Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Tambajoyce/Lab5Traffic-Flow-Prediction-in-Yaound-Cameroon-Using-Neural-Networks

Domain:

mobility
Creator:
Tam
Host:
Traffic Flow Prediction in Yaoundé, Cameroon Using Neural Networks ## Project Overview This project aims to predict traffic congestion levels in Yaoundé, Cameroon, using historical traffic data, weather conditions, and time-related features. By leveraging neural networks, the project focuses on forecasting traffic flow and congestion patterns to support smart city solutions and improve transportation management. The methodology involves handling sequential data and multi-variable forecasting to achieve accurate predictions. ## Dataset The dataset includes: 1. Historical traffic flow data. 2. Weather conditions (e.g., temperature, humidity, precipitation). 3. Time-related features (e.g., day of the week, time of day). Ensure that the dataset is preprocessed and formatted for sequential input, suitable for neural network models. ## Objectives 1. To predict traffic congestion levels in Yaoundé, Cameroon, using historical traffic data, weather conditions, and time-related features. 2. To apply neural networks for forecasting traffic flow and congestion patterns, a key aspect of smart city solutions and transportation management. 3. To explore sequential data handling and multi-variable forecasting techniques. ## Methodology ### 1. Data Preprocessing - **Data Cleaning**: Handle missing values, outliers, and inconsistent records. - **Feature Engineering**: Extract relevant features from time-related data and weather information. - **Normalization**: Scale the data for optimal neural network performance. ### 2. Model Development - Use recurrent neural networks (RNNs), such as LSTMs or GRUs, to model sequential dependencies in traffic data. - Experiment with dense and convolutional layers for feature extraction and forecasting. ### 3. Training and Evaluation - Split data into training, validation, and test sets. - Use metrics like Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to evaluate model performance. ### 4. Deployment and Testing - Test the …

Visit

github.com

Similar

Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of AlgeriaGraph neural networks for laminar flow prediction around random two-dimensional shapesModelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection techniqueMonthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP DataPrediction intervals for electricity load forecasting using neural networksPrediction of paste backfill performance using artificial neural networks

Prediction of groundwater flow in shallow aquifers using artificial neural networks in the northern basins of Algeria

Abstract Prediction of groundwater flow fluctuations is considered an important ste

Graph neural networks for laminar flow prediction around random two-dimensional shapes

In the recent years, the domain of fast flow field prediction has been vastly dominat

Modelling flow dynamics in water distribution networks using artificial neural networks - A leakage detection technique

Computational approaches can be used to detect leakages in water distribution networks. One such app

Monthly Predicted Flow Values of the Sanaga River in Cameroon Using Neural Networks Applied to GLDAS, MERRA and GPCP Data

Prediction intervals for electricity load forecasting using neural networks

Most of the research in time series is concerned with point forecasting. In this paper we focus on i

Prediction of paste backfill performance using artificial neural networks

Increasing regulations and social expectations of mines to minimize environmental impacts whilst ens