UPDATE (Sept. 23, 2025): This story has been updated to correct Don MacKenzie’s title.

Public electric vehicle charging stations in America have . They’re notorious for breaking down, charging at a snail’s pace, refusing customer payment and leaving drivers stranded without juice. Advocates for electric vehicles, or EVs, worry that reliability concerns are hampering adoption at a critical moment in the campaign to reduce greenhouse gas emissions, but data on the topic is limited.

To address this problem, researchers at the 91̽�� designed a survey to tease out exactly how much a car owner’s perception of public charging reliability influences their willingness to buy their first EV. The team created a series of hypothetical scenarios to study the factors that might nudge a skeptical shopper towards an EV over a gasoline-powered car, including vehicle and gas prices, driving range and public charging access.

The results were dramatic. Participants with a negative view of public charging were much less likely to choose an EV than those with a moderate view. It took some serious hypothetical improvements to offset those negative perceptions: The EV needed to be discounted 30%, have 366 extra miles of range or there needed to be 30,000 additional public charging stations.

“No one knew how much charger reliability was coloring the decisions of prospective EV buyers,” said senior author , a 91̽��professor of civil and environmental engineering. “I was not at all surprised by the direction of the response. What surprised me was the size. These were monster results. This is a warning for the whole industry.”

The June 28 in Transport Policy.

The results come at a tenuous time for EV adoption in America. The market continues to grow, but political factors like are complicating sales outlooks. The federal government is also to phase out gas car sales, which could threaten similar efforts in Washington and several other states.

The state of public charging isn’t inspiring confidence in buyers, either. have shown with public networks. There are , and home charging is an option for some drivers, but the threat of slow and flaky public chargers remains a powerful deterrent for anyone venturing outside their “home range.”

“We know there’s a lot of range anxiety out there,” said lead author , a 91̽��doctoral student of civil and environmental engineering. “EV owners often tolerate charging problems, while newcomers are less aware of the hurdles. If trust erodes, adoption could slow.”

The team found it tricky to measure the link between station reliability and buyer behavior because there weren’t obvious real-world groups to compare. Tesla’s stations get consistently higher marks than other networks, but Tesla cars and their owners are too different in other ways to make for a useful comparison. Simply asking people for their thoughts about charging may produce answers that are colored by their overall feelings about EVs.

Instead, the researchers turned to hypothetical scenarios. They recruited roughly 1,500 participants who had never owned an EV and surveyed them in three groups, asking the first to picture a world where public charging is a mess, the second to imagine a charging utopia and the third to simply give their preexisting opinions about charging.

Each group then went “shopping.” Each round of the survey, participants chose between an EV and a comparable gas-powered car. The researchers tweaked variables such as vehicle cost, gas prices and range, and trends emerged over several rounds.

Participants with a negative view of public charging demanded strikingly large concessions before choosing an EV. In some cases, the adjustment needed was nonsensically large.

“People wanted a 366-mile increase in range before they bought an EV,” MacKenzie said. “Lots of EVs don’t even have a 366-mile range today. That’s obviously not a practical demand. But it illustrates the strength of this effect.”

There were other surprises in the data, too.

“The results were basically the same for people who have access to home charging and people who don’t,” Singh said. “So even if they wouldn’t actually have to rely on the charging network, respondents were still concerned about reliability.”

As the auto industry works to bring EVs into the mainstream, these findings are both a warning and an invitation for further study. Little is known, Singh said, about what specific improvements would have the greatest impact on public charging perception. Asking the right questions could help stakeholders throughout the industry figure out where to invest.

“What are the specific factors that would convince skeptics?” she said. “Does a station need to be online 90% of the time to improve a user’s perception? Or 95%? Or 99%? Or would improving the point of sale system help more? Where do you put your dollars to have the greatest effect on public perception?”

What’s clear, MacKenzie said, is that reliability must be prioritized as charging networks expand.

“This is the Achilles’ heel right now for EVs,” he said. “If we push the broader market towards EVs, or if it grows on its own before we can fix this problem, it’s really bad news for continued growth. I think it could engender a real backlash. It only takes one bad experience to lose a customer. That’s a big danger for EV adoption.”

This research was funded by the Joint Office of Energy and Transportation.

, an affiliate assistant professor at 91̽��Tacoma, is a co-author on this paper.

For more information, contact Singh at rs49742@uw.edu.

It’s something every bus rider fears: Their bus is running late.

Under typical conditions, Seattle has some of the most congested traffic in the nation. To prepare for when things return to normal, 91̽�� researchers are carrying out a research project to investigate reasons for these delays.

While a bus could be late for many reasons, one holdup is that it has to compete with other vehicles — personal vehicles, ride-hailing cars, delivery trucks, etc. — that might be parked at the bus stop or double-parked in a travel lane.

People riding the bus have the prime seat to witness potential events like this. That’s why 91̽��researchers are inviting the public to share their experiences on their regular commutes in . This project hopes to identify areas in Seattle and Bellevue where buses are frequently delayed by other vehicles and categorize the most common types of interference. From there, the researchers plan to develop potential solutions for these issues.

“Delays slow transit down and make it less attractive relative to driving,” said co-lead researcher , a 91̽��associate professor of civil and environmental engineering who also leads the Sustainable Transportation Lab. “Some delays are necessary — such as waiting for passengers to board or pausing to make sure it’s safe to reenter traffic. But we suspect there may be cases where interference is happening. Are there cars or delivery trucks parked in the bus lane? Is there a cyclist, who for lack of a better space to ride, is in the bus lane? These are the kind of things we want to know about.”

The survey will ask people to fill out a few questions about where and when they notice problems and what types of interference they see.

Using the results from the survey and input from the project’s stakeholders, the team will identify up to 10 busy transit corridors — sections of a public transit route that may include multiple stops — to study in more detail. The group plans to develop an app so that research assistants can ride buses in the selected corridors to collect data on bus operation and mark cases of interference as they come up. Depending on the results, researchers will develop and test potential solutions.

“We’re really excited about learning what the problems are and hearing what the public has to say,” said co-lead researcher , a research engineer at the 91̽��. “This is an opportunity for us to engage the public in our work and for them to influence our research to solve long-term transportation problems in their cities. We recognize that many people are working from home right now, but we would value any comments about their usual commutes.”

This research is sponsored by Challenge Seattle, Amazon, Uber, Sound Transit, King County Metro, Bellevue Transportation Department and the Seattle Department of Transportation with support from the 91̽��.

For more information, contact Ranjbari at ranjbari@uw.edu.

In October 2014, Seattle launched Pronto, a docked bike-share program. But Pronto had problems shifting into a higher gear, and the city ended the program in 2017, making Seattle one of the few cities in the world to shut down a modern public bike sharing system.

Then, four months later, Seattle became the first city in the U.S. to allow for dockless bike sharing, a system where bikes don’t have to be picked up or returned to specific docking stations.

91̽�� transportation researchers took this opportunity to look into why Pronto failed while dockless bike sharing has been so successful. The researchers used multiple approaches to consider 11 possible factors behind the difference in bike sharing outcomes: They surveyed Seattle bike riders, read press reports, analyzed ridership data and interviewed experts involved in both Pronto and dockless bike sharing in Seattle.

The team Sept. 26 in the journal Transportation Research Part A: Policy and Practice.

“We wanted to know if the problems Pronto had were intrinsic to Seattle, like our wet weather, our hills or our helmet laws. Or if they reflected decisions made by the bike sharing system designers — like the price of a ride or bike location and density across the city,” said senior author , a 91̽��associate professor of civil and environmental engineering who also leads the leads the UW’s Sustainable Transportation Lab.

Some of the findings include:

• Pronto bikes weren’t always in areas that people wanted to go. Many neighborhoods that have high dockless ridership — Alki Point, Ballard, Wallingford, etc. — did not have Pronto docking stations.

• Pronto had a smaller number of bikes per square mile. It launched with 500 bikes — 50 stations — spread over 5 square miles. Dockless bike sharing launched with 1,000 bikes spread over Seattle’s 84 square miles. By the end of the first year, there were 9,000 dockless bikes, owned by three private companies, across the city.
• Pronto was perceived as “moderately difficult” to use, whereas dockless bikes were perceived as easy to use. For example, Pronto users had to go through multiple steps at the docking station — selecting a bike, renting a helmet, paying by credit card — to check out a bike whereas dockless bike users open their app, scan a QR code on a bike and start their trip.
• Pronto was more expensive — $8 per day with no per-ride option — compared to dockless bikes, at about $1 per ride.

To the team, the success of the dockless bike-share programs isn’t necessarily due to the fact that they are dockless, but rather the fact that these bikes had a higher density throughout the city and were more accessible for new users.

Dockless bikes, however, do have some advantages over their docked cousins: They can be dropped off anywhere, their set-up cost is likely to be about 80% cheaper than docked bikes, and companies can move them around the city based on how people are using them.

“These results can help service providers and cities better design and regulate bike- or scooter-sharing systems to increase ridership,” MacKenzie said. “One of the main implications from our study is that service providers should deploy at scale. A system that covers a large area and has plenty of bikes — or stations — is a system that will provide the greatest utility to travelers, and will achieve the highest ridership. For jurisdictions that aren’t ready to commit to a permanent, large-scale deployment, dockless may have an advantage for a temporary deployment because it doesn’t require costly investments in docks. Finally, policymakers should ensure that shared bikes or scooters can be picked up and dropped off in the places people want to travel.”

Luke Peters, who completed this research while a master’s student in the civil and environmental engineering department, is a co-author on this paper. This research was funded by a fellowship from the .

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For more information, contact MacKenzie at dwhm@uw.edu.

Many Americans use a ride-hailing service — like Uber or Lyft — to get to and from work. It provides the privacy of riding in a personal car and the convenience of catching up on emails or social media during traffic jams. In the future, self-driving vehicles could provide the same service, except without a human driver.

But would consumers be willing to ride in a driverless car?

Researchers at the 91̽�� studied how Americans’ perceived cost of commute time changes depending on who’s driving. Through a survey, the team found that people considered a ride-hailing service at least 13% “less expensive,” in terms of time, compared to driving themselves. If the researchers told people the ride-hailing service was driverless, however, then the cost of travel time increased to 15% more than driving a personal car, suggesting that at least for now, people would rather drive themselves than have an autonomous vehicle drive them.

The team Aug. 6 in the journal .

“The idea here is that ‘time is money,’ so the overall cost of driving includes both the direct financial costs and the monetary equivalent of time spent traveling,” said senior author , a 91̽��associate professor of civil and environmental engineering who also leads the UW’s Sustainable Transportation Lab. “The average person in our sample would find riding in a driverless car to be more burdensome than driving themselves. This highlights the risks of making forecasts based on how people say they would respond to driverless cars today.”

The team set up a survey that asked people across the continental U.S. to select between a personal car or a ride-hailing service for a 15-mile commute trip. Half the 502 respondents were told that the ride-hailing service was driverless.

The researchers converted the responses to a score of how much respondents deemed that trip would cost per hour.

“If someone values their trip time at $15 per hour, that means they dislike an hour spent traveling as much as they dislike giving up $15,” said co-author , a research engineer at the UW’s . “So a lower number means that the time spent traveling for that trip is less burdensome.”

On average, respondents preferred a ride-hailing service over driving themselves: Ride-hailing services scored at $21 an hour and driving scored $25 an hour. In addition, if the researchers reminded respondents they could multitask during a ride-hailing service ride, their perceived cost of travel time decreased even more to $13 per hour.

Technically a ride-hailing service should be equally as convenient regardless of whether a human or an autonomous car is driving, but respondents disagreed. Driverless ride-hailing services scored at $28 an hour.

These results make sense, according to the team. Driverless cars aren’t commercially available yet, so people are not familiar with them or may be leery of the technology.

“We believe that our respondents are telling us that if they were riding in an automated vehicle today, they would be sufficiently stressed out by the experience that it would be worse than driving themselves,” MacKenzie said. “This is a reminder that automated vehicles will need to offer benefits to consumers before people will adopt them. To a first approximation, a ride-hailing service with driverless cars would need to offer services at a price at least $7 per hour less than human-driven cars, to make the driverless service more attractive.”

Jingya Gao, an analyst at Amazon China who completed this research as a student in a dual master’s program at the 91̽��and Tongji University, is also a co-author on this paper. This research was funded by the U.S.-China Clean Energy Research Center.

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For more information, contact MacKenzie at dwhm@uw.edu.

While many Puget Sound residents have to choose between taking public transit or personal vehicles to work, Microsoft and Seattle Children’s Hospital employees have an additional option: private commuter buses.

Last year, King County Metro and the Seattle Department of Transportation that allowed these shuttles to . But some residents are concerned that sharing stops with private shuttles could make public transit less reliable.

Now from researchers at the 91̽�� suggests that public buses are unaffected by private shuttles most of the time. The study, which will appear in the print edition of the this fall, examined how well public buses adhered to their schedules both before and during the pilot period.

“There’s this huge symbolic meaning that these private buses have, and their potential impact on public transit would basically be salt in the wound,” said , a co-author of the study and an assistant professor in civil and environmental engineering. “Your neighborhood is gentrified, your friends and neighbors have been forced to move out and now your bus is going to be late? But we found that, by and large, the buses aren’t running behind.”

To determine if the shuttles negatively impacted public buses’ reliability, MacKenzie and civil and environmental engineering doctoral student partnered with , a company that acquires and cleans up real-time performance data from trackers on public transit.

Lewis and MacKenzie used data for the six weeks leading up to the pilot start date on April 24, 2017, and then six weeks afterward for nine stops included in the trial run. The stops spanned the city, including stops in West Seattle, lower Queen Anne, Capitol Hill, and northeast Seattle. The team compared data from those stops to nearby control stops that were not part of the program, which was set to run for six months.

“We asked questions like: To what degree are buses arriving late? And does that change after the pilot starts?” said MacKenzie, who also leads the at the UW. “We’re interested in what happens in the worst-case scenarios before and after to see if the worst case gets worse. If buses start arriving 10 minutes late, that’s really problematic.”

On average, bus reliability stayed the same for all of the stops combined. But when the researchers looked at each stop individually, one stop on Sand Point Way in northeast Seattle was affected. The bus that stops there, the 75, was more likely to arrive two to three minutes later after the pilot program started.

It’s hard to tell exactly what’s happening at that stop without a more in-depth look, the researchers said.

“We wanted to do a quick study with data that’s available to see if there was an overall problem with the pilot program,” said Lewis, who is the corresponding author on the paper. “So rather than the city having to spend time and resources going out and having someone sit and watch every single stop, now they can follow up with only the problem stops.”

From a transportation standpoint, Seattle could benefit from having these private shuttles use public transit stops, the researchers said.

“These companies pay a monthly fee to stop, pick up and drop off passengers there,” said Lewis. “So they have the potential to add a new source of income that goes into improving the transit system.”

The team is optimistic that the results show that having access to real-time data like this can answer important questions that will help influence public policy.

“This is just one example of how open data can help us understand the impacts of these services, but there are certainly more questions,” said Lewis. “If we want to get at bigger questions, such as whether these shuttles are causing gentrification, then we need access to more available data.”

, founder and CEO of , was the team’s partner at Swiftly and is also a co-author on this paper.

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For more information, contact MacKenzie at: dwhm@uw.edu.

Your wait time for an Uber ride in Seattle is shorter if you are in a lower income neighborhood.

Alternatively, wait times are longer for an Uber in wealthier neighborhoods, according to a new 91̽�� that compares Uber service across different neighborhoods in the Seattle region. The paper was published in the October issue the .

“We found that all else being equal, lower income areas have a shorter average waiting time for an UberX. That was completely not what we expected to find,” said senior author , a 91̽��assistant professor of civil and environmental engineering. “That said, this is only one way of measuring whether Transportation Network Companies (TNCs) are providing equitable access for all their customers.”

MacKenzie is also collaborating with researchers from MIT and Stanford University on further studies to capture and quantify other dimensions of racial and gender discrimination in TNC operations across different cities.

Ride-sourcing companies like Uber and its competitor Lyft broke into the urban transportation market about four years ago and have grown in popularity among customers seeking point-to-point rides on demand, often for less money than traditional taxicabs. Uber is by far the largest such company, with more than 327,000 drivers in the U.S. in September 2015.

Given Uber’s rapid growth, the 91̽��researchers wanted to test whether the company offered equitable service across neighborhoods with different socioeconomic backgrounds. The researchers looked at average predicted wait times for an UberX vehicle, the most popular type of service offered by the ride-sourcing company. That’s the estimated time given when a customer opens the Uber smartphone app and inputs the desired pickup location.

Using U.S. census data, they calculated the average income, percentage of minorities, population density and employment density for each Seattle neighborhood served by Uber.

Then, over a two-month period in summer 2015, they collected estimated wait times for an UberX vehicle throughout each neighborhood, resulting in a dataset of nearly 1 million observations across the Seattle region.

Not surprisingly, denser neighborhoods such as downtown Seattle had the lowest average wait time of less than four minutes, and most neighborhoods region-wide had wait times of less than 10 minutes. It makes sense that areas with denser housing and offices would demand more Uber drivers, and thus reduce wait times for everyone in those districts.

But even after adjusting for the effects of population and employment density, neighborhoods with lower average income still experienced better service from UberX, as measured by wait times. Each $10,000 increase in the average income of a neighborhood was associated with a 2.3 percent increase in the expected waiting time for an UberX vehicle.

The study also determined that racially diverse neighborhoods with more minorities saw longer wait times at some times of the day and shorter waits at others — with an average effect of close to zero.

“What we’ve found is a fairly equitable distribution of service based on income and percentage of minorities in census block groups in Seattle,” said co-author , a former 91̽��graduate student who is now a transportation engineer at Clark Dietz in Illinois.

“The bottom line is, this is good news,” MacKenzie said. “We know that there are many other ways inequities and discrimination can arise, and the requirement for a smartphone and electronic payment can also present structural barriers to using these services for those with low incomes or poor credit. But at least geographically, adequate access to TNC services is not necessarily restricted just to areas that are ‘white and wealthy.’”

The researchers only analyzed patterns in Seattle because of funding and time constraints. These methods, however, could be applied to other U.S. cities where Uber operates to see if the availability of rides is equitable in other parts of the country, the researchers said.

The research was funded by the 91̽��Royalty Research Fund and the UW-based Pacific Northwest Transportation Consortium.

For more information, contact MacKenzie at dwhm@uw.edu or 206-685-7198.

Driverless vehicles could intensify car use — reducing or even eliminating promised energy savings and environmental benefits, a new study co-authored by a 91̽�� engineer finds.

Development of autonomous driving systems has accelerated rapidly since the unveiling of Google’s driverless car in 2012, and energy efficiency due to improved traffic flow has been touted as one of the technology’s key advantages.

However, from the University of Leeds, the 91̽��and Oak Ridge National Laboratory published in the journal says the actual impact may be complicated by how the technology changes our relationship with our cars.

While vehicle automation undoubtedly offers some efficiency benefits, if people can work, relax and even hold meetings in their cars, they may drive more. That has the potential to erode the energy and environmental benefits of self-driving cars, the researchers found.

“There is a lot of hype around self-driving cars, much of it somewhat utopian in nature. But there are likely to be positives and negatives,” said co-author , a 91̽��assistant professor of civil and environmental engineering. “By taking a clear-eyed view, we can design and implement policies to maximize the benefits and minimize the downsides of automated vehicles.”

The study analyzes self-driving technology combined with data on car and truck use, driver licenses and vehicle running costs to model the impact on energy demand of various levels of automation on U.S. roads by 2050.

The analysis identifies several efficiency benefits from self-driving cars and predicts ranges of likely energy impacts, depending on the extent of adoption of the technology and other factors:

• More efficient computer-directed driving styles (0 to 20 percent reduction in energy use)
• Improved traffic flow and reduced jams because of coordination between vehicles (0 to 4 percent reduction)
• “Platooning” of automated vehicles driving very close together to create aerodynamic savings (4 to 25 percent reduction)
• Reduced crash risks mean that cars can be lighter (5 to 23 percent reduction)
• Less emphasis from car buyers on high performance (5 to 23 percent reduction)

But the study also predicts that the very attractiveness of self-driving technology could reduce or even outweigh the efficiency gains.

It estimates a 5 to 60 percent increase in car energy consumption due to people choosing to use highly automated cars in situations where they would have previously taken alternative transport (e.g., trains or planes).

“When you make a decision about transport, you don’t just think about the out-of-pocket costs of the train ticket or the car’s petrol; you also take into account non-financial costs,” said lead author , associate professor in the University of Leeds’ Faculty of Engineering and a research group leader in the University’s Institute for Transport Studies.

“Car owners might choose to travel by train to relatively distant business meetings because the train allows them to work and relax. The need to drive is part of the cost of choosing the car, just as standing on a cold platform is part of the cost of the train. If you can relax in your car as it safely drives itself to a meeting in another city, that changes the whole equation,” Wadud said

The study also predicts that people who currently find it difficult or impossible to drive, such as the elderly or some people with disabilities, will have increased access to road transport with the advent of the new systems, resulting in an estimated 2 to 10 percent increase in road energy use for personal travel.

Possible higher speed limits because of the improved safety of autonomous cars (7 to 22 percent) and demand for heavy extra equipment in driverless cars such as TV screens and computers (0 to 11 percent) might also tend to reduce efficiency savings.

A major uncertainty is the effect of autonomous driving technology on car-sharing. The technology could allow vehicles to move independently between different users and therefore not only increase sharing, but possibly also make it easier for users to match trip types to car types. Instead of using one car for all journeys, users might be able to use a shared, smaller car for a commute and a larger one for family leisure trips, for example. The authors say these factors could reduce energy consumption by 21 to 45 percent.

The study says many of the energy benefits of self-driving technology could be delivered by systems that still require the human driver to pay attention to the road and therefore do not radically alter transport decision-making.

The authors suggest that policymakers could focus less on accelerating the introduction of complete automation and more on promoting aspects of automation with positive environmental outcomes. For instance, regulators could encourage standardisation of car networking protocols to allow vehicles to communicate with each other on the road and therefore deliver benefits such as “platooning.”

The researchers warn that, if a high level of automation becomes the norm, it may be necessary to financially intervene in transport decisions. For example, self-driving cars’ navigation and communication systems could be used as a basis for road pricing schemes to control congestion and reduce overall travel demand.

“Vehicle automation presents a paradox: it may encourage people to travel much more, but at the same time it makes it practical to implement tools such as road pricing that can offset those effects,” MacKenzie said. “Ultimately, however, it’s up to government to set appropriate policies to manage these impacts.”

Co-authors include , distinguished research scientist at Oak Ridge National Laboratory.

For more information, contact MacKenzie at dwhm@uw.edu or 206-685-7198 or Wadud through Montana Wright, Press Office, University of Leeds; +44 113 343 8373 or M.wright1@leeds.ac.uk.

This article was adapted from a University of Leeds press release.

Its relies — in part — on enacted by the Obama administration that aim to increase the fuel efficiency of cars and light trucks to the equivalent of 54 miles per gallon by 2025.

New research from the 91̽�� and the Massachusetts Institute of Technology shows that automakers won’t meet those goals if they continue on the path they have followed for the last two decades. But when faced with another turning point in the nation’s energy future, the industry was previously able to deliver sufficient levels of innovation and ingenuity.

Fuel efficiency gains enabled by new technologies developed after the 1970s energy crisis outpace what would be necessary to meet the newer federal regulations, according to a published last month in a special issue of the journal on clean transport.

The 91̽��research quantifies fuel efficiency improvements from 1975 to 2009 by combining econometric and engineering analysis to provide a new and more complete accounting of those gains.

“We wanted to understand how ambitious and challenging the newer fuel economy standards are, compared to what the automotive industry has been able to deliver historically,” said lead author , 91̽��assistant professor of civil and environmental engineering.

“It’s within the realm of what we’ve done before, just not recently. It’s asking for a return to the rate of innovation the industry was able to deliver in the 1970s and 1980s. But it’s not beyond that,” MacKenzie said.

Since 1975, the U.S. has sought to reduce energy consumption through — or CAFE — standards that mandate how fuel efficient new cars and trucks must be. To meet more aggressive standards enacted by the Obama administration without sacrificing capabilities that consumers have come to expect, technology must improve quickly enough to reduce fuel consumption by 4.3 percent per year from 2011 to 2025.

The new analysis found that from 1975 to 1990, new technologies and manufacturing advances could have reduced the per-mile fuel consumption of new cars in the U.S. by 5 percent each year.

In reality, they only improved by 4 percent because automakers also focused on improving acceleration times, adding new features and making other tradeoffs that cause cars to guzzle more gas.

From 1990 to 2009, the rate of technological innovation slowed considerably. Advances could have improved per-mile fuel consumption by 2.1 percent per year, but the actual gains were closer to 0.7 percent per year.

That’s because automakers also added features that reduce fuel efficiency: larger size, radically faster zero to 60 acceleration times and features like power windows and soundproofing that add weight to a car.

The 91̽��analysis did not investigate why the shift occurred, but high gas prices and increases in federal CAFE standards throughout the 1970s and 1980s provided both a market and a regulatory incentive for auto manufacturers to reduce fuel consumption.

Previous studies that attempted to quantify these historic fuel savings haven’t fully captured how engineering and manufacturing advances during that time period — such as front wheel drive, the use of lighter materials and modern construction techniques that shave weight — contributed to fuel efficiency gains, MacKenzie said.

“This is not the first paper that’s tried to answer this question, but other studies have only measured a portion of the improvements,” MacKenzie said. “Our contribution is that we feel like we have a more comprehensive measure of efficiency improvement, and we did that by combining statistical and economic approaches that have been used in the past with engineering analysis.”

MacKenzie said there are newer technologies available today or on the horizon — from lightweight materials and composites to more efficient transmission and engine technologies — that the industry can exploit to achieve further gains.

That may seem challenging, he said, but making the overwhelming majority of cars front wheel drive probably didn’t seem trivial in the 1970s.

“In retrospect, these things look like low-hanging fruit, but that doesn’t mean they were easy,” he said. “What this paper shows is that the business-as-usual improvements of recent years are not going to be adequate. These standards through 2025 are not asking the auto industry to keep doing what they’re doing but focus a little more on fuel economy. But it’s clearly within the range of what’s been done before.”

The research was funded by the MIT Energy Initiative, Roos (1944) Fund for Alternative Energy, Eni, Chevron and CONCAWE.

The paper was co-authored by , professor of mechanical engineering at the Massachusetts Institute of Technology.

For more information, contact MacKenzie at dwhm@uw.edu or (206) 685-7198.