Traffic incident details support users by helping them make informed travel decisions.
The relationship of traffic and city centers for rideshare drivers
This experience for customers – those doing the ordering, riding, or summoning – is perhaps most prized, but shouldn’t come at the expense of the drivers’ experience. It’s important to recognize that drivers and riders might not share the same exact mapping experience at the same time, and that’s for good reason.
Within rideshare models, drivers typically collect serial orders, which means an application may be constantly searching for nearby drivers, whether or not they are already on a trip. You’ve probably seen this in motion if you’ve driven for or taken many ride-shares; in the last few minutes or miles before drop-off, a driver is often pinged to take on a subsequent trip. The goal is to minimize their time and fuel use by picking up rides or orders near their drop-off location.
This is why it can be even harder to plot long-distance fares, and instead it’s logical for mobility applications to focus on city centers. Trips to less populated areas mean that the driver is less likely to have a ride request come in after their drop-off; without a passenger, the driver is then going without fare while making their way back to a city center with more users.
When drivers collect subsequent rides in city centers, they also have the luxury of operating within nearby access to many more major roads and freeways, allowing the routing of many possible detours from trouble spots. These major roads usually have more regular traffic patterns, bolstering the accuracy and reliability of traffic predictions. With the addition of more physical room for accidents to be cleared and allow cars to move around incidents, they allow for fewer holdups and represent a denser field of data as they are more widely traveled.
Creating an optimal experience around time estimation and traffic for rideshare drivers is key, but it doesn’t complete the ideal user experience yet. Next, let’s focus on the components that help this application retain users from short-range delivery applications.
How traffic data supports short-range delivery
In the example above, we talked about what happens under the hood after a passenger makes a request which is sent to a driver. When you open a delivery app, typically there are estimates already waiting for you, giving you immediate insight into how far away your delivery might be before you take any action.
When traffic predictions fail drivers, they also fail customers – however, when they support drivers, they create seamless transitions that go unseen by users. Better data leads to better informed estimates and enables smarter use of trip time for drivers. A popular example of this is making multiple deliveries along the same route for short-range food delivery.
From a delivery driver’s perspective, delivering multiple food orders along a similar path of travel saves fuel, and allows them to take new orders faster to maximize their working time, just like our rideshare driver. Optimizing deliveries along fastest routes is especially important in the case of multiple orders at once, so that orders delivered farthest away from the driver still meet customers’ expectations.
By comparison to the driver’s experience flow, the user’s might show lags while a driver drops off a nearby food order between the restaurant and their final delivery spot. This could be the result of avoidable traffic conditions. If traffic along routes is estimated with multiple stops in mind, the driver will be able to adapt their journey to create the best possible delivery time for all of their customers in that trip.
Ideally, adjustments to the driver’s delivery route made in the interest of traffic means that no customer will notice delays with their order. Similarly, drivers can communicate with each customer and provide real-time updates if necessary, working to preserve the user experience to keep customers coming back to the app.
Traffic-based decisions drive better results
Mobility applications require building traffic-optimized experiences both for drivers and customers, as each trip represents an exercise in both parties evaluating how well ETA calculations stand up to their promise.
Building these positive experiences early on, backed by TomTom’s traffic data and Maps APIs, can reinforce that trust you need to build up your user base and take your mobility ideas to the next level.