way can we effectively improve the level of urban road traffic control in China. With the expansion of the city scale and the continuous development of new urbanization, many cities in China are facing transportation demand and traffic management pressure. How to take scientific and reasonable measures to relieve urban traffic pressure and do a good job in traffic operation management is a very important task.. As shown in Fig. 10.

Fig. 10. BER plots for different SNRs.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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D. Li et al. / Mechanical Systems and Signal Processing xxx (xxxx) xxx Table 1 Design flow chart of trunk signal coordination control method. Coding mode

Encoding rate

0 1 2 3 4 5 6 7 8

6.60kbps 8.85kbps 12.65kbps 14.25kbps 15.85kbps 18.25kbps 19.85kbps 23.05kbps 23.85kbps

Table 2 The relationship between FEC bits and G.729 bits number per frame. First Second

0

15

80

0 15 80

CL1 = CL2 = 0 CL1 = 1, CL2 = 0 CL1 = 0, CL2 = 1

CL1 = 1, CL2 = 0 CL1 = CL2 = 1 CL1 = 0, CL2 = 1

CL1 = 0, CL2 = 1 CL1 = 0, CL2 = 1 CL1 = 0, CL2 = 1

5. Empirical analysis 5.1. Single agent research and design Image city sharing platform can provide all kinds of surveillance videos or images, and these surveillance videos or images are acquired from the urban intelligent traffic control system. The single agent’s simple design of the urban intelligent traffic control system is similar to single intersection point control. The single-agent traffic relies on the payroll system to refer to the intelligent control of traffic at one or a smaller number of intersections through simple intelligent control methods in urban traffic, and achieve the desired setting effect through control. The intelligent traffic control system of the city will involve many aspects in the design, all need careful consideration in the design of the urban intelligent traffic control system. In this way, an intelligent control system suitable for the city and the urban area can be designed to play a role in alleviating urban traffic pressure and promoting urban development. Intelligent transportation system is the front research topic in China’s transportation field. The core of the study is to address the growing traffic demand and the pressure of transportation resources. Using information technology, communication technology and computer technology, starting from the classification of urban traffic systems, the feasibility of urban traffic control systems is analyzed, and the research and design of urban intelligent traffic control systems are proposed. Pointed out the system control theory of intelligent transportation system, intelligent processing of traffic information, application and performance improvement (Table 1). According to the current traffic situation in China and the problems faced, it explains the important role of traffic control in the economic development and urbanization process, and proposes the important purpose and practical significance of the development of the intelligent transportation system. The intelligent transportation system is a research field that involves a wide range of fields and integrates various high and new technologies. The following focuses on the classification of intelligent traffic systems and the design of multi-agent-based urban traffic control systems (Table 2). 5.2. The status quo of domestic urban traffic and the problems it faces The object of regional traffic signal control is the traffic signal of all intersections in a city or a certain area. Under this control mode, the traffic signal transmits the traffic data to the host computer in real time through the communication network. The host computer continuously adjusts the timing plan being executed according to the real-time change of the traffic volume of the road network at a certain time step. The upper computer controls multiple intersections in a city area at the same time, realizing unified and coordinated management of the intersections in the area and improving the operating efficiency of the road network. The principle of induction control is to adjust the length and time sequence of the corresponding green light time according to the traffic flow data measured by the vehicle detector so as to adapt to the random change of the traffic flow. This approach has more flexibility than timing control (Table 3). 5.3. The purpose and significance of urban traffic control The urban traffic control system is mainly used for urban road traffic control and management, and plays an important role in improving urban road traffic capacity and alleviating urban traffic congestion. This article is a brief explanation and Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Table 3 Urban Intelligent Traffic Control Structure. C

e

N

Number

Return

10 10 20 5 10 10 10 10 10 10 10 10 10 10

0.01 0.005 0.005 0.005 0.01 0.01 0.001 0.01 0.01 0.01 0.01 0.01 0.01 0.005

20 20 20 20 20 20 20 20 25 25 25 25 25 25

12 16 15 16 17 13 20 10 14 20 13 11 21 24

60% 80% 75% 80% 85% 65% 100% 50% 56% 80% 52% 44% 84% 96%

analysis of the issues related to the intelligent traffic control system based on GPRS network. The smooth control of urban roads adopts effective control measures. Maximizing the efficiency of road use is an important part of urban road traffic control. Urban road traffic control is mainly the control of traffic signals. The traffic lights at the intersections are the main control facilities in the urban road network. The original purpose of installing signal lights is to ensure that the traffic flow in different directions or pedestrians can safely pass through the intersection. As the demand for transportation continues to increase, it has long been recognized that as long as traffic lights exist, they will more or less affect the operational efficiency of the transportation network. Therefore, the semaphore must exist with an optimal control strategy to reduce the time for all vehicles in the road network (Table 4). 6. The actual running effect of the simulation system Since the road network construction system provides the basic road data for the microscopic traffic simulation system, and the vehicle behavior simulation model provides the basis for simulating the state update and state change of the vehicle in the simulation system, the following is performed in the microscopic traffic simulation system. Actual inspection. First, a sample road network is constructed by the road network construction system based on Google Map, and the result is shown in Fig. 11. The road network consists of 169 roads, 395 road sections, 565 nodes and 108 intersections. The simulated road network generated by reading this file into the simulation program for analysis is shown in Fig. 12. It can be seen from Fig. 12 that the road network built on Google Map reflects the actual road conditions constructed and is well reproduced in the simulation program. Fig. 13 shows the effect of the road network after adding scene elements. Fig. 14 is a representation of a road network constructed using SHP files in a simulation scenario. The traffic simulation is carried out in this road network below, and the simulation effect after joining the vehicle is shown in Fig. 15. The simulation of vehicle behavior is mainly a simulation of vehicle following behavior and lane change behavior. Fig. 15 shows the simulation effect of the vehicle’s following behavior. The traditional vehicle lane change model is mainly for the update of the vehicle position in the simulation step in the two-dimensional scene. In the continuous simulation under the three-dimensional scene, the lane change action needs to be performed. Smooth processing to form a continuous smooth lane change trajectory. The lane change situation of the vehicle in the simulation system is shown in Fig. 16. The lane change vehicle is marked with a red frame. It can be seen from the figure that the following vehicle has also undergone the same lane change. Through the above actual test of the simulation system, it can be seen that the road network structure constructed by the road network construction method can be reproduced in the simulation and provide data support for the micro traffic Table 4 The number of texts corresponds to the classification result statistics. Number of texts

Recall

Precision rate

Average assessment

50 100 150 200 250 300 350 400 450 ... n

68.572% 70.196% 71.584% 72.779% 73.058% 73.953% 73.058% 73.953% 74.890% ... ...

75.491% 77.027% 78.681% 79.091% 80.384% 81.297% 80.384% 81.297% 83.534% ... ...

72.032% 73.612% 75.133% 75.935% 76.721% 77.625% 78.721% 79.625% 80.893% ... ...

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 11. Example of road network rendering.

Fig. 12. Road network renderings parsed on the simulation program.

Fig. 13. Road network rendering after adding the scene model.

Fig. 14. Road network constructed by SHP file data.

Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Fig. 15. Vehicle driving in a simulated scene.

Fig. 16. 车辆在仿真场景中的换道过程.

simulation. The statistics obtained from the actual test are also consistent with the expectations of the test results based on the simulation model. The operation of the vehicle in the simulation process meets the design requirements, but it is also found that due to the large amount of traffic simulation calculation, the poor performance of the machine is not conducive to the simulation implementation, and there are many factors to be considered in the vehicle behavior simulation process, surrounding vehicles and road networks. It will affect it, which requires further improvement of the simulation model. 7. Conclusion The traffic signal control machine is an intelligent traffic signal based on a real-time embedded operating system. It adopts the latest technology of computer control such as intelligent control, ASOS operating system, etc. In combination with the specific traffic conditions in China, it has realized powerful traffic management and signal control functions. The intelligent operation platform communication service manager is one of the core parts of the system intelligent traffic operation platform and bears the communication task between the on-site traffic equipment and monitoring software, configuration software, database service manager, and data processing software in the urban traffic system. This system is an informationbased and intelligent new type of traffic system formed by the transformation of traditional transportation systems using advanced control technology, information technology, communication technology, and system engineering technology. This kind of system can provide no longer measurement-by-specification but measurement-on-demand through smart sensor web, and services are transferred from being data-driven to service-driven. It can greatly improve traffic conditions without increasing road infrastructure, reduce travel time. Starting from the increasing traffic demand in China, an intelligent traffic control system was proposed. The development status of foreign intelligent traffic control systems is introduced. In order to meet the development needs of China’s urban traffic and road construction, the development model of China’s road traffic control system is proposed. This paper discusses the importance of intelligent traffic control, then studies two intelligent traffic control systems in detail, elaborates the research and design of multi-intelligence control system, and provides reference for the study of traffic control design. Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623

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Please cite this article as: D. Li, L. Deng and Z. Cai, Intelligent vehicle network system and smart city management based on genetic algorithms and image perception, Mechanical Systems and Signal Processing, https://doi.org/10.1016/j.ymssp.2020.106623