What Is Traffic Prediction?

What Is Traffic Prediction?
Traffic prediction is an essential task in the field of transportation planning. It estimates future traffic flows based on historical data and current road conditions. It can be used to improve travel time reliability and reduce its variability, which are important factors influencing people’s mode choices in the transportation system. 
It can also help transportation agencies better manage road conditions by providing a platform for real-time monitoring and adjustment of travel routes and schedules. Traffic prediction has many applications in real-time traffic management systems (RTMS) and smart cities. These can be used for incident detection and management, congestion avoidance, servicing operations planning, and route guidance.
How Does Traffic Prediction Work?
Traffic prediction is a way to predict how much congestion there will be on a given road or highway at a particular time. It’s used by transportation planners, engineers, and government agencies to plan future construction projects. Here are some of the popular methods for traffic prediction:
Coupled Method: This is one of the most popular methods for traffic prediction. It uses data from previous years to predict future traffic patterns. This method takes into account factors like population growth and weather conditions. The downside of the coupled method is that it assumes that historical trends will continue into the future;
Decoupled Forecasting: This method considers only current conditions like the current weather. This method can be less accurate because it doesn’t take into account past trends, future changes in population growth rates, or other factors that may affect road patterns over time.
Where Does Traffic Prediction Data Come From?
Traffic prediction data comes from various sources, including vehicle counts and road sensors. The data is collected over time to build a historical database that can be used to predict future road gridlock conditions.
Vehicle counts are used to estimate traffic flow in many cities worldwide. In addition, some towns have deployed road sensors that collect data on how many cars pass by each minute. This information is then processed by specialized software to estimate the number of vehicles per hour on that section of highway or city street.
Traffic prediction data can come from these sources:
Road Sensors: These devices measure the number of vehicles passing them per minute and transmit this information back to a central system where it’s processed into a usable form. Road sensors are typically placed at intersections and other locations where traffic volumes are high enough for their data to be meaningful;
Vehicle Counts: The most common way to measure traffic volume is by counting passing cars over time using counters or other devices such as inductive loops embedded in the road surface, which sense passing vehicles’ tires as they pass over them. This method works well for measuring volume but does not provide information about congestion or speed limits;
Historical Data: Historical data is helpful for understanding how traffic patterns change throughout the day. You can view this information in various ways: by the hour, day, or month, by direction of travel, and by day of the week. If you’re trying to predict the patterns on a specific date, you may want to look at historical data for that particular day of the week and time period;
Weather Forecasts: Weather forecasts can help predict how bad road congestion might get during storms or other natural disasters. For example, if a hurricane is coming up the coast, people will likely evacuate the area and look for alternate routes out of town. This could cause several hours of delays on major highways and roads;
Traffic Cameras: These are used to capture images of road conditions at key intersections or on major roadways. The images are then used to create an animated view of traffic flow, which can be exported as a video file or viewed online;