For the most part, this data is usually accurate, unless there is a recent change in patterns like construction or a crash at the site. This data can also be used to predict traffic in future. All of these parameters help you give an accurate and real-time traffic update. Google Traffic prediction is based on several factors including Public sensors, GPS data, and analysis of the past record of traffic in the area. In this guide, I’ll show you how to predict traffic on Google Maps for Android. My favorite is the real-time traffic prediction but there is a hidden feature which lets you predict traffic at a certain time. I keep discovering new features like inbuilt fare prediction, crash and speed trap reporting, and traffic prediction. Let's use a simple example of a route planned for Thursday 1 Dec, 2022 in Vancouver, Canada.Google Maps has plenty of features which enhance your driving experience. If you are already using an existing route optimization API, you probably are receiving the route solution as an array of stops, each with an ETA. The Google Maps API does not account for time windows, priorities and service time at each stop, so instead of sending the entire sequenced route to Google, we are just going to ask Google to give us the travel times between stops and adjust our route's ETAs accordingly. What we are going to do here is adjust the ETAs from our route optimization system to account for real time traffic. Google gets their real time traffic data from the hundreds of millions of people that use the Google Maps app while driving. Please contact a Google Maps Partner for access. At the moment, availability is limited to larger enterprise customers. In mid 2022, Google launched the Cloud Fleet Routing API that natively supports real time traffic with a Google Maps Distance Matrix integration. Unlike Google and Mapbox, LogisticsOS is a young startup, but they are able to provide real time traffic together with their route optimization API through licensing deals with established mapping companies such as Tom Tom and HERE Maps. Forecasting traffic conditions requires significant computing power and access to data, which is why this series of blog posts will feature three service providers - Google Maps, Mapbox and LogisticsOS, that have delivered real time traffic data to customers at scale. Most delivery companies plan their routes the night before, so it would not make any sense to base ETAs (estimated arrival times) on traffic conditions at the time the route was created. The same driver's schedule adjusted for real time traffic.īefore we start, it's important to clarify that when I say "real time traffic", I actually mean the predicted traffic at the time the driver starts his route (this also ignores congestion and delays caused by traffic accidents and ad hoc road closures). Part 3: The best route optimization API for ETA accuracy with real-time traffic data A driver's schedule without real time traffic. Part 2: Real-time traffic with the Mapbox Matrix API Part 1: Real-time traffic route optimization with Google Maps (this article) Not taking real time traffic into account might cause your drivers to miss critical time windows and end their route late, leaving both drivers and customers unhappy. This is particularly important if you operate in cities such as Los Angeles, Seattle or New York that see significant variations in average driving speeds over the course of the day due to rush hour traffic. The first in a three-part series, this article shows how to improve the accuracy of your routes using Google Maps.
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