Traffic Prediction Using Deep Learning / College mini Project

Summary

This project focuses on predicting traffic volume using deep learning techniques. The dataset includes various weather-related features, and traffic volume is the target variable. The team used an MLP regressor, mapping string-type data to numerical values for better processing. They scaled the data, visualized feature relationships, and trained the model using a 500-iteration approach. With a mean absolute error of 7%, the model provides reasonably accurate predictions. The project also involves deploying the model through Flask, allowing users to input features and predict traffic conditions via a web interface.

Takeaways

• 📅 The project focuses on traffic prediction using deep learning techniques.
• 📊 The dataset includes features such as date, holiday status, air pollution, humidity, wind speed, wind direction, visibility, and weather conditions, with traffic volume as the target feature.
• 🔢 Both numerical and string-type data are present in the dataset. The string-type data (e.g., weather conditions) was mapped to numerical values for model training.
• 📈 The data was scaled using Min-Max Scaler to a range of -1 to 1 to standardize different ranges of the data.
• 🧠 The team used an MLP (Multi-Layer Perceptron) regressor, a supervised learning technique, with the ReLU activation function and Adam optimizer.
• 💡 Mean Absolute Error (MAE) was calculated as 0.07, or 7%, indicating the performance of the traffic prediction model.
• 🖥️ For deployment, the project was implemented using Flask on the Visual Studio platform, enabling a web-based interface for interaction.
• 📱 The front end allows users to input features like date, temperature, holiday status, and weather conditions to predict traffic volume.
• 🚦 The model predicts traffic conditions (e.g., heavy traffic) based on the input features provided by the user.
• 🔧 Additional features include routes for training the model and testing it through manual input via a user interface.

Q & A

What is the target feature in the dataset used for traffic prediction?

The target feature in the dataset is traffic volume.

Which supervised learning technique was used for traffic prediction?

The MLP Regressor from neural networks, a supervised learning technique, was used for traffic prediction.

How was string data in the dataset handled for the MLP regressor?

String data in the dataset was mapped to different numerical values because MLP regressor cannot handle string data directly.

What technique was used to scale the dataset values?

The Min-Max Scaler was used to scale the dataset values between -1 and 1.

What error metric was used to evaluate the model performance, and what was the error value?

The mean absolute error (MAE) was used to evaluate the model performance, and the error value was 0.07, or 7%.

How were weather-related string features such as weather description handled?

Weather-related string features like weather description were mapped to numerical values to be processed by the MLP regressor.

What optimization function was used in the MLP regressor?

The Adam optimizer was used as the optimization function for the MLP regressor.

What front-end framework was used for the deployment of the model?

The Flask framework was used for the front-end deployment of the model.

What happens when the 'Click me to train model' button is pressed on the webpage?

When the 'Click me to train model' button is pressed, the model is trained and the corresponding code is executed.

How does the input form on the webpage interact with the prediction model?

The input form allows users to provide features like date, temperature, climate conditions, and weather description. After inputting the data, users can click 'Predict,' and the model will predict the traffic condition based on the provided features.

Outlines

00:00

🚦 Overview of Traffic Prediction Project

This mini project focuses on predicting traffic volume using deep learning. The dataset contains features such as date, holiday, air pollution, humidity, wind speed, wind direction, visibility, and various weather conditions. The main target is traffic volume. The dataset includes both numeric and string data, with string data mapped to numerical values for machine learning compatibility. The coding section includes importing essential libraries and using the MLP Regressor from neural networks. After preprocessing, the data is scaled using MinMaxScaler, and the MLP regressor model is trained and tested to predict traffic volumes, with a 7% mean absolute error achieved.

05:01

🖥️ Model Deployment and Prediction Interface

The second part describes the deployment of the model using Flask in Visual Studio. A web interface is created where users can input features like date, time, temperature, holiday status, climate conditions, and weather description to predict traffic conditions. After submitting the inputs, the model provides traffic predictions (e.g., heavy traffic). The flow of execution is described: the server is started, the model is trained, and predictions are made through the input form on the webpage. The result and model analysis are presented, showing the relationship between features and predictions.

Keywords

💡Traffic Prediction

Traffic prediction refers to the process of forecasting future traffic volumes based on historical data. In the video, it is the central theme of the project, where the team uses machine learning techniques to predict traffic volume using various features like weather conditions, date, and time. The accuracy of traffic prediction directly impacts urban planning and traffic management.

💡Deep Learning

Deep learning is a subset of machine learning, involving neural networks with multiple layers. In the video, the team uses a deep learning approach, specifically a Multi-Layer Perceptron (MLP) regressor, to predict traffic volumes. This method allows them to handle the complex relationships between the features and the target variable (traffic volume).

💡MLP Regressor

MLP Regressor (Multi-Layer Perceptron) is a type of artificial neural network used for regression tasks. It is mentioned as the main algorithm used to predict traffic volumes in the project. MLP regressor is part of deep learning, where layers of neurons process input features like weather and time to output predictions for traffic volume.

💡Feature Mapping

Feature mapping refers to the process of converting categorical data, like weather descriptions, into numerical values for use in machine learning models. In the project, the team mapped string-type data, such as weather conditions, to numerical values so they could be used effectively in the MLP regressor model.

💡Normalization

Normalization is the process of scaling data to a specific range, in this case between -1 and 1, to ensure that all features contribute equally to the model's learning process. In the video, the team used a MinMaxScaler to normalize their data, which included features like air pollution and wind speed, ensuring they were on the same scale for better model performance.

💡Mean Absolute Error (MAE)

Mean Absolute Error (MAE) is a common metric for evaluating the accuracy of regression models by calculating the average absolute differences between predicted and actual values. In the video, the team achieved a 7% error using MAE, which they considered reasonable for their traffic prediction project.

💡Flask

Flask is a lightweight web framework for Python that is used to create web applications. In the project, the team used Flask for deploying their traffic prediction model, allowing users to input features through a web interface and receive predictions on traffic volume. It serves as the backend tool for their model's deployment.

💡Data Scaling

Data scaling refers to adjusting the range of data values before feeding them into a model. The team used MinMaxScaler to scale the numerical data (e.g., weather conditions, traffic volume) to ensure they all lie between -1 and 1. This step is essential in ensuring that no particular feature disproportionately affects the model's learning process.

💡Training

Training refers to the process where a machine learning model learns from the input data by optimizing its weights to minimize prediction errors. In the video, the team trained their MLP regressor using the dataset, which included features like date, weather conditions, and traffic volume. The training process involved running 500 epochs to improve the accuracy of the traffic predictions.

💡Test Dataset

A test dataset is a separate portion of data used to evaluate the performance of a machine learning model. In the project, after training the model on the training dataset, the team tested it on a new dataset to validate its accuracy. The test dataset contained similar features and was used to compute the Mean Absolute Error for the model.

Highlights

The project involves traffic volume prediction using deep learning methods.

The dataset includes features like date, holiday status, air pollution, humidity, wind speed, wind direction, visibility, and other weather details.

Traffic volume is the target feature, with string-type data for weather conditions and types being processed.

The team used MLP Regressor from neural networks, a supervised learning technique, for model training.

Data preprocessing involved mapping string-type features to numerical labels to make them compatible with the MLP Regressor.

Min-Max scaling was applied to normalize feature values between -1 and 1.

Visualization of the correlation matrix was done using C library, showing relationships between features.

The model was trained using 500 iterations with the ReLU activation function and Adam optimizer.

The model's performance was evaluated using Mean Absolute Error (MAE), resulting in an error of 7% (0.07 MAE).

A test dataset was used to further evaluate the model’s performance, showing similar results to the training set.

Manual testing with sample input resulted in predicted traffic volume values, which were compared with actual values.

The model was deployed using Flask on Visual Studio for frontend integration.

The frontend allowed users to manually input features like date, time, weather, and climate conditions to predict traffic volume.

Flask was used to create routes for training the model and predicting traffic based on user inputs.

The project successfully demonstrated a functional deep learning model for traffic prediction, deployed with a user-friendly interface.

Transcripts

mini project about traffic prediction

using deep learning this is our Pro and

we have collected the data set and these

are the features which are in our data

set date holay air pollution humidity

wind speed wind direction visibility

view points and all other weather

details and this is a traffic volume

which is the target feature in our data

set and these are the string type data

data set which weather conditions and

the type of weather which is present and

actually we have this this is the

original data set which we have

collected from this data set we have

extracted the required data for our

project and coming to our coding

part in this we have done our coding

here

and we have imported all the libraries

which are use used in our project and we

have used MLP regor from neur networks

which is a supervised learning technique

and we have imported a data set train.

CSV which is the data set which we have

collected and here we have taken all the

attributes into our data set which which

are required and store them in the

traffic volume data set and from

there yes and we have collected the data

set these are all the data which have

which are value stored Fe features which

are of integer type and these data of

string type and we

have we have mapped them with different

for different labels and after

that for the data which is the data

which is of string type we have mapped

the data for different values as we all

know the string data is not trained by

the trained by our MLP regressor for for

that we have mapped our data for

different values for that we have used

this fun tools which is used for mapping

the string data to the different values

and here is the mapping procedure which

is done in our project and before

mapping these are all the data string

data which is in present in those

features and after mapping we can see

here the weather type and weather

description they are mapped to different

values

and this this is a relation Matrix

between the all the features which are

present in our data

data set and here we have done our

scaling part and the we the data which

we have collected it is of different

range values so we have mapped this by

using the mid Max scaler between minus1

to 1 and after scaling we have got these

features as these values

and this is a metrics we have visualized

using the C library

and and here comes we have used our MLP

regressor which is a multi-layer

perception regressor and we have try our

model using 500x and we have used the ra

by default we have used the Rao function

and AD

them for our optimization in our project

and we have predicted the values and

these are all the these are the

predicted values and these are the

actual values which are there in our

project and for the we have another data

set which is

test data set and we have tested our

data and calculated the error value as

it is a regressor regression we have

calculated as a mean absolute error and

we have got

0.07 as an error which is of 7% for

overall the project

and and here I have tested the data by

giving a manually some input to our

model and I have got the output 171 it

is a scaled value and after iners

scaling we have got this range traffic

volume as this

value based on this traffic volume we

have predictor we have mapped our data

and

predictor our data and the condition of

the traffic and coming to the deployment

part we have done our deployment in this

Visual Studio platform by using the

flask here we can see we have imported

to the flask

library for the front end part we have

used the flask uh

uh here are the routes you can see when

we the homepage is home.

HTML when we run the

server the the test. P the server has

started this is the landing page which

we have created when we click on this

button click me to train model the model

will be

trained uh after clicking the train here

you can see this code this code will be

executed and the model will be trained

and uh you can see the terminal that the

code has been

executed and next this page is re

redirected here you can see the input

text

boxes uh using this uh text boxes you

can see the input uh the day feature

date feature the day feature the

time the temperature and is holiday or

not the climate conditions

and the weather

description rainy and the you can see

there are many

features um we can give anyone turn from

result when we click on predict here you

can see the output heavy traffic uh

according to our given features the

output is predicted

uh these are the routes when we click on

predi this this code is exed and when we

click on

train when you click on train button

this code is

executed and this is the

output and these are the analysis of our

model on the features how the data is

[Music]

dependent thank you sir