Vehicle traffic and flood monitoring with reroute system using Bayesian networks analysis

Heavy vehicle traffic and flooded areas are problems experienced on roads because of unimproved road infrastructures and environmental deviations. These factors affect vehicle drivers negatively as they contribute to stress, health problems, and wastefulness of time. This study developed a system called ArRoad that monitors and analyzes vehicle traffic and flooded areas using network of sensors and real-time image processing which then predicts and visualizes possible alternative rerouting paths using machine learning. Water level sensor nodes are used to monitor the flooded areas while real-time video images from cameras are processed to extract the vehicle volume on the streets. A Bayesian Network is generated from the water level sensors and image processing data which provides possible reroute areas to avoid traffic congestion and flooded areas. All data are sent to a cloud platform through the Internet that can be accessed through a mobile user interface. This mobile user application provides information about the condition of the streets and possible reroute maps to users. The accuracy of the system is tested by actual implementation on a specific road. Results showed minimum accessing delay from using the ArRoad to navigate in rerouted paths to prevent impassable roads due to heavy traffic and flood. If effect, it lessens the amount of time experienced by drivers from heavy traffic condition and flooded streets which then improves the quality of life by preventing waste of resources such as time and money.