Developing a Data driven Solution to address traffic congestion in Lagos – using Ikorodu road to Maryland as a case study.

Traffic congestion in Lagos, Nigeria, is a persistent and growing challenge, leading to significant environmental, social, and economic issues. The project proposes a data-driven approach to address the problem, leveraging machine learning, real-time data collection, and advanced analytics. By utilizing real-time data, predictive models were developed to forecast traffic patterns and alleviate congestion.

The project integrates machine learning techniques for prediction and conducts exploratory data analysis to assess how congestion impacts travel time. Various visualizations, such as boxplots, line charts, bar charts, histograms, and correlation matrices, are used to explore relationships between key variables and gain deeper insights into traffic dynamics. Below is the exploratory analysis I did to show the relationship between the dependent variables and independent variables, where there’s congestion and no congestion, day of week and hour of week of traffic congestion occurances, and Also the models developed to predict travel time.