FIWARE-Ready: IMREDD - Traffic time series forecasting (Artificial Intelligence,Big Data,Software as a Service)

Traffic intensity forecasting service using historical data to forecast the traffic on the next day. This can be used optimize traffic management, reduce traffic congestion and improve user experience

The traffic forecasting service is designed to assist cities' systems in effectively managing their traffic and congestion and enhancing the overall user experience. By leveraging historical data and considering various factors such as the day of the week, and past traffic intensity patterns, the model aims to estimate the future traffic intensity at a specific location and day. The service employs standard AI algorithms to collect and process data, ensuring accurate predictions. These algorithms analyze the historical traffic data, identifying patterns and trends that contribute to traffic fluctuations.

By understanding these patterns, the model can generate reliable forecasts of traffic, empowering cities and citizens to make informed decisions regarding fleet distribution and allocation. The traffic forecasting service offers a valuable solution for traffic management that can enhance user satisfaction.

As cities embrace the concept of smart cities, the traffic intensity forecasting service plays an important role in addressing the challenges and needs of urban mobility. One of the key challenges is optimizing traffic management within the city by efficiently regulating the traffic. By accurately forecasting the traffic, the service enables better coordination with other modes of transport, such as buses, trains, and ride-sharing services, fostering a seamless and integrated transportation network.

-Efficient design using libelium’s SIA

IMREDD Traffic time series forecasting