Optimizing EV infrastructure with mobile driving data

We’ve been hearing it for years: EVs are the future of mobility. Not only are they known to be better for the environment, but they can also be more cost effective to charge and maintain compared to traditional, gas-powered vehicles.  

However, the EV industry hasn’t quite taken off as expected, with major automakers scaling back on their initial plans. While many point fingers at the “EV infrastructure gap,” EV charging operators are facing their own unique set of challenges.  

Here are four key issues, along with how mobile driving data can help EV charging operators overcome these roadblocks.  

Four key challenges for EV charging operators 

#1 Operational costs

Opening and maintaining an EV charging site can be expensive. In the long-term, operators can expect costs like electricity, maintenance, and staffing – in addition to any software or equipment updates needed. As operators expand and establish additional locations, allocating budget and resources efficiently only becomes more and more difficult. 

#2 Data access and accuracy

A lack of data can be a huge obstacle when it comes to planning new locations as well as supporting existing ones. While many operators have insight into basic factors like number of charging sessions and the duration of those sessions, many are missing other valuable inputs like:  

• How often and how long drivers wait for a station to open up 

• How long drivers sit at a station after charging 

• How often drivers choose to charge up at a different location 

With an additional layer of insights, EV charging operators can get a more complete picture of business, leading to more confident, data-driven decisions.  

#3 Infrastructure planning

Opening a new EV charging site requires significant investment. On top of choosing an ideal location, securing permits, and setting up equipment, operators need to be careful when it comes to grid management. Unmanaged charging can put stress on the grid, which can potentially result in operational and reliability issues.  

#4 Consumer behavior

Consumer behavior is an important factor for all B2C businesses. However, for EV charging operators, it’s difficult to establish a benchmark around consumer behavior as it can vary widely from station to station – especially since the transition from gas-powered vehicles to EVs varies from state to state.   

How EV charging operators can leverage mobile driving data 

Arity attended MOVE America 2024, joining others who are passionate about solving mobility’s most pressing issues. As we already know, EVs are the future – but several roadblocks are holding the industry back.  

To help, Arity Solutions Engineer Anthony Johnson took the stage to demonstrate how EV charging operators can use mobile phone-based driving data, via Arity’s Road Traffic Analytics, to overcome challenges and the “EV infrastructure gap.”   

First, what is Road Traffic Analytics?   

Built from our massive dataset with a connection to more than 45M U.S. drivers, Arity’s Road Traffic Analytics solution provides a picture of traffic patterns to and around points of interest (POIs), delivered daily, weekly, or monthly.

Second, why is Road Traffic Analytics beneficial for EV charging operators?

Road Traffic Analytics empowers businesses to look at traffic volume, trip origination and destination, and loyalty trends for a current or potential site location. With our solution, an EV charging operator can look at specific points of interest (POIs) and garner insights like:  

• Count of trips that drove on roads adjacent to the POI – This could be valuable for planning future EV charging site locations, selecting one that has high visibility.  

• Count of trips that ended at the POI, along with trip origin and average time to destination – This could be helpful for determining the performance of current EV charging site locations. For instance, do drivers visit the location and then leave immediately? This could indicate a lack of available charging stations. These insights could also be beneficial when it comes to narrowing down future site locations. If there are two or three good options on the table, these inputs can help determine which one is the most convenient for future customers and their day-to-day driving patterns.  

Third, what does this look like in real life?  

In his presentation, Anthony walked the audience through an EV charging example – leveraging our driving data to assess eight different site locations in Austin, TX. These sites included: 

• One gas station 

• Three grocery store parking lots 

• Three shopping center parking lots 
• One school parking lot 

For each of these locations, he looked at:  

• Dwell time – i.e., the amount of time spent at a charging space – which can help us find a correlation between the type of location to the average time spent in charging stations.  

• Distance from the driver’s residence census block group, which can show us how many drivers charged at a location greater than five miles away.  

• Time of visit by hour of day, which can uncover peak times for visits.  
• Station availability, which can be estimated by looking at the total dwell time and the total number of charging spaces per location. This can help determine if there’s a need for additional charging locations nearby.   

Depending on the EV charging operator’s overall strategy, driving data-based assessments like these can help them more effectively 1. Plan for future sites and 2. Plan for which current sites to invest in.   

Ready to optimize EV infrastructure? Contact us to start a conversation around how Road Traffic Analytics can work for you.