![]() (Just in case you’re unfamiliar with how infrastructures for collecting, processing, and storing data are designed and work, you can visit our related post on data engineering to get an idea.) It’s easier to get this information from the aforementioned providers that gather data from a system of sensors, diverse third-party sources, or make use of GPS probe data. Devices used to collect this data areįortunately, you don’t have to install these devices all over the place on your own. Then, you’ll have to collect both historical and current traffic-related information such as the number of vehicles passing at a certain point, their speed, and type (trucks, light vehicles, etc.). Connecting to such global mapping data providers as Google Maps, TomTom, HERE, or OSM is a great way to obtain complete and up-to-date information. First of all, you need to have a detailed map with road networks and related attributes. So, there are several main groups of data that you’ll have to obtain. Traffic is influenced by many factors, and you should consider all of them to make accurate predictions. ![]() ![]() But first, we’ll look into what data is needed for traffic prediction and where you can get it from. We’ll describe some effective algorithms further on. Precise forecasts of road and traffic conditions to avoid congestion are crucial for such companies’ planning and performance.Īs of today, different machine learning (and specifically deep learning) techniques capable of processing huge amounts of both historic and real-time data are used to forecast traffic flow, density, and speed. Often, it’s not only related to current trips, but also to activities in the future. Transportation, delivery, field service, and other businesses have to accurately schedule their operations and create the most efficient routes. Another area of implementation is the logistics industry. These systems use current traffic information as well as generated predictions to improve transport efficiency and safety by informing users of current road conditions and adjusting road infrastructure (e.g., street lights).Ģ. In the last ten to twenty years, many cities adopted intelligent transportation systems (ITS) that support urban transportation network planning and traffic management. Traffic prediction is mainly important for two groups of organizations (we’re not talking about folks planning a weekend getaway, you know). Traffic prediction means forecasting the volume and density of traffic flow, usually for the purpose of managing vehicle movement, reducing congestion, and generating the optimal (least time- or energy-consuming) route. What is traffic prediction, who needs it, and why is it important? But first, let’s start with explaining why it’s important at all. In this post, we explore what’s going on behind the scenes of traffic prediction, which data is used, which technologies and algorithms are implemented, and how to get that desired forecast to your screen. But have you ever thought about how Google Maps knows what to expect on the way? Multiple logistics-related businesses heavily rely on the accuracy of these calculations. ![]() Navigating tools like Google Maps or Waze show you the time needed for your trip, calculate your ETA, and create the most optimal route based on road conditions and predicted traffic. How often do you yourself get stuck in the jam wishing you’d known about it in advance and took a different route? And how often do you have to apologize to your customers for your drivers being late because of traffic? In 2021, NYC drivers lost an average of 102 hours in congestion – and before the pandemic that score was even worse. What else to consider Reading time: 9 minutes.Custom development and API integrations. ![]() Algorithms for generating traffic predictions.What is traffic prediction, who needs it, and why is it important?. ![]()
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