Ride-sharing and ride-hailing services have revolutionized how people get around, but the business model can be quite expensive for both riders and drivers if not strategically set up. Ride-sharing companies need to focus on cost improvements and technological solutions to help them do more with their resources.
In this article, the term ride-sharing shall be used interchangeably with ride-hailing, even though they’re distinct in small ways from each other (ride-sharing is more like carpooling and involves several stops along the way, while ride hailing involves a passenger hailing a cab or calling up a driver to take them to their destination, without carpooling). Most people just call both models ride-sharing, and so will this article!
Now that definitions are out of the way, here’s a list of some of the ways data can help improve ride-sharing services:
- Data Can Help Improve Dispatch And Routing Algorithms
Ride-sharing could use data as a microtransit solution in a number of ways, including reducing costs. Costs can be made to go down by improving how the system assigns rides to drivers. Rides should be assigned in a way that it minimizes the total number of trips taken by drivers or the number of miles driven without a passenger. Data science can provide these analyses, allowing companies to make better spending decisions and use resources more efficiently.
- Advanced Analytics Techniques Can Make Pricing Models More Effective
The goal of pricing is to balance supply and demand so that the number of rides offered at each price is in line with what customers are willing and able to pay. Ride-sharing companies must be careful when it comes to adjusting prices.
They should monitor if there’s an imbalance between supply and demand at different times of day, days of the week, etc. If there aren’t enough drivers available during peak hours, for instance, prices will need to go up until more people sign up for shifts. Ride-sharing companies can use advanced data science techniques, like machine learning algorithms to adjust fares, without overcharging riders or underpaying drivers.
- Big Data Can Help Improve Safety
By collecting data on driver behaviour, ride-sharing services can monitor when drivers are behaving unsafely behind the wheel. This helps create a safer system overall. For example, if a driver is consistently cutting it close when making turns or is speeding above the limit, they’ll notice the pattern in their software and help them take appropriate measures.
While it is, of course, the driver’s responsibility to monitor their own behaviour, an automated way of monitoring could more easily catch things that may otherwise be missed. This makes roads safer for everyone
- Measuring Service Supply & Demand
As cities become more populated, people require better transportation services to meet their needs. By using data to analyse travel patterns, companies can determine the best places to implement new hubs where drivers can wait for passengers. Companies can set up platforms that collect trip information from their ride-sharing services, and, then, analyse those accordingly. This allows cities to use valuable resources more efficiently by placing pickup points in areas with high demand rather than wasting them on areas of low traffic.
- Improving Vehicle Utilization & Maintenance
Through detailed insights into vehicle usage, companies can optimize their fleet by scheduling car maintenance ahead of time and identifying underperforming cars that need repairs or updating. Drivers working for ride-sharing services are already incentivized, through bonuses and promotions, to drive newer models with low gas mileage. Data helps these companies determine the best allocation of resources in order to maximize efficiency.
- Gaining Insight Into Rider Behaviour Over Time
Data collected over the course of a single trip is useful when analysing what’s happening in real-time. However, it becomes exponentially more powerful when looking at an individual’s complete travel history. By tracking how someone uses ride-sharing services, companies can determine long-term behavioural patterns and save their most frequent riders time by automatically suggesting nearby pickup points.
- Storing Data For Financial Analysis
As with any business, data is incredibly important when it comes to financing management. Most companies are required to report financials every quarter, including revenues, assets, expenses, etc. With big data infrastructure in place, ride-sharing companies can easily pull these reports any time, instead of waiting for the end of the quarter. This gives the company a chance to take action before it’s too late.
- Data Science Can Be Used In Ride-Sharing Services For Insurance Purposes
Insurance rates for people driving on the platform should reflect factors like driving history; mile drove while carrying a passenger, weather conditions at the time of the trip, etc. Since insurance premiums tend to vary by zip code, ride-sharing companies should consider data science approaches in order to make sure they’re charging a fair price for insurance based on geographic location. This way, riders in low-risk areas won’t have to pay unfairly high premiums, and drivers who genuinely live in high-risk zip codes can also receive accurate prices.
Conclusion
There are many ways data science can help improve ride-sharing services. Ride-sharing companies should use advanced analytics approaches to allocate resources more efficiently, increase profits, as well as provide quality service. Data science is a great way for these companies to gain new insights on how they can remain competitive now and in the future.
