Appl. Sci. 2023, 13, x. https://doi.org/10.3390/xxxxx www.mdpi.com/journal/applsci
Article 1
Multi-factor highway freight volume prediction based on BP 2
neural network 3
Yanshuang Zhang 1 ， Caixia Tian 2 , ， Baohua Guo 1 ， 3 ，Meixia Wang 1， Zhezhe Zhang 1 and Morobeni-Kgaugelo 1 4
1 School of Energy Science and Engineering, Henan Polytechnic University, Jiaozuo, Henan 454000, China; 5
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>; <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>; <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS>; <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 6
2 School of Resources and Environment, Henan Polytechnic University, Jiaozuo, Henan 454000, China; 7
<PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 8
3 Jiaozuo Engineering Research Center of Road Traffic and Transportation, Henan Polytechnic University, 9
Jiaozuo, Henan 454000, China; <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 10
Correspondence: <PRESIDIO_ANONYMIZED_EMAIL_ADDRESS> 11
12
Abstract: With the development of the times, the traditional single factor time series prediction can- 13
not meet the needs of actual prediction. It is necessary to comprehensively consider the influence of 14
various variables on the prediction results. Therefore, we use MATLAB to predict the highway 15
freight volume of multiple factors. According to the relevant data of highway freight volume in 16
Chinese history, the BP neural network prediction model of highway freight volume is established, 17
coded and calculated in the MATLAB software environment. Through repeated training of the data, 18
the predicted value is finally obtained. To prove the validity of the highway freight volume predic- 19
tion model based on BP neural network, the example analysis of China’s highway freight volume is 20
used and the original data is accurately fitted. The investment in the infrastructure construction is 21
also improved through the prediction of the freight volume to promote the development of trans- 22
portation industry and the progress of the social economy. 23
Keywords: BP neural network; highway freight volume; multi-factor prediction 24
25
1. Introduction 26
Since the reform and opening up, the rapid development of the national economy 27
has promoted the rapid development of various industries. For example, it has promoted 28
the rapid development of road freight transportation. With the rise of online shopping, it 29
has promoted the growth of China’s logistics industry to an extent that has led to the de- 30
velopment of China’s freight transportation. The increase in the investment share of social 31
fixed assets has promoted the development of trucks. In the ’ Twelfth Five-Year Plan ', the 32
state clearly proposed to vigorously develop road freight transportation, improve the 33
level of standardization and intensification, promote the development and improvement 34
of freight transportation methods, and expand the radiation range of less-than-truckload 35
and fast-operating business networks. With the completion of a well-off society in an all- 36
round way, China 's poverty alleviation has achieved great results. The economy has de- 37
veloped steadily, the income level of urban and rural residents has been continuously 38
improved and consumer demand has been increasing. In addition, China 's highway net- 39
work has been continuously improved. For instance, the structure of the highway has 40
been continuously rationalized, the connection between urban and rural areas has been 41
strengthened and the highway network has been gradually built and improved. The pro- 42
gress of informatization technology will promote the development of highway transpor- 43
tation, improve the quality of transportation, promote the transformation and upgrading 44
of transportation mode while promoting the development of transportation industry. 45
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2. Literature review 46
Zhang [1] established a PB neural network model and analyzed the influencing fac- 47
tors of freight volume in Xinjiang. Through the training and fitting of the model, historical 48
data were selected as the test samples of the network model, which proved that the pre- 49
diction of BP neural network was more accurate than other prediction methods. Taking 50
the national railway freight volume as the research object, Luo [3] uses the methods of 51
multiple linear regression and BP neural network to obtain a more suitable prediction 52
method for the national railway and conducts research on the prediction of national rail- 53
way freight volume based on multiple factors. Practice shows that the two prediction 54
methods have their own advantages and the combination of the two can improve the pre- 55
diction accuracy. Chen [8] analyzed the characteristics of Shandong’s freight volume while 56
using several prediction models to make quantitative predictions under the influence of 57
relevant factors and qualitatively corrected the prediction results to obtain the final com- 58
prehensive prediction results. Yang [7] established a PSO-SVM prediction model using a 59
particle swarm support vector machine model, and predicted the road freight volume un- 60
der the influence of industrial transfer in the Yangtze River transport corridor to verify 61
the effectiveness of the model. An [11] used the GM ( 1,1 ) model direct modeling method 62
to improve and analyze the unbiased grey Verhulst model and the traditional grey Ver- 63
hulst model. Through the simulation and prediction of Lanzhou highway freight volume, 64
the superiority of GM ( 1,1 ), traditional grey Verhulst and unbiased grey Verhulst model 65
is analyzed and the feasibility as well as the applicability of the improved model unbiased 66
grey Verhulst model were illustrated. On the basis of qualitative and quantitative analysis 67
of the influencing factors of railway transportation, Wu [23] selected some factors affect- 68
ing China 's freight volume to carry out BP neural network prediction research based on 69
multi-factor railway freight volume. Both prediction fitting and the algorithm conver- 70
gence are good. The complex nonlinear mapping between the influencing factors and the 71
railway freight volume is realized and the prediction results are excellent. 72
Whether it is for the study of highway freight volume, or for the study of railway 73
freight volume, we can get inspiration from it, so as to apply it to the study of highway 74
freight volume. The application of research methods does not distinguish between fields, 75
but focuses on reliability. Based on the research of the above scholars, we can find that 76
multiple regression and multi-factor prediction have a good effect on the prediction of 77
freight volume. In summary, based on the comprehensive consideration of the impact in- 78
dicators of freight volume, this paper selects relevant indicators and collects data. The 79
multi-factor prediction based on BP neural network is used to predict China 's freight vol- 80
ume and compare it with the actual China 's highway freight volume. The predicted re- 81
sults and reliability are obtained. 82
3. Neural network theory 83
3.1. MATLAB multivariate prediction theory 84
In this paper, we mainly use the neural network regression prediction. MATLAB is 85
a powerful tool for data analysis and numerical calculation. It can use a variety of func- 86
tions and toolboxes for multivariate prediction. Multivariate prediction generally predicts 87
the value of one or more output variables through a variety of input variables. The steps 88
of multivariate prediction are shown in Figure 1-1. In MATLAB, we can use a variety of 89
multivariate prediction algorithms to predict. For example, through the construction of 90
multiple decision trees, their results are averaged or voted by random forest regression 91
prediction ; to predict the relationship between independent variables and dependent var- 92
iables and establish multiple linear regression prediction of linear model ; the weighted 93
average is performed according to the nearest neighbor weight to find the K-nearest 94
neighbor regression prediction of the nearest sample point ; support vector regression 95
prediction using support vector machine idea by finding the optimal hyperplane ; the 96 Appl. Sci. 2023, 13, x FOR PEER REVIEW 3 of 12

principal component regression prediction of the principal component analysis of the in- 97
dependent variables and the principal component regression analysis of the principal 98
component ; the partial least squares regression prediction of the linear model is estab- 99
lished after the dimension reduction of the independent variable and the dependent var- 100
iable. The neural network model is used to establish the neural network regression pre- 101
diction of the nonlinear mapping relationship between input and output. 102
103
104
Figure 1. Multivariate flow chart. 105
First item; 106
Data preparation: Collect and organize input and output data. Ensure that the data 107
is clean and complete and perform the necessary preprocessing, such as removing outli- 108
ers, filling missing values, etc. 109
Second item; 110
Feature selection: According to the characteristics of the problem and data, select the 111
appropriate input variables as features. Correlation analysis, principal component analy- 112
sis and other methods can be used to assist selection. 113
Third item; 114
Data partitioning: The data set is divided into training set and test set. Usually, most 115
of the data is used to train the model, and the remaining part is used to evaluate the per- 116
formance of the model. 117
Fourth item; 118
Model training: Select the appropriate algorithm and use the training set to train the 119
model. In MATLAB, functions such as fitlm, svmtrain and trainlm can be used to train the 120
model. 121
Fifth item; 122
Model evaluation: Use the test set to evaluate the performance of the model. Various 123
indicators, such as mean square error and coefficient of determination, can be used to 124
evaluate the accuracy and generalization ability of the model. 125
Sixth item; 126
Model optimization: According to the evaluation results, the model is adjusted and 127
optimized. Different parameter settings and feature combinations can be tried to improve 128
the performance of the model. 129
Seventh item; 130
Prediction: Use the trained model to predict. Input new data and get the correspond- 131
ing output through the model. 132
3.2. BP neural network theory 133
3.2.1. Algorithm flow 134 Appl. Sci. 2023, 13, x FOR PEER REVIEW 4 of 12

The characteristic of BP neural network is to improve signal propagation according 135
to the reverse feedback of error. The topology of BP neural network model is generally 136
composed of three aspects: input layer, hidden layer and output layer, as shown in Figure 137
2.1 below. 138
139
140
Figure 2. 1 Topological structure of BP neural network model. 141
The output layers
1
, , , ,
j i
y y y denote information output, that is, the target re- 142
sult. 143
The hidden layers
1 2
, , , , ,
h q
b b b b denote data processing, you can set the number 144
of layers. 145
The input layers
1
, , , ,
j d
x x x denote information input, read data 146
w is the black path between b and y. 147
w represents the weight from hidden layer to input layer. 148
v is the black path between b and x. 149
v represents the weight of the input layer to the hidden layer. 150
3.2.2. Working process 151
In the working process, the weight and bias from the hidden layer to the output layer 152
are adjusted in turn, and the weight and bias from the input layer to the hidden layer are 153
adjusted in turn. The working process of the BP neural network is shown in Figure 2.2. 154
155 Appl. Sci. 2023, 13, x FOR PEER REVIEW 5 of 12

Figure 2. 2 Working process diagram of BP neural network. 156
3.2.3. Propagation process 157
First item; 158
Forward propagation 159
The information enters the network from the input layer and passes through each 160
layer in turn, and the process of obtaining the final output layer result is shown in Figure 161
2.3. 162
163
Figure 2. 3 Forward propagation graph. 164
The calculation process mainly involves matrix multiplication, using the value of 165
each layer multiplied by the corresponding weight + bias variable (activation function). 166
From the input layer to the hidden layer: 167
1
d
h ih i
i
v x
=
= 
(1) 168
The activation function through the hidden layer is: 169
( ) h h h
b f = −
 
（2） 170
Among them, the weight between the first neuron of the input layer and the first 171
neuron of the hidden layer is the threshold of the first neuron of the hidden layer. 172
From the hidden layer to the output layer: 173
1
q
j hj h j
h
w b
=
= + 
（3） 174
Among them, the weight between the first neuron in the hidden layer and the first 175
neuron in the output layer is the threshold of the first neuron in the output layer. 176
In the machine learning model, the parameters we start with are random, so the cal- 177
culation results may have a large error with the real values. In order to reduce this error, 178
we need to adjust the parameters according to the error, and this process requires the back 179
propagation algorithm of the model. 180
Second item; 181
Back propagation 182
The core idea of the back propagation algorithm is to calculate the error between the 183
output layer and the expected value, and then propagate the error back to each layer of 184
the network, and then adjust the parameters in the network. In this process, each neuron 185
adjusts its own weight and bias according to the error, so that the output of the network 186
is closer to the expected value. This process is an iterative process, which needs to be re- 187
peated until the error reaches the minimum or meets the preset stop condition. The error 188
is usually measured by calculating the square sum of the difference between the true value 189
and the neural network training result. Of course, other calculation methods can also be 190
selected. This error value will be used to guide the parameter adjustment in the back prop- 191
agation process, so that the network can better fit the real data. 192
4. Case analysis of highway freight volume forecasting based on neural network 193
4.1. Data source 194 Appl. Sci. 2023, 13, x FOR PEER REVIEW 6 of 12

According to the strong nonlinear mapping and other characteristics of the neural 195
network, only the input and output data of the object are given. Through the learning 196
function of the network itself, the characteristics of the mapping relationship between in- 197
put and output can be achieved. By using the strong nonlinear mapping and other char- 198
acteristics of the neural network, that is, through the input and output data of the given 199
object, the mapping relationship between input and output can be achieved through the 200
learning function of the network itself. Through the analysis of the relevant data over the 201
years and the review of the relevant literature. Finally, five indicators such as road diesel 202
consumption, population, GDP, road freight turnover, and road freight volume were se- 203
lected as influencing factors. The data were derived from the China Statistical Yearbook, 204
and the relevant data from 1997 to 2021 were mainly queried and summarized. The ex- 205
ample analysis proves that the freight volume prediction model based on BP neural net- 206
work is effective, which provides a new prediction idea and method for highway freight 207
volume prediction. The data over the years are shown in Table 1. 208
Table 1. Data source. 209
particular year
Diesel consump-
tion (million tons)
Population (ten
thousand people)
Gross domestic
product (billion
yuan)
Freight turnover
(billions of tons of
kilometers)
Freight volume (mil-
lion tons)
1997 1379.53 123626 78802.9 5271.5 976536
1998 1901.93 124761 83817.6 5483.4 976004
1999 2221.65 125768 89366.5 5724.3 990444
2000 2543.81 126743 99066.1 6129.4 1038813
2001 2754.31 127627 109276.2 6330.4 1056312
2002 2964.80 128453 120480.4 6782.5 1116324
2003 34485.2 129227 136576.3 7099.5 1159957
2004 4182.24 129988 161415.4 7840.9 1244990
2005 4964.78 130756 185998.9 8693.2 1341778
2006 5747.32 131448 219028.5 9754.2 1466347
2007 6794.36 132129 270704.0 11354.7 1639432
2008 7343.16 132802 321229.5 32868.2 1916759
2009 7891.96 133450 347934.9 37188.8 2127834
2010 8518.56 134091 410354.1 43389.7 2448052
2011 9485.2 134916 483392.8 51374.7 2820100
2012 10727.03 135922 537329.0 59534.9 3188475
2013 10920.53 136726 588141.2 55738.1 3076648
2014 11042.8 137646 644380.2 56846.9 3113334
2015 11162.8 138326 685571.2 57955.7 3150019
2016 11068.48 139232 742694.1 61080.1 3341259
2017 11253.69 140011 830945.7 66771.5 3686858
2018 11167 140541 915243.5 71249.2 3956871
2019 9867 141008 983751.2 59636.4 3435480
2020 9532 141212 1005451.3 60171.8 3426413
2021 9984 141260 1141230.8 69087.7 3913889
210
4.2. MATLAB running process 211
Based on the analysis of historical data and the influencing factors of highway freight 212
volume, BP neural network is used to predict the highway freight volume in China. The 213
specific operation process is as follows, and the neural network view is shown in Figure 214
3.1. 215
216 Appl. Sci. 2023, 13, x FOR PEER REVIEW 7 of 12

217
Figure 3. 1 Neural network view. 218
First item; 219
Emptying environment variables. Delete the defined variables and start building our 220
code again. 221
Second item; 222
Import data. The data of five indicators such as road diesel consumption, population, 223
gross domestic product, road freight turnover and road freight volume in China from 1997 224
to 2018 were selected as import data. A total of 22 sets of data were collected from the 225
national statistical yearbook. 226
Third item; 227
Divide the training set and test set. Partitioning training set and test set is an im- 228
portant task in machine learning and data analysis, which is used to evaluate the perfor- 229
mance and generalization ability of the model. Here we select the first 17 sets of data, that 230
is, 1997-2013 data as the divided training set, and select the last 5 sets of data, that is, 2014- 231
2018 data as the divided test set. 232
Fourth item; 233
Data normalization. The data is scaled to a certain range according to a certain pro- 234
portion to improve the accuracy and efficiency of subsequent data processing or analysis, 235
accelerate the speed of gradient descent to find the optimal solution, and improve the 236
training efficiency of the model. 237
Fifth item; 238
Create a network. In MATLAB, a new feedforward neural network is created, where 239
p _ train is the training input data, t _ train is the training target data, and 5 represents the 240
number of neurons in the hidden layer of the network. 241
Sixth item; 242
Set training parameters. The maximum number of iterations of training is set to 1000 243
times. The training algorithm will try to minimize the error by adjusting the weight and 244
deviation of the network, and perform up to 1000 iterations. If the set error threshold is 245
reached within this number of iterations or other stop conditions are met, the training may 246
end early. The training error threshold is set to 1e-6. The training algorithm will try to 247
make the output error of the network less than or equal to this threshold. When the thresh- 248
old is reached or lower than this threshold, the training will stop, even if the maximum 249
number of iterations has not been reached. The learning rate is set to 0.01, which deter- 250
mines the magnitude of network weight and bias update in each iteration. High learning 251
rate may lead to fast or unstable training, and low learning rate may lead to more stable 252
training. 253
Seventh item; 254
Training network. The train function is called to train the neural network net, using 255
the input data p _ train and the target data t _ train. 256
Eighth item; 257 Appl. Sci. 2023, 13, x FOR PEER REVIEW 8 of 12

Simulation test. The trained neural network net is used to simulate (predict) the train- 258
ing data p _ train and the test data p _ test, and the simulated outputs t _ sim1 and t _ sim2 259
are obtained respectively. 260
Ninth item; 261
Data denormalization. The map minmax function is used to normalize the input data 262
P _ train and P _ test and the target data T _ train and T _ test. Scales the data to a specific 263
range (between 0 and 1) for better neural network training. 264
Tenth item; 265
Root mean square error. Calculate the error between the neural network prediction 266
result and the actual target value. The calculation method of root mean square error RMSE 267
is used. 268
Eleventh item; 269
Drawing. The comparison diagram between the neural network prediction results 270
and the actual target values is drawn, and the root mean square error (RMSE) of each data 271
set is displayed. The comparison diagram of the training set prediction results and the 272
comparison diagram of the test set prediction results can be obtained. 273
Twelfth item; 274
Calculation of relevant indicators. Several key indicators of the neural network pre- 275
diction results are calculated, R2 (determination coefficient), MAE (mean absolute error), 276
MBE (mean deviation) and RMSE (root mean square error). 277
Thirteenth item; 278
Draw scatter plot. By drawing the scatter plot to visualize the relationship between 279
the predicted value and the true value of the training set and the test set, the training set 280
scatter plot and the test set scatter plot are drawn. 281
Fourteenth item; 282
Read the predicted data. Read data from Excel files and read data on four indicators 283
of China 's road diesel consumption, population, gross domestic product, and road freight 284
turnover from 2019 to 2021. 285
Fifteenth item; 286
Data transpose. The kes matrix is transposed, and the transposed matrix is re-as- 287
signed to kes. 288
Sixteenth item; 289
Data normalization. Each value in the original data is mapped to the specified range 290
by linear transformation. 291
Seventeenth item; 292
Simulation test. Perform neural network simulation tests. 293
Eighteenth item; 294
Data denormalization. The denormalized data is stored in T _ sim3, and it is directly 295
compared with the original output data to evaluate the performance of the model. 296
Nineteenth item; 297
Save the results. Save the data as an Excel file, and open and view these results when 298
needed. 299
4.3. Analysis of output results 300
First item; 301
The prediction results of the training set are compared with the prediction results of 302
the test set, as shown in Figure 3.1. 303 Appl. Sci. 2023, 13, x FOR PEER REVIEW 9 of 12

304
Figure 3. 1 Comparison of training set prediction results and test set prediction results. 305
The comparison of the prediction results of the training set: RMSE = 15101.9604, the 306
comparison of the prediction results of the test set: RMSE = 73520.6683 .The virtual depic- 307
tion shows that that the difference between the two is large. The data shows that the model 308
performs better in the training set and performs relatively poorly in the test set. 309
Second item; 310
Analysis and comparison of the training set and the prediction set fitting graph, as 311
shown in Figure 3.2. 312
313
314
Figure 3. 2 Comparison of training set and prediction set fitting. 315
From the figure, we can see that in the predicted value image of the training set and 316
the prediction set, in the fitting relationship between the scatter points of the predicted 317
value and the real value of the training set as well as the prediction set and the linear 318
fitting line, we can compare and observe that the fitting effect of the training set is very 319
significant, and the scatter points are in or very close to the linear fitting line, which indi- 320
cates that the model is very accurate in the prediction results of the training set and poor 321
in the structure of the test set prediction. 322
Third item; 323
Output analysis of prediction results of training set and test set. The prediction re- 324
sults of the training set and test set are shown in Table 3.1. 325
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
0 2 4 6 8 10 12 14 16
forecasting results
prediction samples
Predicted Value
True Value
0
1
2
3
4
0 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
prediction samples
predicted samples
True Value
Predicted Value
0
0.5
1
1.5
2
2.5
3
3.5
4
0 1 2 3 4
Prediction value of training set
The real value of training set
The predicted value of the training set vs. the
true value of the training set
0
0.5
1
1.5
2
2.5
3
3.5
0 1 2 3 4
Predict ed value of test set
The true value of the test set
Test set predicted value vs. test set true valueAppl. Sci. 2023, 13, x FOR PEER REVIEW 10 of 12

326
Table 3. 1 Test results of training set and prediction set. 327
R2 MAE MBE RMSE
training sets 0.99977 9473.3095 -2629.6813 15101.9604
testing set 0.99437 39126.4915 39126.4915 73520.6683
（4）R 2 ( coefficient of determination ), in the training set R 2 = 0.99977, the test set R 2 = 328
0.99437, we can see that both the training set and the test set, their test results are very 329
close to 1, which shows that the model has a good fitting effect on road freight volume. 330
Compared with the test set, the performance of the training set is more prominent. 331
Fourth item; 332
Analysis and forecast of freight volume in the past three years. The forecast results 333
of China 's freight volume in the past three years are shown in Table 3.2. 334
Table 3. 2 Prediction results of China 's freight volume in the three years. 335
Freight volume of China 2019 2020 2021
Prediction value 3099695.178 3131698.189 3534316.054
Actual value 3435480 3426413 3913889
We use the data from 1997 to 2013 as the training set, and select the data from 2014 336
to 2018 as the test set to predict the freight volume of China from 2019 to 2021. It is pre- 337
dicted that the freight volume of China 's highways in 2019 will be 3099695.178 million 338
tons. In 2020, the freight volume of China 's highways will be 3131698.189 million tons. In 339
2021, the freight volume of China 's highways will be 3534316.054 million tons. The actual 340
freight volume of China 's highways will be 34354.8 million tons in 2019. In 2020, China 's 341
road freight volume will be 3426413 million tons, and in 2021, China 's freight volume will 342
be 3913889 million tons. It can be seen from the comparison that the predicted Chinese 343
road freight volume in 2019-2021 is lower than the actual Chinese road freight volume. 344
Since 2019 to 2021 it is in the period of the new coronavirus epidemic. There are many 345
reasons why the actual freight volume is higher than the predicted highway freight vol- 346
ume. On the one hand, it is due to the national policy, the state is coordinating a series of 347
measures to promote epidemic prevention and control and social and economic develop- 348
ment. Strengthen the service guarantee of road freight employees, provide free nucleic 349
acid detection for them, provide guarantee for their commuting, and provide help for 350
truck drivers with difficulties in life. Make every effort to smooth the transportation chan- 351
nel, ensure the smooth flow of the main traffic lines, coordinate and linkage between var- 352
ious regions, solve the problem of road network disconnection in time and strengthen the 353
monitoring and dispatching of the road network. Furthermore, accelerate the establish- 354
ment of material transfer stations, connection points or distribution sites and make every 355
effort to organize emergency material transfer. Accurate management policies should be 356
implemented as well to optimize the epidemic management measures according to the 357
itinerary and whether the epidemic is involved and truck traffic should not be arbitrarily 358
restricted. With the gradual relaxation of prevention and control policies in various re- 359
gions, various restrictions on freight vehicles and epidemic prevention checkpoints have 360
been lifted, enabling freight vehicles to carry out cross-regional transportation more 361
freely. This has enabled truck drivers and logistics companies, which were originally lim- 362
ited due to the epidemic, to resume road operations, which has led to a recovery in road 363
freight volume. On the other hand, during the epidemic, the transportation of epidemic 364
prevention materials and the transfer of materials needed to ensure people 's production 365
and life also promoted the increase of highway freight volume to a certain extent. During 366
the period when the epidemic situation was controlled and gradually stabilized, the pro- 367
duction and consumption demand of various industries began to rebound, and the circu- 368
lation of goods needed to be completed by road transportation. The rapid development 369
of the commercial industry made online shopping a choice for more and more people, 370 Appl. Sci. 2023, 13, x FOR PEER REVIEW 11 of 12

further promoting the growth of road freight volume. In addition, in order to stimulate 371
consumption and promote economic recovery, the state has also introduced a series of 372
policies to support the development of the logistics industry. These policies include re- 373
ducing logistics costs, improving logistics efficiency, optimizing logistics networks, etc., 374
which provide a strong guarantee for the development of the road freight industry. Fi- 375
nally, the advancement of digital reform has also injected new impetus into the develop- 376
ment of the road freight industry. Through the wide application of digital means such as 377
electronic licenses, the road freight industry has been able to achieve more efficient and 378
convenient services, improve transportation efficiency and quality, and further promote 379
the growth of road freight volume. In summary, the reasons for the increase in road freight 380
volume during the epidemic mainly include policy relaxation, demand recovery, policy 381
support and digital reform. These factors work together to make the road freight industry 382
maintain a steady development trend during the epidemic. 383
5. Conclusion 384
This study comprehensively considers a variety of factors, such as road diesel con- 385
sumption, population, gross domestic product, and road freight turnover on China 's road 386
freight volume. It fully considers the influence of independent variables on dependent 387
variables, and has certain operability. In the future, we can try to enrich the influencing 388
factors of freight volume and increase the spatial dimension characteristics, so as to enrich 389
the influencing factors of China 's road freight volume. In this study, through the estab- 390
lishment of BP neural network, the multi-factor prediction method is used to accurately 391
fit the original data and finally the predicted value of China 's highway freight volume is 392
obtained, which has achieved high prediction accuracy. After testing, the model has a 393
small error, which meets the prediction requirements of future freight volume, meets the 394
needs of economic development, meets the needs of regional and social development, and 395
provides a certain reference for correctly predicting the development trend of future 396
freight volume. 397
The significance of highway freight volume prediction involves many aspects. It is 398
conducive to enterprises to grasp the development trend of the market, understand the 399
changes in demand, and correctly formulate development strategies to cope with market 400
changes, reduce operating costs, improve transportation efficiency, and gain a foothold in 401
the market. Through the prediction of highway freight volume, it is conducive to the ra- 402
tional planning of highway operation lines, strengthening the planning and management 403
of highway vehicles, and rational planning of traffic flow. 404
For the case analysis of road freight volume in this study, we can improve from the 405
following aspects; increasing the vertical number of data and horizontal influencing fac- 406
tors as well as increasing the sample size of predicted data. While selecting the influencing 407
factors, we should consider the correlation with the freight volume. In addition, we can 408
also adjust the number of training rounds and select the appropriate model.


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