Using Big Data for Vehicle Miles Traveled (VMT) Evaluations
At StreetLight Data, we’re excited that Vehicle Miles Traveled (VMT) is gaining traction as a transportation performance measure. In this blog post, we explore the reasons behind increasing interest in VMT and discuss why StreetLight InSight®, our easy-to-use web app for transportation Metrics, is a great tool for calculating and exploring VMT. If you’re not familiar with StreetLight InSight, click here to learn more by watching our demo videos.
Why All the Interest in VMT?
Performance is More than Traffic Flow
Traffic flow, often measured as Level of Service (LOS), is traditionally transportation’s most important performance measure. In practice, relying on traffic flow to evaluate performance prioritizes projects that reduce congestion and delays by increasing capacity (i.e.: roadway and intersection expansions). Justifying and funding measures that promote bicycle, pedestrian, carpooling, fewer trips, and transit usage can be challenging because they do not necessarily improve LOS (see Figure 1 below). However, these types of multimodal projects are also considered keys to healthy, livable, and sustainable neighborhoods.
California’s recent proposed guidelines to use VMT as the “primary metric for assessing transportation impact” are designed to overcome this preference for capacity expansion. Some stakeholders have applauded the change because VMT reduction measures can actually provide more permanent congestion relief, reduce greenhouse and criteria gas emissions, and decrease wear and tear on roadways.
Figure 1: Protected bike lanes like this one in Vancouver, Canada can help reduce VMT – or in this case, Kilometers Traveled (KMT). Photo Source: Paul Krueger
The Hunt for New Revenue Streams
Interest in VMT is also spiking as policymakers explore mileage-based fees and taxes to replace lost revenue from gasoline tax shortfalls. Increased fuel efficiencies and the growing popularity of electric cars are penalizing smaller states with high pass-through traffic in particular. However, policymakers cannot project the revenue that mileage-based fees and taxes could generate without an effective way to quantify regional VMT.
Using Big Data to Calculate VMT
The following chart shows a simple VMT analysis created with Big Data via StreetLight InSight. A new lane was added to a highway. For the following months, we compared the VMT for trips affiliated with that segment of highway to the same period in the prior year. We saw an expansion in VMT from personal vehicles using that highway. This was due to a mix of additional traffic volume and longer trips. Commercial traffic, with more fixed demand, did not react as strongly.
Despite its attractiveness as a performance measure, VMT is estimated using methods (for example, petroleum use and outdated surveys) that are too general, and that also make less sense in today’s changing vehicle technology mix. To really understand VMT and use it as a guide for policy decisions, we need more nuance and precision. We need to be able to assess the causes behind changes in VMT, and to see how patterns change by geography, income, urban form, weather and more. That’s what Big Data enables.
StreetLight InSight® and VMT
StreetLight InSight, our easy-to-use web application, takes Big Data and transforms it into Travel Metrics that describe mobility behavior accurately, precisely, and comprehensively. The key StreetLight InSight Metrics for VMT are:
- The StreetLight InSight Trip Index (which gives a normalized volume of trips) for trips between, and through Zones and corridors.
- The average trip length and trip length distributions (in miles) for trips that begin, end, or pass through specified analysis Zones
- The StreetLight InSight Trip Index at one or more calibration locations, where you also have accurate real-world volume counts (e.g.: from a loop counter). This calibration data will allow you to expand the Trip Index to an estimated total VMT.
These Metrics can be easily combined to calculate and evaluate VMT trends. For more granular analyses, Metrics can also be segmented by:
- Times of day, types of day, and times of year
- Types of vehicle, including personal and heavy- and medium-duty commercial
- Types of trip, including trips to residential and commercial districts
- Demographic groups, including age, income level, race, and education
Thanks to our innovative algorithmic techniques, StreetLight InSight makes using Big Data for VMT simpler, more efficient, and more accurate than using modeled projections or “raw” Big Data. Our techniques include:
The VMT Questions You Can Answer with StreetLight InSight
With StreetLight InSight Metrics, transportation experts, planners, and policymakers are empowered to answer complex questions, such as:
- How does regional VMT change over time?
- How does a particular area, land use, or development contribute to regional VMT?
- How do short trips (personal and commercial) contribute to VMT?
- How much of my region’s VMT is contributed by internal trips vs. vehicles that are just passing through? (For a great example of this, see Figure 2 below.)
- What was the impact of this project on both VMT and traffic flow?
Figure 2: The chart above shows, as a share of total VMT from personal vehicles in three counties in Colorado, what percent of the VMT is related to trips that are internal, in/out bound, or just passing through on the way to another county. In comparison to Adams County and Alamosa County, Adams County’s roads and citizens bear far more wear and tear, noise, and emissions from vehicles that aren’t stopping to do anything in Adams County.
Interested in using StreetLight InSight to calculate VMT? Request a consultation with one of our expert consultants to get started.
INRIX, a StreetLight Data partner, provided the GPS data for the above StreetLight InSight VMT analyses. Click here to read more about our GPS data sources.