# Designing Data Visualization Using Artificial Intelligence for Urban Intelligent Transportation Scenarios

Authors: Xuan Yang; Zhiyan Pu; Lei Zhang
Journal: Mobile Information Systems
 (2022)

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## Abstract

Artificial intelligence refers to a simulation extension and expansion technology that has found its presence in various applications and research domains. Artificial intelligence allows the related theories to portray similar to human intelligence. It combines developments in several fields, including computer science, statistics, and linguistics. The research objects of data visualization can be divided into three aspects: data, visualization technology, and visualization performance. This paper aims to study the data visualization design of artificial intelligence in urban intelligent transportation scenarios. It predicts the traffic flow via the traffic control method using neural network. It provides an effective way for timely dredging and alleviating problems, such as traffic jams and establishing alternative routes. This paper firstly analyzes the methods of artificial intelligence, traffic control methods, and data visualization and then designs the visualization based on artificial intelligence in traffic scenarios. By comparing the accuracy analysis of different models, the experimental results show that the error of the peak traffic flow is less than 10% and the lowest error value is 2.2%.


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## Body

## 1. Introduction

With the rapid development of science and technology, the process of urbanization is accelerating, and the number of vehicles in cities is also increasing. This greatly increases the traffic pressure in the city. In many cities around the world, there are traffic congestion and imperfect transportation facilities. Different difficulties caused by traffic have become an urgent problem. Only by presenting data to users in a vivid, intuitive, easy-to-understand, and friendly way can we achieve fast, accurate, and ruthless dissemination and provide users with effective and valuable information. With the gradual maturity of artificial intelligence technology, its application in the field of transportation is also deepening. It plays a pivotal role in improving urban traffic and can make full use of the city’s existing road facilities. Intelligent transportation will better serve people’s daily travel, traffic road management, and information services, as well as other services.Smart transportation is the best way to solve urban traffic problems. An intelligent transportation system refers to an integrated real-time traffic and management system. It makes full use of the Internet, artificial intelligence, and other technologies to give full play to its full range of effects. The purpose of intelligent transportation is that people, vehicles, and roads complement each other and create a synergistic effect. While improving the transportation environment, it also promotes a qualitative improvement in transportation efficiency. At the same time, it also ensures road traffic safety and reduces the occurrence of traffic accidents. Visualization technology visualizes complex data to the user through graphs and images and provides users with rich interactive methods to deeply analyze the hidden information in the data. On this basis, it efficiently analyzes and uses a large amount of traffic data to realize the visualization of big data. This makes it play its own value, helping people complete travel decisions and solving many transportation problems. As long as rescue measures can be taken in time for the problems caused by artificial intelligence technology so as to seek advantages and avoid disadvantages in the process of artificial intelligence technology development, this will not only help the smooth development of artificial intelligence technology, but also promote the continuous development of human society with progress.The innovations of this paper are as follows: (1) Starting from the collection of traffic data, according to the forecast model of traffic flow, the error rate of traffic flow forecast is analyzed to improve the accuracy of traffic flow forecast during peak hours. It provides an effective method to divert traffic in a timely manner and alleviate the problem of time traffic congestion. (2) This paper proposes a short-term traffic prediction model suitable for urban intelligent traffic control, including a traffic prediction model based on neural network, and conducts data visualization analysis on traffic in Chengdu.


## 2. Related Work

With the continuous development of computer technology, various data visualization bases also emerge in an endless stream. An interactive visualization of ESCAPE trial data was developed and evaluated by Brigdan et al., who iteratively designed an interactive visualization using the Bokeh software library for Python. The design was evaluated through a user study that quantitatively evaluated its efficiency and accuracy compared to the traditional modified Rankin Scale graphic, as well as qualitative feedback. However, his visualization is not novel enough [1]. Goldman et al.’s expanding public Xena data center currently hosts more than 1,400 datasets from over 35 cancer types, as well as pan-cancer datasets. Their public data centers provide the scientific community with important cancer genomics and functional genomics datasets, and they support most data types. Visualization and analysis included dynamic Kaplan–Meier survival analysis to assess survival stratification for any information other than their visualization spreadsheets, scatter plots, and bar graphs. They seek feedback on new visualizations and features on the poster. However, the accuracy of their data needs to be improved [2]. Bhowmick et al. discuss web data visualization operators such as Web Nest, Web Unnest, Web Coalesce, Web Expand, Web Pack, Web Unpack, and Web Sort in the context of a web repository system called Whoweda to provide users with the flexibility to a meaningful view of the web documentation set. However, they lack the analysis of big data [3]. Haara et al. introduced a multiobjective forest planning decision-making problem framework and corresponding data features. They use the framework and example planning data to illustrate and evaluate the potential of 14 interactive data visualization techniques to support multiobjective forest planning decisions. In addition, the possibility of using these technologies more widely to integrate the provision of ecosystem services into forest management and planning is discussed. However, they lack a comparison with other techniques [4]. Scarpato and Alessio describe a semiautomatic GUI generator (SAGG), which is able to display the considered link data in a better way by introducing a customized GUI; the key idea behind the method is to use existing web pages to infer the visual pattern of the link data. Its purpose is to provide the average user with a semiautomatic GUI generator capable of visualizing linked data without requiring the aforementioned user to understand the semantic techniques of data visualization. However, they did not propose a solution to the visualization problem in experiments [5]. Kirk et al. propose a framework that describes the range of different competency “ingredients” that form a recipe for data visualization expertise from the perspective of an experienced practitioner. Data visualization is difficult to master due to the inherent complexity of the challenge of promoting understanding. The ability to visualize data is considered an essential competency, so developing the necessary skills is essential to prepare students for future professional activity in the field; however, for educators, designing courses that cover all aspects is a challenge. However, they did not take into account the feasibility [6]. Kodaka et al. designed a demand acquisition technique by advancing User Story Mapping (USM) and validated it with a potential end user responsible for water resources management in the Bago River Basin in Myanmar through a workshop using a simulated system interface. Through research, it can be verified that a user story-based approach enables end users to decompose their operational activities into tasks. It also allows them to link the necessary data with visual images to facilitate their mission completion and water management decisions. However, they did not take into account the specific needs of users [7]. Based on the development of illustration, Huang and Qu explore the relationship between illustration, textual information, and media. They studied the feasibility of combining illustration and information visualization, expounded the background theory and significance of flow field design, and proposed a flow field generation method based on heat source diffusion. They analyze the shading of the flow field topology through the interactive input of the flow field design and then compare it with related work and propose a visualization method of layered flow field lines. Finally, the visualization method of stratified flow field is explained, and its effect is demonstrated. However, their accuracy for illustration information did not improve much [8].


## 3. AI-Based Intelligent Traffic Data Methods

### 3.1. Data Visualization Type

There are many ways to categorize data visualizations, but the most basic is based on the type of data to be visualized. According to the data type, data can be divided into low-dimensional data, high-dimensional data, temporal data, hierarchical data, and network relational data. For each type of data, data visualization can be divided into five categories: visualization of low-dimensional data, visualization of high-dimensional data, visualization of temporal data, visualization of hierarchical data, and visualization of network relational data.Low-dimensional data can be one-dimensional linear data, two-dimensional data with two attributes, and three-dimensional data with three-dimensional attributes. The implementation of this data visualization technique is usually simple and easy, but the information hidden behind the data cannot be fully revealed in the final view.High-dimensional data is often used to describe data with more than three features, each of which is roughly the same. High-dimensional data can be seen everywhere in our daily life. For example, a simple item contains various characteristics, such as price, name, model, color, and production date. High-dimensional imaging technology aims to use imaging to represent high-dimensional data, supplemented by interactive media, to help people analyze and understand high-dimensional data.Simply put, temporal data is data that is continuously produced as a time series changes. Time data is also closely related to people’s lives, and it is simpler and more intuitive for users to use imaging technology to obtain time data.Hierarchical data is essentially tree-structured data. Each node has a parent node (except the root node), and nodes are further divided into sibling nodes (same parent nodes) and child nodes (nodes that belong to the parent node). In data visualization technology, the use of tree diagram visualization technology can better represent the structural relationship of hierarchical data.Network relational data refers to data nodes that are directly or indirectly related to any data in the network. Because the nodes in the network data structure are not constrained by the nodes associated with them, this results in that the network relationship data does not have a stable hierarchical structure, and there may be multiple paths between nodes. Therefore, the relationship between nodes will also increase with the number of features. In data visualization technology, network graph data visualization based on mechanical model can better express the relationship between network data.


### 3.2. Traffic Control Method under Neural Network

In the urban road network, the upstream traffic flow determines the traffic flow at the next intersection, and there are many ways to categorize data visualizations, but the most basic is based on the type of data to be visualized. The current traffic flow at the intersection also directly affects the downstream traffic flow. Therefore, the upstream and downstream traffic flow is a key factor in traffic control [9]. However, the relationship between upstream and downstream traffic flow is also affected by many factors, such as road conditions, traffic signal control, driving speed, and so on. Based on the situation in real life, the real urban road network is much more complex. It is generally difficult to express correctly using only an exact functional relation. Considering the influence of realistic factors, the work in this paper uses neural networks to simulate this situation. It represents the relationship between traffic flow at each intersection on the road network under traffic control and induction. Considering the usefulness of the model, one must first make relevant basic assumptions about it.In order to successfully build this model, based on practical feasibility, this paper has the following basic assumptions:(1)
There will not be a situation where there is no traffic flow at the intersection for a long time. Generally speaking, the traffic flow obtained in the 5-minute statistical period is uninterrupted. There will not be a special situation where there is no traffic in multiple statistical periods.(2)
There are abundant databases that can provide models as samples. Given the nature of a neural network as a learning model, it needs to be trained using large batches of data in order to be able to obtain a set of parameters that best fits the situation.(3)
The road traffic network is simplified into a “well-shaped” network structure, and the flow of a specific entrance at each intersection is only affected by the flow of the three intersections around the downstream intersection. At the same time, it will also have some impact on the flow of the three surrounding intersections upstream [10].(4)
In a specific time interval, for each intersectioni, there is a measured flow of Vi. This measured V flow is used as input to the neural network model.According to the previous theoretical basis, we can refine the actual road network into the model shown in Figure1. Among them, Figure 2 is a simplified model of Figure 1.Figure 1
The decoy-control coordination model of the actual intersection.Figure 2
Simplified model of decoy-control synergy at an actual intersection.As shown in Figure3, for any traffic network point K, select any entrance direction according to the situation. Based on the previous assumptions, there are three intersections that affect the flow in the direction of the entrance. Assuming that they are K1, K2, and K3, respectively, the neural network model is shown in Figure 4.Figure 3
Traffic control model of an actual intersection.Figure 4
Neural network model of traffic control model.In the above neural network model, the three input nodes of the input layer are the traffic flow from intersectionK1, K2, and K3 to intersection K, which is the current actual detected traffic flow [11]. The input layer node is the current expected traffic flow at the K intersection. The input layer takes(1)X=vk1,vk2,vk3T,Y˜=vk.It initializes each element ofW with a computer number.Hidden layerZ:For a single neuronza, there are(2)zb=f∑a=13wabxa.In formula (2), f is used as the activation function, and the classical sigmoid function is adopted, as shown in the following formula:(3)fx=11+e−x.Output layerY:Output layer weight matrixH, then Y = f (Z’H).Backward propagation stage:The model is a neural network with a mentor and can use the BP algorithm. It modifies the weight table using the incremental rule and initializes the training factorα such that 0 < α < 1. The initialization requires precision Φ.It defines the error measure of themth sample in the neural network:(4)Em=Ym−Y˜m22.The total error for the entire sample set is(5)E=∑Em.Output layer weight matrixH correction:(6)hb=hb+αzbQ−YY˜−YYQ,where Q is the standard reference flow of the sample.The weight matrixW from the input layer to the hidden layer is modified:(7)wab=wab+Δwab,Δwab=αzb1−zbwabθxa.Among themθ=Q−YY˜−YY/Q, until E<φ, finally get the optimized neural network system.On the basis of the above, the traffic prediction results based on neural network are introduced. The model established above is improved, and the original input nodes are increased from 3 to 6. Three of the output nodes are the measured traffic at that time, and the three input nodes are the predicted traffic, as shown in Figure5.Figure 5
Neural network model of traffic control model.Compared with the model proposed above, after introducing traffic flow prediction, the input layer is increased from the original 3 input nodes to 6 input nodes. The hidden layer nodes and output layer nodes do not change [12].In fact, the traffic flow state of an intersection is not only affected by the current flow of the upstream and downstream intersections, but also by the future traffic flow of the upstream and downstream intersections. On the basis of the above assumptions, the traffic flow prediction results based on neural network are introduced to improve the model established above. The idea of improvement is to use the predicted flow elements of the upstream and downstream intersections as the input nodes of the neural network and increase the original input nodes from 3 to 6, of which 3 input nodes are the current measured flow, and 3 input nodes are predicted flow.(8)X=vk1,vk2,vk3,vk1∼,vk2∼,vk3∼T,\Y˜=vk∼.Consistent with the above, we still choose to initialize each element ofW with the computer number. For a single neuron za, the calculation formula will also change accordingly:(9)zb=f∑a=16wabxa.In formula (9), f is used as the activation function, and the classical sigmoid function is adopted, as shown in the following formula:(10)fx=11+e−x.No changes have been made to the output layer here. Therefore, the calculation formula of the weight of the output layerH does not have any defense, which is Y = f (Z’H).Backward propagation stage:The model is a mentored neural network. The BP algorithm can be used, the weight table can be modified using the incremental rule, and the training factorα can be initialized so that 0 < α < 1.It defines the error measure of themth sample in the neural network:(11)Em=Ym−Y˜m22.The total error for the entire sample set is(12)E=∑Em.Output layer weight matrixH correction:(13)hb=hb+αzbQ−YY˜−YYQ,where Q is the standard reference flow of the sample.The weight matrixW from the input layer to the hidden layer is modified:(14)wab=wab+Δwab,Δwab=αzb1−zbwabθxa.Among them,θ=Q−YY˜−YY/Q. Until E<φ, finally get the optimized neural network system.


### 3.3. Road Traffic Operation Index and Congestion Rate

The road traffic operation index, the road traffic congestion index, is abbreviated as TPI. This is a conceptual value that can comprehensively reflect the normal state or congestion of the traffic road network [13, 14]. There are five levels of TPI, which are unblocked, basically unblocked, lightly congested, moderately congested, and severely congested. The daily traffic performance indicators can be determined by the average of the traffic performance indicators during the peak hours of the day. The calculation method is as follows:(a)
Taking 15 minutes as the statistical period of a set of data, the average travel speed of each road section in the road network is obtained.(b)
According to the classification shown in the Table1, determine the traffic operation level of each road section.(c)
It proportionally weighted with Vehicle Kilometers Traveled (VKT) to obtain the ratio of the congestion kilometers of the road network. The calculation method of VKT ratio is as follows:Table 1
Data table of traffic operation level division of road sections.
Run levelSmoothBasically unblockedLight congestionModerate congestionSerious congestionHighwayV > 6550 <V ≤ 6535 <V ≤ 5020 <V ≤ 35V ≤ 20Main roadV > 4030 <V ≤ 4020 <V ≤ 3015 <V ≤ 20V ≤ 15Secondary road, branch roadV > 3525 <V ≤ 3515 <V ≤ 2510 <V ≤ 15V ≤ 10Note. V represents the average travel speed of the road section; the unit is km/h.Taking the expressway as an example:VKT for expressway segmentn:(15)VKTn=Vn×Ln,where VKTn is the VKT value of n sections in the statistical period (pcu·km), Vn is the amount of car traffic passing through n road sections during the statistical period (pcu), and Ln is the length of road segment n (unit: km).Expressway section VKT:(16)VKThighway=∑nMVKLn,N is the number of road segments of the expressway (unit: bar) and VKT is the value calculation of main road, secondary road and branch road, and so on, same as above, and so on.The traffic congestion rate is the sum of all road traffic indicators in a specific period of time as a ratio of road traffic performance indicators to moderate and severe congestion levels in an area. It generally reflects the degree of congestion over a period of time [15]. The higher the data is, the more serious the road congestion is.Calculation method:(17)TCR=∑cTPIc∑k=1NTPIk,where TCR is road traffic congestion rate (%) and TPI is the road traffic performance indicator for the cth statistical period in a given period, cTPIc≥6. TPIk is the traffic performance index of the kth statistical interval within a certain period, N is the length of the given period/the length of the statistical period, and the statistical period is usually controlled within 15 minutes.


### 3.4. Map Visualization Method of Urban Traffic Big Data

Data is the raw material and research topic of visualization and one of the threads in solving practical problems. To express information accurately and visually through data, it is necessary to study the type and semantics of the data in order to match the use of appropriate visual coding methods. Data is a record of objective events, containing descriptions of the nature, status, and interactions of objective events. It is usually expressed and communicated with recognizable symbols. Today, data has rich expressions and is no longer a narrow number. It can be text, graphics, and pictures with specific concepts or more symbols.Visualization technology converts data into graphs and displays them to the public, paying attention to the implementation of technology and optimization of algorithms and transforming abstract objects into concrete objects by developing visualization tools. The purpose is to make it easier for the public to understand the object and to give a visual impact to deepen the impression. It includes the field of computer simulation and computer graphics, and so on. As for known examples, there are visual simulation systems and virtual reality technology.For the visualization method of traffic big data, various data of vehicles can be used as the basic data source. For example, GPS data of vehicles, card swiping data of traffic, route data of traffic planning, and so on, can all be used for it. With the help of the swiping card data after using public transportation, it is possible to count the traffic data of people getting on and off at each stop. The traffic stops are graded in different colors to indicate the increase or decrease in the flow of people. For example, yellow represents the data of the number of people getting on the bus, and blue represents the data of the number of people getting off. It draws a circle at the stop position, and the size of the circle represents the sum of the number of people getting on and getting off, that is, the total flow of people. In addition, using the three-dimensional view can more vividly display the size of the traffic data at the location of its stop. On maps with arcs, use an image with transition effects to represent 3D columns. The specific location of the vehicle stop site is marked on the map, and the size of the flow of people is reflected by the height of the column. It uses the data of passengers swiping their cards after taking the traffic to count the traffic volume between public transportation stops. The higher the concentration of lines between the stops, the greater the flow of people, and vice versa, the smaller the flow of people [16, 17].


## 3.1. Data Visualization Type

There are many ways to categorize data visualizations, but the most basic is based on the type of data to be visualized. According to the data type, data can be divided into low-dimensional data, high-dimensional data, temporal data, hierarchical data, and network relational data. For each type of data, data visualization can be divided into five categories: visualization of low-dimensional data, visualization of high-dimensional data, visualization of temporal data, visualization of hierarchical data, and visualization of network relational data.Low-dimensional data can be one-dimensional linear data, two-dimensional data with two attributes, and three-dimensional data with three-dimensional attributes. The implementation of this data visualization technique is usually simple and easy, but the information hidden behind the data cannot be fully revealed in the final view.High-dimensional data is often used to describe data with more than three features, each of which is roughly the same. High-dimensional data can be seen everywhere in our daily life. For example, a simple item contains various characteristics, such as price, name, model, color, and production date. High-dimensional imaging technology aims to use imaging to represent high-dimensional data, supplemented by interactive media, to help people analyze and understand high-dimensional data.Simply put, temporal data is data that is continuously produced as a time series changes. Time data is also closely related to people’s lives, and it is simpler and more intuitive for users to use imaging technology to obtain time data.Hierarchical data is essentially tree-structured data. Each node has a parent node (except the root node), and nodes are further divided into sibling nodes (same parent nodes) and child nodes (nodes that belong to the parent node). In data visualization technology, the use of tree diagram visualization technology can better represent the structural relationship of hierarchical data.Network relational data refers to data nodes that are directly or indirectly related to any data in the network. Because the nodes in the network data structure are not constrained by the nodes associated with them, this results in that the network relationship data does not have a stable hierarchical structure, and there may be multiple paths between nodes. Therefore, the relationship between nodes will also increase with the number of features. In data visualization technology, network graph data visualization based on mechanical model can better express the relationship between network data.


## 3.2. Traffic Control Method under Neural Network

In the urban road network, the upstream traffic flow determines the traffic flow at the next intersection, and there are many ways to categorize data visualizations, but the most basic is based on the type of data to be visualized. The current traffic flow at the intersection also directly affects the downstream traffic flow. Therefore, the upstream and downstream traffic flow is a key factor in traffic control [9]. However, the relationship between upstream and downstream traffic flow is also affected by many factors, such as road conditions, traffic signal control, driving speed, and so on. Based on the situation in real life, the real urban road network is much more complex. It is generally difficult to express correctly using only an exact functional relation. Considering the influence of realistic factors, the work in this paper uses neural networks to simulate this situation. It represents the relationship between traffic flow at each intersection on the road network under traffic control and induction. Considering the usefulness of the model, one must first make relevant basic assumptions about it.In order to successfully build this model, based on practical feasibility, this paper has the following basic assumptions:(1)
There will not be a situation where there is no traffic flow at the intersection for a long time. Generally speaking, the traffic flow obtained in the 5-minute statistical period is uninterrupted. There will not be a special situation where there is no traffic in multiple statistical periods.(2)
There are abundant databases that can provide models as samples. Given the nature of a neural network as a learning model, it needs to be trained using large batches of data in order to be able to obtain a set of parameters that best fits the situation.(3)
The road traffic network is simplified into a “well-shaped” network structure, and the flow of a specific entrance at each intersection is only affected by the flow of the three intersections around the downstream intersection. At the same time, it will also have some impact on the flow of the three surrounding intersections upstream [10].(4)
In a specific time interval, for each intersectioni, there is a measured flow of Vi. This measured V flow is used as input to the neural network model.According to the previous theoretical basis, we can refine the actual road network into the model shown in Figure1. Among them, Figure 2 is a simplified model of Figure 1.Figure 1
The decoy-control coordination model of the actual intersection.Figure 2
Simplified model of decoy-control synergy at an actual intersection.As shown in Figure3, for any traffic network point K, select any entrance direction according to the situation. Based on the previous assumptions, there are three intersections that affect the flow in the direction of the entrance. Assuming that they are K1, K2, and K3, respectively, the neural network model is shown in Figure 4.Figure 3
Traffic control model of an actual intersection.Figure 4
Neural network model of traffic control model.In the above neural network model, the three input nodes of the input layer are the traffic flow from intersectionK1, K2, and K3 to intersection K, which is the current actual detected traffic flow [11]. The input layer node is the current expected traffic flow at the K intersection. The input layer takes(1)X=vk1,vk2,vk3T,Y˜=vk.It initializes each element ofW with a computer number.Hidden layerZ:For a single neuronza, there are(2)zb=f∑a=13wabxa.In formula (2), f is used as the activation function, and the classical sigmoid function is adopted, as shown in the following formula:(3)fx=11+e−x.Output layerY:Output layer weight matrixH, then Y = f (Z’H).Backward propagation stage:The model is a neural network with a mentor and can use the BP algorithm. It modifies the weight table using the incremental rule and initializes the training factorα such that 0 < α < 1. The initialization requires precision Φ.It defines the error measure of themth sample in the neural network:(4)Em=Ym−Y˜m22.The total error for the entire sample set is(5)E=∑Em.Output layer weight matrixH correction:(6)hb=hb+αzbQ−YY˜−YYQ,where Q is the standard reference flow of the sample.The weight matrixW from the input layer to the hidden layer is modified:(7)wab=wab+Δwab,Δwab=αzb1−zbwabθxa.Among themθ=Q−YY˜−YY/Q, until E<φ, finally get the optimized neural network system.On the basis of the above, the traffic prediction results based on neural network are introduced. The model established above is improved, and the original input nodes are increased from 3 to 6. Three of the output nodes are the measured traffic at that time, and the three input nodes are the predicted traffic, as shown in Figure5.Figure 5
Neural network model of traffic control model.Compared with the model proposed above, after introducing traffic flow prediction, the input layer is increased from the original 3 input nodes to 6 input nodes. The hidden layer nodes and output layer nodes do not change [12].In fact, the traffic flow state of an intersection is not only affected by the current flow of the upstream and downstream intersections, but also by the future traffic flow of the upstream and downstream intersections. On the basis of the above assumptions, the traffic flow prediction results based on neural network are introduced to improve the model established above. The idea of improvement is to use the predicted flow elements of the upstream and downstream intersections as the input nodes of the neural network and increase the original input nodes from 3 to 6, of which 3 input nodes are the current measured flow, and 3 input nodes are predicted flow.(8)X=vk1,vk2,vk3,vk1∼,vk2∼,vk3∼T,\Y˜=vk∼.Consistent with the above, we still choose to initialize each element ofW with the computer number. For a single neuron za, the calculation formula will also change accordingly:(9)zb=f∑a=16wabxa.In formula (9), f is used as the activation function, and the classical sigmoid function is adopted, as shown in the following formula:(10)fx=11+e−x.No changes have been made to the output layer here. Therefore, the calculation formula of the weight of the output layerH does not have any defense, which is Y = f (Z’H).Backward propagation stage:The model is a mentored neural network. The BP algorithm can be used, the weight table can be modified using the incremental rule, and the training factorα can be initialized so that 0 < α < 1.It defines the error measure of themth sample in the neural network:(11)Em=Ym−Y˜m22.The total error for the entire sample set is(12)E=∑Em.Output layer weight matrixH correction:(13)hb=hb+αzbQ−YY˜−YYQ,where Q is the standard reference flow of the sample.The weight matrixW from the input layer to the hidden layer is modified:(14)wab=wab+Δwab,Δwab=αzb1−zbwabθxa.Among them,θ=Q−YY˜−YY/Q. Until E<φ, finally get the optimized neural network system.


## 3.3. Road Traffic Operation Index and Congestion Rate

The road traffic operation index, the road traffic congestion index, is abbreviated as TPI. This is a conceptual value that can comprehensively reflect the normal state or congestion of the traffic road network [13, 14]. There are five levels of TPI, which are unblocked, basically unblocked, lightly congested, moderately congested, and severely congested. The daily traffic performance indicators can be determined by the average of the traffic performance indicators during the peak hours of the day. The calculation method is as follows:(a)
Taking 15 minutes as the statistical period of a set of data, the average travel speed of each road section in the road network is obtained.(b)
According to the classification shown in the Table1, determine the traffic operation level of each road section.(c)
It proportionally weighted with Vehicle Kilometers Traveled (VKT) to obtain the ratio of the congestion kilometers of the road network. The calculation method of VKT ratio is as follows:Table 1
Data table of traffic operation level division of road sections.
Run levelSmoothBasically unblockedLight congestionModerate congestionSerious congestionHighwayV > 6550 <V ≤ 6535 <V ≤ 5020 <V ≤ 35V ≤ 20Main roadV > 4030 <V ≤ 4020 <V ≤ 3015 <V ≤ 20V ≤ 15Secondary road, branch roadV > 3525 <V ≤ 3515 <V ≤ 2510 <V ≤ 15V ≤ 10Note. V represents the average travel speed of the road section; the unit is km/h.Taking the expressway as an example:VKT for expressway segmentn:(15)VKTn=Vn×Ln,where VKTn is the VKT value of n sections in the statistical period (pcu·km), Vn is the amount of car traffic passing through n road sections during the statistical period (pcu), and Ln is the length of road segment n (unit: km).Expressway section VKT:(16)VKThighway=∑nMVKLn,N is the number of road segments of the expressway (unit: bar) and VKT is the value calculation of main road, secondary road and branch road, and so on, same as above, and so on.The traffic congestion rate is the sum of all road traffic indicators in a specific period of time as a ratio of road traffic performance indicators to moderate and severe congestion levels in an area. It generally reflects the degree of congestion over a period of time [15]. The higher the data is, the more serious the road congestion is.Calculation method:(17)TCR=∑cTPIc∑k=1NTPIk,where TCR is road traffic congestion rate (%) and TPI is the road traffic performance indicator for the cth statistical period in a given period, cTPIc≥6. TPIk is the traffic performance index of the kth statistical interval within a certain period, N is the length of the given period/the length of the statistical period, and the statistical period is usually controlled within 15 minutes.


## 3.4. Map Visualization Method of Urban Traffic Big Data

Data is the raw material and research topic of visualization and one of the threads in solving practical problems. To express information accurately and visually through data, it is necessary to study the type and semantics of the data in order to match the use of appropriate visual coding methods. Data is a record of objective events, containing descriptions of the nature, status, and interactions of objective events. It is usually expressed and communicated with recognizable symbols. Today, data has rich expressions and is no longer a narrow number. It can be text, graphics, and pictures with specific concepts or more symbols.Visualization technology converts data into graphs and displays them to the public, paying attention to the implementation of technology and optimization of algorithms and transforming abstract objects into concrete objects by developing visualization tools. The purpose is to make it easier for the public to understand the object and to give a visual impact to deepen the impression. It includes the field of computer simulation and computer graphics, and so on. As for known examples, there are visual simulation systems and virtual reality technology.For the visualization method of traffic big data, various data of vehicles can be used as the basic data source. For example, GPS data of vehicles, card swiping data of traffic, route data of traffic planning, and so on, can all be used for it. With the help of the swiping card data after using public transportation, it is possible to count the traffic data of people getting on and off at each stop. The traffic stops are graded in different colors to indicate the increase or decrease in the flow of people. For example, yellow represents the data of the number of people getting on the bus, and blue represents the data of the number of people getting off. It draws a circle at the stop position, and the size of the circle represents the sum of the number of people getting on and getting off, that is, the total flow of people. In addition, using the three-dimensional view can more vividly display the size of the traffic data at the location of its stop. On maps with arcs, use an image with transition effects to represent 3D columns. The specific location of the vehicle stop site is marked on the map, and the size of the flow of people is reflected by the height of the column. It uses the data of passengers swiping their cards after taking the traffic to count the traffic volume between public transportation stops. The higher the concentration of lines between the stops, the greater the flow of people, and vice versa, the smaller the flow of people [16, 17].


## 4. Visualization Experiment and Analysis of Sichuan Traffic Management

### 4.1. Basic Situation of the City

Since China implemented the policy of reform and opening up, the national economy has developed rapidly, the process of urbanization has been accelerated, the urban population has increased sharply, and the number of motor vehicles has greatly increased. At the “2003 International Conference on Sustainable Urbanization Strategies,” Qiu Baoxing pointed out that China has now entered a period of rapid urbanization. Chengdu is one of the largest cities with a population of 10 million in the west. Chengdu enjoys the reputation of “Land of Abundance” since ancient times due to its location in the western Sichuan Basin, with a vertical and horizontal river network and rich products. Therefore, it is of great significance to study the management of public transportation in Chengdu [18].Chengdu City is located in the central part of Sichuan Province, west of the Sichuan Basin, between 102″54–104053 east longitude and 30″05–31026 north latitude. The city is 192 kilometers long from east to west and 166 kilometers wide from north to south, with a total area of 12,390 square kilometers. It is adjacent to Deyang City in the northeast, Ziyang in the southeast, Meishan in the south, Ya’an in the southwest, and Aba Tibetan and Qiang Autonomous Prefecture in the northwest [19, 20].


### 4.2. Visual View of Chengdu Public Transportation Data

Data is the center of any information visualization. In order to create a better information visualization design project, we should know the original source of the received data resource, the data resource collection process and its purpose [21]. Finding exactly the data we need in a design is a fairly difficult task, so it is best to start with the data we already have.For the data of Chengdu’s public transportation, there are many websites that provide accessible data resources. Once sufficient raw data has been acquired, the next step begins with data reprocessing: data parsing and organization. After downloading data sets from various websites, you can try to process the data in a variety of ways, analyze, and obtain the internal connection between various data sets. This process is the process of reprocessing data resources. Through the analysis of the main public transportation in Chengdu, the following data are obtained, as shown in Figure6 and Table 2.Figure 6
Route map of Chengdu no. 4 bus station.Table 2
Daily passenger flow of Chengdu fourth road bus station.
EndStartChadianzi bus stationBattle of Yingbin avenueMetro Yingbin avenue stationTaurus parkBabao street fightSheep market interface battleChengdu east railway stationChadianzi bus station084047121622389328616104Battle of Yingbin Avenue414042412048234930815318Metro Yingbin avenue station1390110302931420133024137Taurus park118067314200231023341451Babao street fight238110382043196708905245Sheep market interface battle1983220275199789203179Chengdu east railway station510341033789203930413180In addition, through the statistics of the number of traffic electronic police monitoring points and the number of manual traffic police in various districts and counties in Chengdu, the two numbers are integrated to comprehensively consider the distribution of police forces in various districts and counties in Chengdu.Combining the above data with the geographic location data of districts and counties in Chengdu, the visualization design of urban public transportation information is carried out.


### 4.3. Visual View of Web-Based Information Release

Through the observation of the daily passenger flow data of Chengdu’s public transport hubs, it can be found that it has not only a quantitative relationship between each station but also a directional relationship. After visualization, as shown in Figure7, a line between two nodes indicates that there is a relationship between them, and the thickness of the line indicates the weight or degree of the relationship.Figure 7
Relationships and connections between nodes.
(a)(b)The relationship of the data in the table is represented by the connection method. A connection can represent a set of common relationships as shown in Figures a-b, a set of positional relationships as shown in Figures a-c, or a set of directed relationships as shown in Figures A-D. In this design, the passenger flow from one public transportation hub site to another is a pair of directed relationships, we use different colored lines to represent and distinguish, and the thickness of the line represents the amount of passenger flow. Thick, indicating that the passenger flow of the route is more, and the line is thinner, indicating that the passenger flow of the route is less [22].In order to display information from multiple angles, we use the same data to draw a force-directed graph. A force-directed graph is also a visual graph used to represent the connection relationship between various nodes. The circles of different colors represent each hub site, and the size of the circle indicates the size of the passenger flow. When the user selects several sites to display, the sites displayed by the chord diagram and the force-directed diagram are consistent, and the user can click the switch icon in the upper right corner to switch back and forth between the two graphics, which is beneficial for the user to perform multiangle mapping. Compare with reference. In addition, the function of saving pictures is also designed in the page for users to record. The drawing result of the force-directed graph is shown in Figure8:Figure 8
Drawing effect of force-directed graph.In the release of urban public transportation safety information, we found that the coverage and distribution data of traffic police in various districts and counties have geographic characteristics. Therefore, selecting the electronic map as the background, coupled with the visual data information, will give people a more intuitive spatial experience. The open API of Baidu Maps provides us with a good design background. Baidu Maps API is a set of application programming interfaces based on Baidu Maps service open to developers, which can be used directly as long as they are connected in the design. With the electronic map as the background, use the heat map to visualize the data. Heat maps can visually present some data that are not easy to understand or express, such as frequency, density, temperature, and so on, and use the combination of area and color to present this information. The heat map can show what happens in the unclickable area, and the detection method of the city heat map is only for reference. The reason why a heat map is called a “heat map” is that it uses the well-known flame color to convey a sense of temperature and conveys quantitative information that is difficult to understand by using the three basic models of shape, color, and temperature, which is very vivid. Intuitive. As shown in Figure9, in the heat map visualization of urban public transportation safety information release, high heat represents the abundant traffic police force and high degree of safety, and low heat represents the insufficiency of police force and low security degree.Figure 9
Heat map rendering effect.


### 4.4. Experiment of Traffic Flow Prediction Based on Neural Network

If accurate traffic flow prediction can be made before the arrival of peak traffic flow, and the traffic flow state of the road network can be predicted. It will reduce or prevent the occurrence of congestion, which is also the need for intelligent traffic management. Therefore, when the traffic flow prediction and the traffic control system are linked together, it is of great practical significance to release the congestion information in advance and to relieve the congested vehicles. Combined with the BP neural network algorithm, the traffic flow sequence segmentation algorithm is applied to predict the traffic flow. The following is a combination of specific examples to establish a prediction model.The basic idea of the BP algorithm is to obtain the absolute error between the two results (known categories of each sample) by continuously adjusting the data and calculating the value of the actual output result. If the expected results are not obtained, the weights need to be constantly adjusted to minimize the mean square error between the two results.It chooses a main road through the downtown area, with two intersections with high traffic flow and high traffic status. Using this area as the research object, the following research was carried out.First, a sequence segmentation algorithm is used, using the coil detector at the intersection. The southbound traffic flow data of the intersection is collected every 5 minutes, and the collection time is 24 hours. A total of 4231 samples are obtained, and the samples are divided, and the dividing line can be obtained:t1∗,t2∗,t3∗,t4∗,t5∗,t6∗,t7∗,t8∗.Among them,t1∗ = 7:30, t2∗ = 8:55, t3∗ = 10:45, t4∗ = 13:10, t5∗ = 17:00, t6∗ = 19:00, t7∗ = 21:00, and t8∗ = 24:00. These dividing lines divide the time period of the day into 8 and use the traffic flow data for a week on the roads that meet the above conditions. The survey method adopts the method of using buried coils, and the coils collect data every 5 minutes. The traffic flow of the four entry lanes at the intersection is used as the collected data, and the collection time is 24 hours; a total of 17979 sample data were obtained. As the input of the BP neural network, a BP neural network is established in each segment in turn. First, it computes the net input and output for each sample and then computes the backpropagated error. And carry out weight correction to judge whether the error converges to the required accuracy.There are 6 input variables and 1 output variable (traffic forecast for the next 5 minutes). The BP neural network adopts a three-layer neural network structure, that is, input layer, hidden layer, output layer, and the number of hidden layer units is 7. According to the basic algorithm of BP neural network, the predicted traffic volume of eight time periods from 00:00 to 24:00 in a day is obtained. The predicted flow and observed flow of the neural network are shown in Figure10, and the error rate of flow prediction is shown in Figure 10.Figure 10
Neural network predicts traffic versus observed traffic.The observed flow rate of the neural network in Figure10. Traffic flow is predicted by a combined model algorithm of BP neural network with sequence segmentation. It is not difficult to see from Figure 10 that there are two peaks in the distribution of traffic flow, one is the morning peak period and the other is the afternoon peak period. For a city’s major intersections, its traffic flow contains the richest information about traffic status. At the same time, the traffic signal timing system is ever-changing, making different adjustments according to different situations. The traffic information release system can also detect the traffic flow of road sections that may be congested in advance according to regulations. It releases road state information in a timely manner and replans a more complete route for the driver.The above prediction shows that the accuracy of the predicted flow is relatively low in the period of low traffic flow. The error values are relatively high, but the errors are reduced to less than 10% during peak traffic times. The above modes improve the accuracy of traffic flow forecasting during peak periods. Thus, it provides an effective way for timely dredging and alleviating problems, such as traffic jams.


## 4.1. Basic Situation of the City

Since China implemented the policy of reform and opening up, the national economy has developed rapidly, the process of urbanization has been accelerated, the urban population has increased sharply, and the number of motor vehicles has greatly increased. At the “2003 International Conference on Sustainable Urbanization Strategies,” Qiu Baoxing pointed out that China has now entered a period of rapid urbanization. Chengdu is one of the largest cities with a population of 10 million in the west. Chengdu enjoys the reputation of “Land of Abundance” since ancient times due to its location in the western Sichuan Basin, with a vertical and horizontal river network and rich products. Therefore, it is of great significance to study the management of public transportation in Chengdu [18].Chengdu City is located in the central part of Sichuan Province, west of the Sichuan Basin, between 102″54–104053 east longitude and 30″05–31026 north latitude. The city is 192 kilometers long from east to west and 166 kilometers wide from north to south, with a total area of 12,390 square kilometers. It is adjacent to Deyang City in the northeast, Ziyang in the southeast, Meishan in the south, Ya’an in the southwest, and Aba Tibetan and Qiang Autonomous Prefecture in the northwest [19, 20].


## 4.2. Visual View of Chengdu Public Transportation Data

Data is the center of any information visualization. In order to create a better information visualization design project, we should know the original source of the received data resource, the data resource collection process and its purpose [21]. Finding exactly the data we need in a design is a fairly difficult task, so it is best to start with the data we already have.For the data of Chengdu’s public transportation, there are many websites that provide accessible data resources. Once sufficient raw data has been acquired, the next step begins with data reprocessing: data parsing and organization. After downloading data sets from various websites, you can try to process the data in a variety of ways, analyze, and obtain the internal connection between various data sets. This process is the process of reprocessing data resources. Through the analysis of the main public transportation in Chengdu, the following data are obtained, as shown in Figure6 and Table 2.Figure 6
Route map of Chengdu no. 4 bus station.Table 2
Daily passenger flow of Chengdu fourth road bus station.
EndStartChadianzi bus stationBattle of Yingbin avenueMetro Yingbin avenue stationTaurus parkBabao street fightSheep market interface battleChengdu east railway stationChadianzi bus station084047121622389328616104Battle of Yingbin Avenue414042412048234930815318Metro Yingbin avenue station1390110302931420133024137Taurus park118067314200231023341451Babao street fight238110382043196708905245Sheep market interface battle1983220275199789203179Chengdu east railway station510341033789203930413180In addition, through the statistics of the number of traffic electronic police monitoring points and the number of manual traffic police in various districts and counties in Chengdu, the two numbers are integrated to comprehensively consider the distribution of police forces in various districts and counties in Chengdu.Combining the above data with the geographic location data of districts and counties in Chengdu, the visualization design of urban public transportation information is carried out.


## 4.3. Visual View of Web-Based Information Release

Through the observation of the daily passenger flow data of Chengdu’s public transport hubs, it can be found that it has not only a quantitative relationship between each station but also a directional relationship. After visualization, as shown in Figure7, a line between two nodes indicates that there is a relationship between them, and the thickness of the line indicates the weight or degree of the relationship.Figure 7
Relationships and connections between nodes.
(a)(b)The relationship of the data in the table is represented by the connection method. A connection can represent a set of common relationships as shown in Figures a-b, a set of positional relationships as shown in Figures a-c, or a set of directed relationships as shown in Figures A-D. In this design, the passenger flow from one public transportation hub site to another is a pair of directed relationships, we use different colored lines to represent and distinguish, and the thickness of the line represents the amount of passenger flow. Thick, indicating that the passenger flow of the route is more, and the line is thinner, indicating that the passenger flow of the route is less [22].In order to display information from multiple angles, we use the same data to draw a force-directed graph. A force-directed graph is also a visual graph used to represent the connection relationship between various nodes. The circles of different colors represent each hub site, and the size of the circle indicates the size of the passenger flow. When the user selects several sites to display, the sites displayed by the chord diagram and the force-directed diagram are consistent, and the user can click the switch icon in the upper right corner to switch back and forth between the two graphics, which is beneficial for the user to perform multiangle mapping. Compare with reference. In addition, the function of saving pictures is also designed in the page for users to record. The drawing result of the force-directed graph is shown in Figure8:Figure 8
Drawing effect of force-directed graph.In the release of urban public transportation safety information, we found that the coverage and distribution data of traffic police in various districts and counties have geographic characteristics. Therefore, selecting the electronic map as the background, coupled with the visual data information, will give people a more intuitive spatial experience. The open API of Baidu Maps provides us with a good design background. Baidu Maps API is a set of application programming interfaces based on Baidu Maps service open to developers, which can be used directly as long as they are connected in the design. With the electronic map as the background, use the heat map to visualize the data. Heat maps can visually present some data that are not easy to understand or express, such as frequency, density, temperature, and so on, and use the combination of area and color to present this information. The heat map can show what happens in the unclickable area, and the detection method of the city heat map is only for reference. The reason why a heat map is called a “heat map” is that it uses the well-known flame color to convey a sense of temperature and conveys quantitative information that is difficult to understand by using the three basic models of shape, color, and temperature, which is very vivid. Intuitive. As shown in Figure9, in the heat map visualization of urban public transportation safety information release, high heat represents the abundant traffic police force and high degree of safety, and low heat represents the insufficiency of police force and low security degree.Figure 9
Heat map rendering effect.


## 4.4. Experiment of Traffic Flow Prediction Based on Neural Network

If accurate traffic flow prediction can be made before the arrival of peak traffic flow, and the traffic flow state of the road network can be predicted. It will reduce or prevent the occurrence of congestion, which is also the need for intelligent traffic management. Therefore, when the traffic flow prediction and the traffic control system are linked together, it is of great practical significance to release the congestion information in advance and to relieve the congested vehicles. Combined with the BP neural network algorithm, the traffic flow sequence segmentation algorithm is applied to predict the traffic flow. The following is a combination of specific examples to establish a prediction model.The basic idea of the BP algorithm is to obtain the absolute error between the two results (known categories of each sample) by continuously adjusting the data and calculating the value of the actual output result. If the expected results are not obtained, the weights need to be constantly adjusted to minimize the mean square error between the two results.It chooses a main road through the downtown area, with two intersections with high traffic flow and high traffic status. Using this area as the research object, the following research was carried out.First, a sequence segmentation algorithm is used, using the coil detector at the intersection. The southbound traffic flow data of the intersection is collected every 5 minutes, and the collection time is 24 hours. A total of 4231 samples are obtained, and the samples are divided, and the dividing line can be obtained:t1∗,t2∗,t3∗,t4∗,t5∗,t6∗,t7∗,t8∗.Among them,t1∗ = 7:30, t2∗ = 8:55, t3∗ = 10:45, t4∗ = 13:10, t5∗ = 17:00, t6∗ = 19:00, t7∗ = 21:00, and t8∗ = 24:00. These dividing lines divide the time period of the day into 8 and use the traffic flow data for a week on the roads that meet the above conditions. The survey method adopts the method of using buried coils, and the coils collect data every 5 minutes. The traffic flow of the four entry lanes at the intersection is used as the collected data, and the collection time is 24 hours; a total of 17979 sample data were obtained. As the input of the BP neural network, a BP neural network is established in each segment in turn. First, it computes the net input and output for each sample and then computes the backpropagated error. And carry out weight correction to judge whether the error converges to the required accuracy.There are 6 input variables and 1 output variable (traffic forecast for the next 5 minutes). The BP neural network adopts a three-layer neural network structure, that is, input layer, hidden layer, output layer, and the number of hidden layer units is 7. According to the basic algorithm of BP neural network, the predicted traffic volume of eight time periods from 00:00 to 24:00 in a day is obtained. The predicted flow and observed flow of the neural network are shown in Figure10, and the error rate of flow prediction is shown in Figure 10.Figure 10
Neural network predicts traffic versus observed traffic.The observed flow rate of the neural network in Figure10. Traffic flow is predicted by a combined model algorithm of BP neural network with sequence segmentation. It is not difficult to see from Figure 10 that there are two peaks in the distribution of traffic flow, one is the morning peak period and the other is the afternoon peak period. For a city’s major intersections, its traffic flow contains the richest information about traffic status. At the same time, the traffic signal timing system is ever-changing, making different adjustments according to different situations. The traffic information release system can also detect the traffic flow of road sections that may be congested in advance according to regulations. It releases road state information in a timely manner and replans a more complete route for the driver.The above prediction shows that the accuracy of the predicted flow is relatively low in the period of low traffic flow. The error values are relatively high, but the errors are reduced to less than 10% during peak traffic times. The above modes improve the accuracy of traffic flow forecasting during peak periods. Thus, it provides an effective way for timely dredging and alleviating problems, such as traffic jams.


## 5. Discussion

Visual analysis technology provides an intuitive and effective method. In addition to this, analysis algorithms for automatic extraction of traffic information in a targeted manner can be activated when one finds some unexpected features in the images created by the visualization. Intelligent transportation continues to appear in daily life, and intelligent travel has become the general trend. With the rapid development of artificial intelligence technology and the continuous optimization of urban traffic management, the construction of smart cities will be more perfect. Their travel will be safer, smarter, and more comfortable [23].The article first studies the current situation and application background of artificial intelligence and intelligent transportation. It introduces the traffic control method under the neural network and calculates the road traffic operation index and congestion rate. Based on this, combined with the data characteristics of the traffic data studied in this subject, a suitable visualization method is proposed, and the visualization results of this paper are analyzed and discussed. Through the experimental design, it is finally concluded that the accuracy of traffic flow prediction during peak periods is improved [24, 25].This paper takes Chengdu public transportation information as an example to design a visual release system and summarizes three main points of its visual design: simplifying the data, changing the hidden information to the obvious, and changing the information release to the static one. Under the premise of grasping these three points, select the appropriate data and use the corresponding data processing method to process it, and then map the visual structure and draw the visual view according to the obtained data table.


## 6. Conclusions

With the progress of society and the development of intelligent transportation, people’s demand for safe transportation and intelligent transportation increases rapidly. Therefore, the visualization research work oriented to traffic data has also received more and more attention and favor from scholars and related departments. By developing the intelligent transportation system ITS, it is the only way to solve the problems of traffic congestion and traffic safety, improve the level of urban traffic management, and continuously meet the increasingly high travel requirements of urban people. In this context, with the massive generation of traffic data, the processing of these massive, high-dimensional data is a challenge. Visualization technology is a powerful means to solve such problems. Based on the existing visual analysis technology, this paper conducts a detailed analysis and in-depth research on the relevant visual models and corresponding algorithms. Artificial intelligence is one of the most important sciences for us at this stage. Whenever there is a breakthrough in the field of artificial intelligence, one needs to be vigilant and reflects on themself, whether it has invented a technology beyond human control. Therefore, sometimes, we can sacrifice a certain speed in the research of science and technology to improve the overall stability of society.



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*Source: 3967267-2022-08-27.xml*