For many years, new technology has been a catalyst for change in the transportation sector. The ridesharing industry is one of the most recent examples. Artificial intelligence technology is integral to the evolution of this new industry.
AI is the driving force of change for ridesharing companies
The ridesharing industry is growing much faster than skeptics initially predicted. In 2015, only 15% of people in the United States used a ridesharing service. In 2018, that figure more than doubled to 36%.
Despite the robust growth in this relatively new industry, many challenges and concerns still remain. Artificial intelligence could address many of these concerns. This will be taken into consideration with a number of new ridesharing startups, as well as taxi application development.
Some examples of ways that artificial intelligence is being used to benefit the ridesharing industry are listed below.
Provide better Pay equity
One of the biggest criticisms of Uber and other ridesharing companies is inadequate compensation. Drivers are independent contractors, so ridesharing companies have had more leeway to limit wages and benefits. Since drivers are generally compensated based on performance, they have to find ways to increase productivity to maximize revenue.
Under the status quo, the pay structure for ridesharing companies can create safety concerns. Drivers need to balance safety with their desire to generate more revenue. They may be tempted to speed or minimize breaking distances to transport passengers more quickly to pad their hourly earnings. This places them and the passengers in danger, while also jeopardizing the brand image of the ridesharing company.
A new study found that artificial intelligence can create an ideal solution. They found that AI algorithms are able to help drivers increase revenue without lowering safety standards. Various artificial intelligence solutions can be used to accomplish this. The most obvious benefit is using machine learning to identify the most efficient routes, so drivers can complete the assignment and get their payment more quickly without having to speed and endanger their passengers.
Maximizing vehicle occupancy
One of the most underutilized opportunities for ridesharing companies to increase revenue is by maximizing occupancy. The same driver might be able to pick up more passengers along the same route, which allows them to generate more revenue in the same amount of time. It also reduces the distance that they need to travel, which lowers their carbon footprint. Furthermore, maximizing occupancy reduces overall waiting times, which is important for customer satisfaction.
Although the benefits of maximizing occupancy are evident, it can be difficult to implement. Fortunately, artificial intelligence is making it more feasible. Underscore.VC conducted an interview with a new ridesharing startup. The founder said that they used sophisticated AI tools to plan passenger pickups better to maximize occupancy.
Identifying an authorized ride sharing vehicles
Some unscrupulous drivers fraudulently impersonate employees of ridesharing companies. They might pose safety risks for customers if their intention is to rob assault them. They might also charge higher fees than the real ridesharing company permits. They might also have lost their privilege for working with a ridesharing company after a traffic violation or performance issues.
Fraudulent drivers create significant risks to customers. Fortunately, new artificial intelligence algorithms are making it easier to identify imposters. This study from Elsevier presents an overview of the solutions that they offer.
So far, privacy concerns have limited government options for addressing ridesharing fraud. Fortunately, the Elsevier article points out that a team of AI experts have found a solution that doesn’t require access to private data.
Yang and Wang’s group has found an approach that doesn’t require such data. Their solution uses the observation that cars used for ridesharing, whether authorized or not, behave like taxis. In contrast, many family cars “ mainly used for a few regular journeys, to work and to the supermarket “ behave more like buses. Trajectory data for bus and taxi journeys is widely available in the public domain. The proposed algorithm is called CoTrans and uses an artificial intelligence technique called transfer learning. This is a type of neural network that works in two stages.
