Four ways deep learning is changing the future of ridesharing

New advances in big data and predictive analytics will be enormously beneficial to ridesharing companies


Anyone conducting a SWOT analysis of the ridesharing industry will reach some interesting conclusions. The industry is growing at a remarkable pace, with some experts predicting that the industry will grow 900% by 2030, reaching a value of $285bn. It may even turn out to be the downfall of the taxi industry.

At the same time, the industry is undergoing a number of challenges that could curtail growth. Predictive analytics models are key to bolstering growth and minimizing risks.

Here are some ways that predictive analytics will influence the ridesharing industry.

Knowing when people will need ridesharing services

In March, a study from Penn State found that predictive analytics could play a vital role in the future of the ridesharing industry. The report said that new algorithms will soon be able to predict when customers need rides before they even request them. Jessie Li, associate professor of the Information Sciences and Technology Department at Penn State, stated that this would significantly reduce customer wait times. Li said that ridesharing companies could dispatch cars to customers before they even called for them.

There is another benefit this feature that is less obvious, but equally important. Since taxis and ridesharing companies would know more precisely when customers needed a ride, they would not need to wait nearly as long for them to be picked up. This would minimize pollution and the time that driver spend idle without generating revenue.

What factors will predictive analytics models depend on? It is hard to say since even the current models are in their infancy. However, we can venture some educated guesses. Here are a few likely variables:

  • It will take into consideration the demographics of the customers. Since older people probably need more time getting ready, they may send an alert less quickly than for younger users.
  • They will consider weather patterns and any factors that will cause customers to experience an emergency.
  • They will look at the time of day for context and compare it against the daily schedule for every 10 customers. This will be particularly important for customers that need a ride-sharing service to get to work each day.
  • Of course, these analytics models will also gauge traffic patterns to account for delays the drivers will face while picking the customer up.

Picking up customers on time will be especially important to maintain a decent reputation and customer service ratings. Predictive analytics models will possibly increase customer satisfaction by 80% or more, depending on their level of effectiveness.

Improving security

There is one major downside to using a service like Uber over a traditional taxi: data security and privacy. You need to share a lot of information when you create an account with a ridesharing company. Unfortunately, we have discovered that this data is not immune from hackers.

Hackers breached Uber last year and users were not happy that the company waited to report the incident. The good news is that a new report has shown that AI may be an invaluable tool to protect against these breaches. Improving security should restore customer confidence.

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Predicting the market value of different municipalities

Ridesharing companies are still looking for new markets to expand into. According to this ridesharing infographic, they still only operate in around 400 cities throughout the US. Some cities have outright banned them, largely to protect the taxi industry. Others are not a lucrative enough market yet.

However, there are also many new markets that have not been tapped which could be highly lucrative for ridesharing companies. Predictive analytics models can identify promising market opportunities that may have been overlooked.

Reducing scandals with employees

Ridesharing companies have been under fire after a number of issues with their employees, with some drivers even being charged with sexually assaulting passengers. Many of these incidents could have been prevented by doing adequate background checks. Fortunately, new advances in data technology have made conducting background checks much easier, which should help them reduce the prevalence of these types of issues.

However, companies will still need to be cautious about using overly draconian predictive analytics models. They have to make sure that the developers don't use discriminatory models to disqualify drivers.

Predictive analytics is the future of ridesharing

The ridesharing industry was born from the big data Renaissance. This has given it a strong competitive advantage over traditional taxi companies. New advances in big data and predictive analytics will be enormously beneficial to ridesharing companies.


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