Configuring Linux systems to support smart transportation and smart logistics development

Configuring Linux systems to support smart transportation and smart logistics development

Smart transportation and smart logistics are important application areas of modern technology. By integrating technologies such as the Internet of Things, artificial intelligence, and big data, traffic flow can be achieved Optimization, logistics route planning and transportation efficiency improvement. In this process, configuring the Linux system becomes a crucial step. This article will introduce how to configure a Linux system to support the development of intelligent transportation and intelligent logistics, and provide corresponding code examples.

First, we need to install the necessary packages and dependencies. In Ubuntu system, you can use the following command to install the required software packages:

sudo apt-get update
sudo apt-get install -y python3 python3-pip
pip3 install numpy pandas tensorflow

The above command will update the system package information and install Python3 and related software packages, among which TensorFlow is a popular machine learning framework , widely used in intelligent transportation and intelligent logistics.

Next, we need to configure environment variables so that the system can correctly identify and run Python programs. In the Ubuntu system, you can configure environment variables by modifying the .bashrc file. First, open the .bashrc file with the following command:

nano ~/.bashrc

Then, add the following line at the end of the file:

export PATH=$PATH:/usr/local/bin
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/lib

Save the file and exit. Run the following command to make the configuration take effect:

source ~/.bashrc

Now, we can start developing related functions of intelligent transportation and intelligent logistics. Here is a simple example code that demonstrates how to use TensorFlow for traffic flow prediction:

import numpy as np
import pandas as pd
import tensorflow as tf

# 导入数据集
data = pd.read_csv('traffic_data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values

# 数据预处理
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)

# 构建神经网络模型
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Dense(units=32, activation='relu', input_shape=(X_train.shape[1],)))
model.add(tf.keras.layers.Dense(units=16, activation='relu'))
model.add(tf.keras.layers.Dense(units=1, activation='linear'))

# 编译并训练模型
model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(X_train, y_train, batch_size=32, epochs=100, verbose=1)

# 预测并评估模型
y_pred = model.predict(X_test)
mse = tf.keras.losses.mean_squared_error(y_test, y_pred).numpy()
print('Mean Squared Error:', mse)

The above code uses a simple neural network model to predict traffic flow. First import the data set, and then perform data preprocessing, including splitting the training set and test set, and performing feature scaling. Next, build the neural network model and compile the model using the Adam optimizer and mean square error loss function. Finally, model training, prediction, and evaluation are performed.

In addition to traffic prediction of intelligent transportation, we can also use other functions supported by the Linux system to develop path planning and transportation optimization of intelligent logistics. For example, we can use open source path planning libraries, such as Graphhopper or OSRM, to calculate the shortest path. We can also use the network tools provided by the Linux system, such as IP routing tables and QoS (Quality of Service) configuration, to optimize network communication for logistics transportation.

To sum up, by configuring the Linux system to support the development of intelligent transportation and intelligent logistics, we can use powerful open source tools and libraries to achieve functions such as traffic flow prediction, route planning, and transportation optimization. We hope that the configuration and code examples provided in this article can help readers better carry out related development work.

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