Last year we launched our on-demand rides and delivery solution to help businesses improve operations as well as transform the driver and customer journey from booking to arrival or delivery. When it comes to on-demand rides and deliveries, every minute matters. When users book a ride or order food, they want a seamless experience and real-time accurate updates. Today, we’re taking a closer look into data quality improvements for location, time and distance accuracy, and motorbike routes.
Machine learning helps drive location accuracy
Location accuracy stands at the base of our customers’ operation. The location signals that are coming from mobile devices can sometimes be off for various reasons and a driver’s location can get stuck, or jump around.
Recently we developed mechanisms in our fleet management product that can take in multiple location signals and determine the most reliable location to use for a given vehicle. With that, we noticed drastic improvements:
Eliminated long periods of location ‘stuckness’ almost completely; a vehicle is considered ‘stuck’ when we think it’s moving but the measured location is notReduced the jumpiness of the location signal by 52%-86%: ‘jumpiness’ is when a vehicle shows a sudden and usually drastic change in location. A jump is determined to exist when the speed the vehicle had to go at in order to cover the distance it did is unrealistic Reduced the average jump distance by 44%-86% : ‘jump distance’ is the distance between two consecutive location pings when we determined a ‘location jump’ has occurred.
Dunzo, a local e-commerce platform in India, explains how integrating the order tracking capability within Google Maps Platform’s On Demand Rides and Delivery solution has helped reduce support calls by 90%. The out-of-the-box solution helped Dunzo’s two-wheeler delivery partners with updating location sync to reduce stuckness and jumpiness as well as deliver premium user experiences.
When the vehicle location is more reliable, the dispatch decision is of higher quality, meaning there is a higher chance you will be able to make the optimal decision. This can lead to less wait time for consumers, increased driver happiness and fewer cancellations.
Improvements in ETA accuracy in motorbike routes
In many geographic areas, road space is limited and car ownership is prohibitively expensive so motorbike is a prominent transportation mode. Motorbike mapping requires unique routing, ETA models, and navigation capabilities. Improvements in motorbike routing and ETA estimation have enabled customers like Gojek to offer better overall services, even in geographies with poor wifi or missing roads.
Recently, we further improved ETA outcomes by developing new machine learning models trained specifically for motorbikes. These models help our systems account for differences in congestion and traffic flow that arise in different regions and scenarios.
Globally, we measured an ~8% improvement in ETA accuracy for riders in the general public, and a ~6% improvement in on-demand rides and deliveries ETA accuracy. In this on-demand economy, where consumers are accustomed to real-time trip and order progress, these improvements will significantly improve their experience.
We will continue to innovate on both our car and motorbike on-demand rides and deliveries capabilities based on customer demand and requirements. We are committed to the success of our customers by building a seamless experience for all parties—consumers, drivers, and fleet operators—in the rides and deliveries journey.
For more information on Google Maps Platform, visit our website.
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