Highly automated vehicles (HAVs) continue to dominate headlines, often with a narrative of how the technology is going to either alleviate or exacerbate all of our transportation challenges. However, transportation experts agree that like any technological innovation, the ultimate impact of impending “driverless” vehicles will have much more to do with when and where the technology is deployed than an inherent value or downside. Leveraging current data on travel patterns and conditions is essential to maximizing the significant upside of HAVs, while minimizing the downside.
In 2017, INRIX published a white paper at South by Southwest promoting the idea of data-driven deployment for HAVs and encouraged cities and vehicle operators to match testing and deployment of shared-use, electric HAVs with the regions and routes that make the most sense for these vehicles capabilities and designs. In that paper INRIX matched existing vehicle travel patterns in the top 50 U.S. cities with vehicles best suited for short distance, centralized, low-speed, high-volume trips. We furthermore looked in more detail at three cities – Austin, New York and San Francisco – and, using travel, parking and census data, identified corridors and regions within these cities where HAVs designed for this use case could have immediate impact. This report was not intended to tell cities or vehicle operators where HAVs should go, but rather to demonstrate the value of data-based evaluation to position this new technology to address identified transportation needs or challenges.
This week, at ITS World Congress in Copenhagen, INRIX released a new paper focused on HAV technology in trucks and including analysis of the UK and Germany in addition to the U.S. As with our previous paper at SXSW, we’ve looked to match the specific use case of a technology (in this case long-distance commercial trucking) with the travel patterns and conditions that will maximize benefits of the technology. In our last paper, we noted that individual policy priorities and commercial goals (congestion mitigation, mobility access to underserved populations, workforce movement, etc.) would dictate which data sets were applicable and with what weight. In this paper we’ve broken out two areas of focus (safety improvements and commercial benefit) to demonstrate the difference in route prioritization given what metrics are considered.
What we found is that, when evaluating the corridors in each country for commercial freight, those best suited to see safety benefits from HAV technology in commercial trucks (routes with high freight volumes and road conditions that are challenging for human drivers) vary from those where automated commercial trucks are the best business fit (long distance, high-volume, low-congestion routes that present relatively easy conditions and opportunity to deliver return on investment for HAV systems). However, in each country there are routes that present significant potential for both safety and commercial benefit. Using this sort of data-driven analysis to match specific HAV technology with designed use cases to achieve clearly articulated outcomes not only helps the public and private sectors identify initial testing and deployment areas that prioritize the goals of both parties, but also positions the technology for the greatest chance of success.
Read the full Automated Freight Corridor Assessment