Transportation and Travel

Transportation and travel industries have utilized machine intelligence for generations. In recent years, machine learning has played a critical role in smart transportation. SpeedWise® Machine Learning (SML) algorithms can help transportation and travel industries quickly and efficiently apply solutions that will reduce congestion, improve safety, expand efficiency of transportation, and ultimately enhance customer experience.

Use Cases


Machine learning can be used to detect traffic patterns to predict which locations high traffic may occur. Anticipating traffic ahead of time can help authorities create plans to reroute traffic in advance to prevent blockage. Driving apps can also utilize machine learning by warning users of high traffic locations.

Public Transportation

The time of arrival for transport services is one of the most challenging variables to account for in public transportation. Machine learning can be used to reliably predict bus arrival times based on real-time bus location data and variables such as traffic, operational delays, weather, vehicle breakdowns and more.

Flight Delays

Machine learning can use historical flight data and variables such as weather conditions, mechanical issues, demand, and more to forecast flight delays. Predicting flight delays can help companies better prepare by avoiding the key variables that are causing the flight delay.

Flight Fare and Hotel Prices

Machine learning can use variables such as seasonal trends, demand growth, special offers, and more to predict a drop in prices for hotels or flights. Many applications can use these insights to tell customers about a price drop or good deal.

Dynamic Hotel Room Pricing

Have you noticed examples of hotels changing their prices daily? Price optimization can be achieved with the use of machine learning. Machine learning can use different variables to forecast the best price, competitive with what other hotels are offering.