Deconstructing traffic and the power of prediction for mobility and on-demand

When we think about traffic, it’s easy to focus on accidents, congestion, and patterns of travel. All of these have a big impact on how long it takes drivers to get from point A to B, but these changes also affect what’s happening in an on-demand app

An application should be able to adapt to changes in traffic to support route planning. However, this then affects the driver’s navigation experience and what the customer sees on their device.

While these components are individual parts of the journey, they work together to reduce the time all parties spend in congestion and to create a user experience that provides total clarity as to when the customer or their delivery is going to arrive.

Route planning is one of the best ways to obtain an accurate estimated time of arrival, aka, an ETA. Ideally, this ETA is the same for both user and driver throughout the delivery process.

While a user interacts with an application, route planning might occur in a couple of different places: when a customer requests a rideshare pickup, and when a driver is matched with a passenger or delivery (or series of deliveries or drop-offs). It will then repeat for the driver when they are matched with subsequent passengers while on their current trip.

The optimally laid-out route takes into account existing road conditions, known traffic incidents, and historical and real-time traffic and road information. That’s a lot of detail, so let’s break down what’s under the hood of an example app whenever a user requests a ride or delivery, built with TomTom Maps APIs:

On a map, the Traffic Incidents API overlays visual indications of accidents, road or lane closures, and other dangers on the road, such as ice. This portion of the Traffic API can return the magnitude of the event, the time it happened, and provide visual feedback that is easy to understand for all users (both drivers and passengers). These road hazard details may be something the user sees when first looking at the map portion of the application on their phone, so they can be aware of their surroundings before summoning a ride.

When a request is made, a quick evaluation of traffic occurs to determine the most congested areas. This is known as the Traffic Flow API and uses familiar-colored overlays of lanes to indicate how slow or fast traffic is moving on a particular stretch of road. Before using this information to help plot the fastest route, the map might display traffic incidents and flow within a radius of each point, origin, and destination.

In this key step, the Routing API uses TomTom’s routing engine to build a journey using not only traffic information from above, but also other data such as that of Speed Profiles. Where TomTom Traffic reports current speeds and conditions, updated every minute, Speed Profiles utilizes historical traffic data from the past year to note the actual average speed for a given stretch of road, at each time of day. This is key when comparing speed limits to actual speed traveled on certain roads – some might be habitually congested, and some might have higher regular travel speeds than expected.

These components represent a considered formula perfected over years of analyzing key information related to how and when traffic moves. It all happens behind the scenes in just a few seconds when a driver and customer are matched for a trip.

When all these components come together, they produce a route which is most likely to adhere to its original ETA – both for the driver arriving for pickup (of person or food delivery) and for a passenger reaching their destination.

In the unpredictable world of rush hour traffic, last-minute appointments, and dinner dates, selecting the path of travel least likely to create unforeseen delays goes a long way to keeping drivers, passengers, and delivery recipients happy and coming back to the app.