Have you ever wondered what makes a ride-hailing solution so accurate? In order to meet the needs of riders and drivers alike, companies like Uber and Lyft commit countless hours to creating effective algorithms. In this blog, learn about different data structures and algorithms that are increasingly making appearances in our lives to solve the problem of getting people to their destinations quickly.
Introduction
Imagine that you’re going to meet your friend at a coffee shop. You order a ridesharing cab from a smartphone, and in a few minutes, the car pulls up. Now, you realize that you’re the first pick-up, meaning that at least one other person will be picked up before you reach the coffee shop.
Here, the job of the algorithm is to figure out a way to add one more passenger without turning your 15-minute trip into a three-hour journey.
The Role of Data Structures and Algorithms
For any globally acclaimed business, both the data and the algorithm work hand-in-hand to create innovative solutions. In fact, modern algorithms are written around data sophistication to prevent any leakage of core business information.
In the case of ride-hailing apps, the algorithm’s job is to take your customers from your initial location, known as the origin node, and deliver them to the drop-off or destination node. These nodes can be more than two if there is more than one passenger to be picked up along the trip.
Moreover, the streets in between two nodes are called paths. Therefore, the algorithm’s job is to structure the data in a sequence of nodes and paths from the origin node to the destination node.
In between, everything is recorded, such as waiting time, route navigation, the number of additional nodes, and much more. The job is never-ending and depends entirely on the strength of the algorithm for multi-tasking purposes.
So, what’s optimization?
In the context of algorithms, optimization improves another set of processes by identifying limitations. Some of the most common limitations occurring in ride-hailing apps include current location, traffic conditions, and available cars every time a user requests a ride.
By tackling such limitations, optimizations lead to better decision-making. Moreover, optimized algorithms are a lot smarter than their native counterparts, as they can detect delays on the path and improvise on the spot based on the data.
Creating An On-Demand Ride-Hailing Solution
You might have read about the features and workflow of the ride-hailing app, but now learn about the backend work as well. Let’s start by understanding the simple request process for booking a taxi.
The Dispatch Algorithm
The on-demand service receives the request from the cab through a web socket and tracks the location of the user via GPS. It also receives and records the following data points: number of seats, type of car, pool car, etc. This dispatch algorithm allocates the requirements to supply and makes requests for the cabs.
When supply receives the request from demand, the location gets updated using the cell ID. Supply sends the request to the server located at the nearest point to the user based on the GPS location data. After that, the system draws the circle and sends the request out to all the nearby cabs.
The servers here play a central role in handling new traffic allocations for the newly added city.
Overall, the dispatch algorithm sends and receives requests through WebSockets to make the application cooperative and scalable for heavy traffic.
The Map Algorithm
Before launching in a new area, Uber onboarded the new region to the map technology stack by using a third-party map service provider to build the map in their application. Currently, Google Maps API helms the third-party map service provider, which tracks the location and calculates ETAs.
An API is a type of software that acts as a communication bridge between the application and the service. It forms the basis of the algorithm that tracks countless vehicles at once and feeds the data back to the application. However, many other types of calculations take place simultaneously in the map algorithm. One such calculation covers spots or incorrect road geometry, which is also called trace coverage.
Trace coverage calculation is based on two inputs: historic GPS traces of all Uber rides and map data under testing. Not only does it cover traces on the map, but it also compares and matches them with road segments. Another such calculation takes care of the distance between the actual location and all the pick-up and drop-off points used by drivers, which is called preferred access point accuracy.
For ride-hailing apps, pick-up points are a really important metric, especially for large venues. They help the algorithm track the shortest distance by calculating the distance between the actual locations. Here, after the calculation has been done, the algorithm sets a pin to that location so that in the future, when a passenger requests a ride, the map guides the driver to the preferred access point.
Overall, the mapping algorithm calculates every pick-up and drop-off location of the rides to ensure the freshness and accuracy of the suggested access points.
The ETA Algorithm
ETA is an extremely important metric because it directly impacts the earnings of the ride-hailing business. Heavy traffic or road construction are the two main factors involved in computing the ETA, as it is calculated based on the road system (not geographically).
When a rider requests a taxi from a location, the app not only identifies the taxi but also includes the taxi that is about to finish a trip. Every 4 seconds, a car on the road sends a GPS location back to the service provider so that the ride-hailing solution can use the updated data. Therefore, it becomes easier to decide on a taxi that is about to finish the ride or is idle, closer to the location of the passenger who has just made the request.
Moreover, modern companies use AI-simulated algorithms to represent the entire road network on a graph to calculate the ETAs. The nodes represent intersections, and the edges represent road segments. Here, the use of data structures is instrumental in guiding the algorithm to make precise decisions more quickly. One-way streets, turn costs, turn restrictions, and speed limits are some of the useful datasets for this niche.
Conclusion
Modern ride-hailing companies rely on the ability of algorithms to consider many variables and improve their services based on them. As companies develop algorithms, they start with the most basic solution to a problem and keep iterating their experience as new challenges emerge. In the same way, your first minimum viable product for a ride-hailing solution may need to address problems. But with time and consistency, you can improve and optimize to cater to the needs of your customers.
