Compute to Commute
Keys, phone, wallet - check. When traveling anywhere, most of us rely on navigation apps—like Google Maps, Apple Maps, or local transit apps—to improve urban mobility and inform our path of travel and estimated time to destination. We have access to real-time updates on traffic and congestion per transportation type, recommended detours to avoid traffic, and bus times right at our fingertips. Because as good as that podcast that you’re listening to is, there are few things more frustrating than just missing your bus and getting to work late. Do you ever wonder how these apps are able to work with such precise accuracy?
Big Data Plays a Big Role
At the heart of powering navigation apps to improve our commutes is data. Big data. For instance, Google Maps relies on millions of gigabytes of data in order to work. And it doesn’t just stop with apps. Sensors on smart traffic lights, freeways, speeds signs, street cameras, parking lots and street lights are fueled by big data, especially in the impending 5G world. Sensors via smartphone apps will even be able to detect an abnormally bumpy ride to provide feedback that a road needs to be repaired, preserving the safety of subsequent drivers and reducing the high cost of sending surveyors on-site.
The Future of Commuting
With traffic increasing, ridesharing is only expected to grow and could even replace public transit all together in some areas. Analysis of data has even discovered that prohibiting left turns can help escape contraflow and save millions of gallons of fuel per year. With more accurate traffic and urban transportation data gathered along major commute routes, we’ll use fewer vehicles more efficiently. Self-driving cars are developing sensor-guided technology to determine when the driver can sit back and relax and when the driver will take over. Flying Uber taxis are planned to deploy in Dubai and Dallas by 2020 and are currently developing quieter technology and eco-friendly energy and emissions than their car counterparts. Some VC investors are staying grounded, sticking to more traditional means of solving the first and last-mile problem, which account for about 50% of commute time, by improving bike and scooter docking and dockless options in metropolitan areas.
Are We There Yet?
Big data is gathering more and more info about where people are going, how they’re getting there, what routes are available, what routes they’re using, and what vehicles they are using to get there. Urbanites’ ability and willingness to adapt to big data’s progression in commute technology will greatly impact the speed in which commute enhancements will be fully integrated. Much of commuter technology’s advancement revolves around sensors and surveillance to ensure safety and, in the case of the autonomous car, safety is dependent on the accuracy of sensor data.
The Engine Behind Big Data
Google Maps alone uses petabytes of data so, as you can imagine, the amount of data for all navigation apps and sensor data is massive. Data centers and the cloud are at the heart of making this tsunami of data useful. The infrastructure housing all this data must be resilient, secure, and scalable in order to continue to keep up with the data being produced by commuters, satellites, and IoT sensors and devices. As an extension to centralized data centers, edge computing strategies will allow for data to be processed and transferred closer to IoT devices, significantly decreasing latency.
As for analyzing and optimizing big data, machine learning (ML) plays a huge role in turning this data into something useful. ML algorithms can make automatic adjustments in real-time and identify patterns, all while making decisions with little to no human intervention. For example, your autonomous car may be able to automatically adjust your route to work based on the change in weather or a recent car accident that’s causing a traffic jam.
Here Comes the Cloud
With big data comes great responsibility. The cloud provides a solution to the inherent complexity that comes with processing massive amounts of data. Large volumes of data require more processing power and the ability to scale fast. Fortunately, cloud services have made scalability easy and seamless with readily-available infrastructure. The elastic nature of working with massive amounts of data requires the ability to quickly ramp up or down services within the cloud while only paying for what you use. For navigation and commuting, rush hour and the holidays will most likely see an increase in volume of data that needs to be processed since there are typically more people, vehicles, and devices on the road.
Ultimately, cloud services make it possible to spend less time on the technical aspects of an organization’s IT environment, which means more time can be spent on creating actionable insights from real-time big data analytics. This is extremely important to delivering the best user experience and ensuring the safety of commuters when things like driverless cars and smart traffic lights enter a more connected “smart cities” landscape.
Big Data, Big Opportunities
While technology hasn’t been able to completely eliminate traffic or speed up the subway, it has improved the way we travel and commute every single day thanks to big data and the cloud. Whether we’re scooting to work or eventually have our cars self-drive us across the country, data and the processing of data will continue to be a critical backbone to making our travel safer and more efficient.