How to Make the Bipartisan Infrastructure Framework Work Better

How to Make the Bipartisan Infrastructure Framework Work Better

Streetlight Data applauds the Bipartisan Infrastructure Framework as a once-in-a generation opportunity to transform our nation’s infrastructure and address many of the most pressing challenges of our time. Success of this bold initiative will move our communities toward a more sustainable and equitable future, make our surface and physical infrastructure safer, and prioritize innovation so we can continue to adapt to the challenges of tomorrow.

As Congress deliberates an investment at a scale we haven’t seen since World War II, we urge lawmakers to prioritize accountability of taxpayer dollars through proven methods such as before-and-after studies. Taking this important step will ensure that this bold legislation led by President Biden meets his commitment to “address the climate crisis, grow the economy sustainably and equitably for decades to come.” 

Please click here to learn more about our approach for before-and-after studies for transportation infrastructure, and to download our policy paper.Streetlight Data applauds the Bipartisan Infrastructure Framework as a once-in-a generation opportunity to transform our nation’s infrastructure and address many of the most pressing challenges of our time. Success of this bold initiative will move our communities toward a more sustainable and equitable future, make our surface and physical infrastructure safer, and prioritize innovation so we can continue to adapt to the challenges of tomorrow.

As Congress deliberates an investment at a scale we haven’t seen since World War II, we urge lawmakers to prioritize accountability of taxpayer dollars through proven methods such as before-and-after studies. Taking this important step will ensure that this bold legislation led by President Biden meets his commitment to “address the climate crisis, grow the economy sustainably and equitably for decades to come.: 

Please click here to learn more about our approach for before-and-after studies for transportation infrastructure, and to download our policy paper.

How We Can Ensure Transportation Equity and Emissions Reduction for Infrastructure Bill Projects

How We Can Ensure Transportation Equity and Emissions Reduction for Infrastructure Bill Projects

The Biden administration’s ambitious American Jobs Act proposes to spend $2 trillion in infrastructure improvements — including many transportation investments. But before the federal government starts awarding grant money, we must guarantee that our tax dollars achieve their intended goal of fostering equity and reducing greenhouse gas emissions. 

How do we do that? By applying comprehensive transportation data to measure actual before-and-after results. The Biden administration’s ambitious American Jobs Act proposes to spend $2 billion in infrastructure improvements — including many transportation investments. But before the federal government starts awarding grant money, we must guarantee that our tax dollars achieve their intended goal of fostering equity and reducing greenhouse gas emissions. 

How do we do that? By applying comprehensive transportation data to measure actual before-and-after results.

Two simple requirements can make this happen: 

1. Assess all project grant applications by their stated predicted impact on climate and equity (and anything else the agency cares about)
2. Create incentives for agencies that continually measure if they achieved their goal, and adapt if they don’t

StreetLight Data remains a strong advocate for gathering the most comprehensive data possible to measure, monitor, and update transportation projects. That philosophy is what our company was founded on, and we urge the current administration to put it into practice to ensure the Infrastructure Bill benefits all Americans. 

Read our policy paper to learn more

Two simple requirements can make this happen: 

1. Assess all project grant applications by their stated predicted impact on climate and equity (and anything else the agency cares about)
2. Create incentives for agencies that continually measure if they achieved their goal, and adapt if they don’t

StreetLight Data remains a strong advocate for gathering the most comprehensive data possible to measure, monitor, and update transportation projects. That philosophy is what our company was founded on, and we urge the current administration to put it into practice to ensure the Infrastructure Bill benefits all Americans. 

Read the policy paper to learn more

Need Ride-Hailing Services Intel? Big Data Has That.

Need Ride-Hailing Services Intel? Big Data Has That.

The ride-hailing revolution launched by Uber and Lyft has expanded to embrace shuttles, autonomous vehicles, and micro-mobility options like scooters and bike share.

As ride-hailing services expand, car ownership is decreasing, putting pressure on original equipment manufacturers (OEM) in the automotive industry. Many transportation and mobility companies are working with us to explore business options in this emerging industry.

Planning a ride-hailing business requires researching the target market to understand the best mobility options with strong business potential, and accurately forecasting revenue. But how can a company do that in a business where no data about core patterns exists outside of a few dominant companies who don’t data share? Big Data to the rescue.

The Data Behind Ride-Hailing Services

What companies entering this new field want is granularity, with accurate data to flow into a model for predictive analysis. Then they can simulate the best business outcomes and opportunities.

For example, key topics for planning a ride-hailing service include:

1. Macro considerations: If we start a ride-hailing service, should we launch in Orlando or Dallas?
2. Micro considerations: Which neighborhood and routes should we deploy in first? Where should we build assets (such as charging parking garages for a fleet of autonomous cars)? Where should we make costly investments such as HD mapping, and work to get city permissions for test routes? What size vehicle should we manufacture – how many seats, how much cargo space, etc?

StreetLight Data has been working with mobility companies to help answer these questions. With our metrics on ride-hailing service movements, our customers are able to build complex demand models, and help identify opportunities for related businesses.

Available Data and How to Get It

StreetLight is the only reliable, geospatially accurate source of ride-hailing data and non-ride hailing transportation all in one place. In fact, even Uber and Lyft don’t have good data on non-ride-hailing transportation, such as people traveling in their own cars, riding their own bikes, or with other transportation operators. Complete data on all modes is necessary to understand current demand and latent (unsatisfied) demand for new transportation offerings.

How can StreetLight estimate this closely guarded ride-hailing service behavior? Essentially, we can analyze how the movement patterns of devices belonging to ride-hailing drivers differ from other drivers. The patterns are clear: Ride-hailing service vehicles travel more miles per day, via irregular routes. Armed with detailed data, we then use machine learning and probabilistic modeling to apply these “gig driving” patterns/characteristics to our existing database of millions and millions of Location Based Data (LBS) from mobile devices.

As a result, we can offer an unprecedented array of analytics about ride-hailing behavior, including origins and destinations, the volume of ride-hail service drivers on different route segments, the neighborhoods where ride hailing is more or less concentrated, and more.

How Companies Use Ride-Hailing Data

Early adopters of our data have run analyses to identify various ride-hailing service patterns such as: Where are the busiest ride hailing routes? How does that change according to time of day and type of day (i.e. weekdays vs weekends)? Where are common clusters of origins and destinations? Are there common “spine” routes that ride-hailing service drivers take, even if the origins and destinations are different?

We not only have data on ride-hailing driving, we can also illuminate, with high spatial granularity/specificity, the difference between ride-hailing services and normal driving. That allows us to spot trends. One obvious example is with nightlife hot spots: we know that ride hailing as a percentage of total driving increases dramatically on Friday and Saturday late nights.

Our customers feed data into their forecast models that use their own proprietary info and assumptions (such as their customers’ willingness to pay, vehicle costs, deployment costs, etc). By layering their business intelligence with our metrics about demand (current and latent) for their future services, they can reveal what investments would be the most lucrative.

For example, consider testing the hypothesis of whether a six-seater shared-ride AV shuttle is more cost-effective to operate than a two-seater, non-shared shuttle. StreetLight’s metrics can show where in a city six people are most likely to share a trip to and from, along with which route(s) to take where people already have proven a willingness to use ride-hailing services.

Looking for granular data about fast-evolving travel behaviors? Don’t expect to get that anytime soon from ride-hailing service companies. Fortunately, Big Data has the answers, and can help companies make informed strategic decisions.

How Does Gig Driving Interact with Congestion? A Road-by-road Analysis.

How Does Gig Driving Interact with Congestion? A Road-by-road Analysis.

Many of our clients are concerned with new modes of travel and how they interact with existing transportation. Comprised of services like Uber and Lyft, “gig driving” for riders and delivery is a persistent concern. And its impact on congestion is hotly debated topic. Some claim it makes congestion better (less parking or less personal car driving), and others claim it makes congestion worse. The biggest challenge to finding the answer is a deep lack of available data about this mode of transportation.

Our team at StreetLight Data suspects that the interaction between gig driving and congestion depends heavily on what we call “context,” i.e. location, existing transit, urban density, land use, time of day, etc. We also believe that gig driving is here to stay and that we can arm policy makers with data to avoid the desire to “undo” what is a mobility mega trend — and instead try to harness and accelerate positive effects on the community and transportation networks.

To test our hypothesis about granular variation, we used our access to billions of transportation data points, and a lot of expertise in data science and transportation. We measured the interaction between gig driving and congestion road-by-road throughout the greater Miami region.

Our goals:

a.  Find a way to measure gig driving that doesn’t rely on waiting for data sharing by private companies

b.  Measure “gig mode share” — or the percent of vehicle trips on a given road segment at a given time that is a gig vehicle — on all roads within a single MSA. We chose greater Miami, an area boasting over six million people, heavy tourism, a diversity of urban density, rich variety of neighborhoods and plenty of nightlife, all adding up to a rich testing ground for our hypothesis.

c.  Combine this “gig mode share” analytic with our core analytics about total vehicle volume and congestion to explore the interaction between gig share and congestion in different urban contexts.

Why do this? Because we suspect that sometimes gig is associated with better congestion, sometimes worse, and sometimes it’s neutral. There’s no one size fits all approach to managing gig driving within a city, so we need granular analytics to make good policy decisions, for example, figuring out which areas really need designated pick-up/drop-off areas or need incentives to encourage more equitable gig driving access, etc.

Spoiler alert – our hypothesis was proven largely correct. In the greater Miami region, depending on context, a road with a higher “gig mode share” can have a positive, negative, or neutral correlation with congestion. We measure congestion as (1 – [free flow factor]). See this link for details. The contexts we explored in this report are time of day, proximity to major transit centers, road class (highway/non-highway), land use, and density.

Methodology

StreetLight analyzes mobility patterns from millions of mobile devices every day. Analyzing congestion and driving characteristics for the full population is our core product, and for more on our approach, click here.

To create gig driving analytics, we worked with our partners at Cuebiq and Kochava Collective to segment anonymous users as having gig driving apps installed on their devices. Then we analyzed how those segments differ from the norm. It’s very clear that gig drivers don’t move around like you or me!

Their patterns include more miles traveled per day, irregular routes without fixed favorite places, and more. We used machine learning and probabilistic modeling to identify when a mobile device is in “gig driving” mode, and not — then applied those insights to our existing database. For this study, we analyzed over 15,000 gig drives, and several million personal driving trips.

Findings

Because each region is a unique with its own contextual nuances, these findings only hold for greater Miami! Each city must perform its own detailed analytics on its own context. As we repeat this effort in more places, we may find commonalities. However, we’ve shown  that it’s feasible and straightforward to do this analysis for any city.

None of these relationships are causal! They are correlative. [Correlation does not equal causation!]

1. As shown in Figure 1 – gig mode share (gig trips as percent of all trips on the road segment) is higher in certain parts of town, notably tourist- and hotel-heavy areas like Miami Beach, downtown Miami, and at Transportation Network Company (TNC) ramps into Miami-Dade Airport. This matches intuition and helps build confidence in this hard-to-validate analytic.

Figure 1 – Gig mode share means gig driving as percent of all trips on road segments in greater Miami (weekday, all day). Heavy gig mode share areas, shown in darker red, are concentrated downtown, airports, and in beach/tourist areas.

Figure 2: Close up of gig driving as percent all trips in downtown Miami. High gig mode share areas (darkest red) are concentrated at the Airport and heavy hotel/tourist districts, which matches intuition. It is extremely hard to find data to validate gig driving metrics at a granular level because this data doesn’t exist outside of private gig companies’ servers…until now.

2.  In general, gig driving patterns follow overall temporal patterns. We see morning and evening peaks on weekdays, for example. However, gig mode share goes up on evenings, late night, and weekends. These are times with low congestion, when a few extra gig cars won’t make a big difference.

If the roads are congested in the first place, if gig creates more trips than would have existing otherwise, gigs do make a difference (like any type of marginal addition). Thus, the relationship between gig mode share and time of day is neutral.

3.  In general, gig driving is a fairly consistent share of highway driving. This means that all highways have a similar experience with gig, and there are not one or two highways disproportionately impacted. Highways with higher gig mode shares have a very slight increase in congestion (especially on weekdays), but it is between 0.1% and 0.2% increase for each % increase in gig mode share – which is barely noticeable even in rush hour.

4.  The bigger opportunity for policy makers is on non-highway roads. On arterial roads, we see 6x the impact as on highways: a 0.6-0.7% increase in congestion with each percent increase in gig mode share. Thus, we find that, in general, gig mode share has more variability, and more of an impact, on congestion, on non-highway roads. Policy makers can use this to find local roads to focus on for interventions.

5. In commercial areas (as opposed to residential areas) is where it gets interesting. In greater Miami, in the very dense neighborhoods (top 10% of points-of-interest density, see map) a high gig mode share is correlated with a lower congestion.

However, in the mid-dense commercial areas, high gig mode share is correlated with higher congestion. We interpret this to mean that in greater Miami there’s a threshold of activity density above which gig mode share increases are associated with less congestion, and below which gig is associated with more congestion.

Perhaps this is because of reduced parking-searching in the very hot neighborhoods. But in medium-density neighborhoods, the gig vehicles may be adding trips or stopping and blocking traffic. Other Miami-neighborhood-specific factors like road width may come into play as well.

Figure 3a, b, c: Gig driving as percent of total, location of commercial points of interest (like restaurants, bars, entertainment) and a mashup. In some locations, both gig mode share and POI are “hot.’ These are areas associated with less congestion compared to areas with gig mode share is high but POIs are moderate – suggesting an activity threshold above which gig is associated with better congestion in greater Miami.

For example, Figure 4a shows neighborhoods near downtown Miami.  All of them are in the top 20th percentile in terms of gig mode share as a percent of all driving on weekdays. Orange neighborhoods, like Little Havana, have both high gig mode share and high congestion. Green neighborhoods, like Bay Shore, have high gig mode share but very low congestion.

Even within the orange neighborhoods, the ones with a star, like Brickell, representing “top activity density” are likely to have lower congestion than one with no star.  Image 4b shows the same information but for weekends.

Figures 4a and 4b: These maps show neighborhoods in central Miami (as defined by Zillow). Green neighborhoods are ones where gig mode share is very high (top 20th percentile) and congestion is fairly low (bottom 40th percentile). Orange neighborhoods are ones where gig mode share is very high (top 20th percentile) and congestion is very high (top 20th percentile). The stars show neighborhoods with very high concentration of bars, restaurants, stores etc. Even though some of these neighborhoods are “orange” they are likely to get a little less congested as more gigs enter, though the opposite relationship is true for their non-starred peers.

6.  Transit Hub Proximity – We looked at the subset of roads that were within 400 meters (walkable range) of the various bus and transit stations in the region. Overall, these roads are more congested than the typical greater Miami road. This doesn’t mean transit causes congestion! It means we tend to put transit in busy places, as we should.

In these zones, the higher the gig mode share, the lower the congestion. This finding may be a restatement of our finding #4 – as more transit hubs are in high-density areas. It may also mean that lots of people are taking gig to transit stations in the further-flung, less congested parts of town. Future analysis of transit ridership and gig trip destinations will help us learn more about this relationship.

We know there are so many more contexts to analyze, and we are already getting started. This analysis looked at variation of gig driving across one region in the present – other researchers in the past have looked at changes over long period of time (before and after gig hit a city), and StreetLight can explore that in the future.

In addition, we can look into the context of demographics, rent prices, and transit ridership. We can split out gig delivery from gig rideshare. We want to measure the way gig driving interacts with social justice, safety, climate, and VMT, not just congestion.

If you have more ideas of context, please share them with us! It’s a complicated story, and we strongly believe granular analysis is critical for understanding it. We therefore wanted to get the conversation started with these first set of results.

To close, here’s a video that shows a subset of the Gig Driving “pings” animated over 24 hours. Most activity is concentrated downtown and in Miami Beach. But a few lucky (unlucky?) drivers get some 1+ hour trips into and across the Everglades!JTNDaWZyYW1lJTIwdGl0bGUlM0QlMjJ2aW1lby1wbGF5ZXIlMjIlMjBzcmMlM0QlMjJodHRwcyUzQSUyRiUyRnBsYXllci52aW1lby5jb20lMkZ2aWRlbyUyRjMwNDkyMzY3OSUzRmglM0Q1MTljODhlMjYzJTIyJTIwd2lkdGglM0QlMjI2NDAlMjIlMjBoZWlnaHQlM0QlMjIzNjAlMjIlMjBmcmFtZWJvcmRlciUzRCUyMjAlMjIlMjAlMjAlMjAlMjBhbGxvd2Z1bGxzY3JlZW4lM0UlM0MlMkZpZnJhbWUlM0U=

The Best I 95 Rest Stops (According to Big Data)

The Best I 95 Rest Stops (According to Big Data)

Jump Ahead

The StreetLight InSight Analysis What Are the Top Five Busiest Rest Stops Along I-95? What Are the Busiest Times of Day to Go to Rest Stops? Which Rest Stops Are Visitors Traveling the Furthest To? Where Do Rest Stop Visitors Go Next? Conclusion

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Now that we’re in the full swing of summer here on the East Coast, my friends and family have been taking road trips along I-95 – and for better or for worse, rest stops are an inevitable part of those trips. A few weekends ago, my parents’ car broke down on their way back home from New York, but fortunately they were able to make it to a rest stop. As my mom texted me from the rest stop parking lot, waiting for a tow truck at 11pm, I started to wonder: How many other people were at rest stops at such a late hour? Who else might be there with my parents? Was this rest stop typically a busy one?

It wasn’t long before I realized that, as a StreetLight Data employee, I had the answers at my fingertips – it was just a matter of running a study with StreetLight InSight®, our on-demand transportation analytics platform. In this post, I’ll show you what I found out, and share a few tips for evaluating your state’s rest stops.

The StreetLight InSight Analysis

I used StreetLight InSight to analyze the volume of traffic entering and exiting 76 rest stops along I-95 – an interstate highway that runs down the East Coast of the U.S. from Maine to Florida – during the months of June, July, and August in 2017.

To do this, I first downloaded a list of 103 rest stops by latitude/longitude from POI Factory. From there, I removed any rest stops that did not have any facilities – basically, rest stops that just have parking or a scenic view. Next, I entered each latitude/longitude into StreetLight InSight, located the rest stop, and drew a polygon around it to create my analysis zones.

I chose to analyze June, July, and August in order to capture family trips taken when kids are out of school. To run this study, I used StreetLight’s Location-Based Services data source. These are anonymous location records created by smartphone applications that provide Location-Based Services. This type of Big Data is great for analyzing personal travel, so it was the right data source to use given my focus on summer road trips. Here’s exactly what I wanted to find out:

• What are the top busiest five busiest rest stops along I-95?
• What is the busiest time of day to go to rest stops?
• Which rest stops have visitors traveling the furthest away?

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What Are the Top Five Busiest Rest Stops Along I-95?

I used the StreetLight InSight Zone Activity analysis for this study. It provides transportation analytics that describe the relative volume of trips that begin, end, or pass through specific locations. The results showed that the top five busiest rest stops along I-95 are at the following locations:

• Delaware – NB/SB at MM 5 (Delaware House)* – Newark, DE
• Maryland – NB/SB at MM 81 (Maryland House)* – Aberdeen, MD
• Connecticut – NB at MM 12.4 (Darien Service Plaza) – Darien, CT
• Maryland – NB/SB at MM 2/3 (Chesapeake House)* – North East, MD
• Connecticut – SB at MM 41.4 (Milford Service Plaza) – Milford, CT

*These rest stops are in the middle of the highway, so visitors from both directions share the facility, which may help explain the high volume. But it’s a good insight for rest stop builders of the future who want to maximize visitors.

And with that, my first question was answered – my parents were at the fourth busiest rest stop along I-95, the Chesapeake House rest stop.

It’s interesting to note that the top five rest stops are clustered around two locations. The Milford Service Plaza and Darien Service Plaza are both located north of New York in Connecticut. It is possible that these are popular rest stops because they serve travelers between New York City and Boston heading to summer beach destinations. Likewise, the three rest stops further south, in Delaware and Maryland, are between Philadelphia and Washington, D.C., and they’re “on the way” to beach destinations in the mid-Atlantic.

While I didn’t analyze rest stop amenities comprehensively as part of this study, I was curious about the services offered at the top five most popular rest stops. This is what I learned:

• They all appear to be recently remodeled.
• They all provide electric vehicle charging stations as well as gas stations.
• They each have at least six different food service options.
• Four out of the top five rest stops have green/eco-friendly initiatives: The two rest stops in Maryland are LEED certified and the two in Connecticut are solar-powered.

What Are the Busiest Times of Day to Go to Rest Stops?

Next, I dug a little further into the transportation analytics and found out the busiest times of day to go to the top five rest stops. StreetLight InSight lets me customize day parts, so I defined them as:

1. Early morning (5am-9am)
2. Mid morning (9am-12pm)
3. Lunchtime (12pm-2pm)
4. Mid afternoon (2pm-5pm)
5. Evening (5pm-8pm)
6. Night (8pm-5am)

Across the top five rest stops, the busiest times of day were:

1. Mid afternoon (2pm-5pm)
2. Night (8pm-5am)
3. Mid morning (9am-12pm)
4. Lunchtime (12pm-2pm)
5. Evening (5pm-8pm)
6. Early morning (5am-9am)

Here’s the breakdown for each rest stop:

With this information, I learned the answer to my second question – my parents were at the Chesapeake House rest stop during one of their top two busiest times of day, between 8pm and 5am.

Which Rest Stops Are Visitors Traveling the Furthest To?

Finally, I wanted to find out which of these rest stops have visitors traveling from the furthest away. With a few clicks of my mouse, I learned that the Delaware House rest stop has visitors traveling from the furthest distance, with Maryland House in Aberdeen coming in a close second. I suspect that the popularity of Delaware House is related to this rest stop’s proximity to three highly populated metropolitan areas – Washington, DC, Philadelphia, PA, and New York City. If any local planners are reading this and have some context to share on this location, I’d love to hear from you.

Next, I decided to look into the length and duration of trips from all of the rest stops I studied, on average. Were trips from the top five most popular rest stops typically longer or shorter than the average rest stop trip? It only took minutes to find out that the rest stops with visitors coming from the furthest distances were in South Carolina and North Carolina – states home to popular beaches like Myrtle Beach, Nags Head, and Hilton Head. Looks like people will go the distance to visit beaches in the South.

The next step was to compare the length of trips that begin at any of the 76 rest stops I analyzed with the top five most popular rest stops. I was surprised to see the relatively high volume of shorter, regional trips that originate at rest stops. Nearly 35% of trips from all of the rest stops in the analysis are less than twenty miles long. Thirty one percent of trips from the top five most popular rest stops are under 20 miles – a slightly smaller share, but not by much.

This finding indicates that rest stops are providing important benefits and services to regional travelers – not just long-distance ones. When we invest in rest stops, we’re certainly helping road trippers and truck drivers, but also residents of local and nearby jurisdictions.

Where Do Rest Stop Visitors Go Next?

Of course, this led me to wonder: where were the visitors to the top five busiest rest stops going next, and where are their home locations? To find out, I ran two additional analyses – an Origin-Destination to Pre-Set Geography and a Visitor Home-Work analysis. Origin-Destination to Pre-set Geography analyses scan all trips that begin, end, or pass through any geography to identify the origins and destinations, ZIP code, census block group, or transportation analysis zone. (For Canadian analyses, dissemination areas are available.) Visitor Home-Work analyses provide the aggregate expected home and work locations of visitors to a specific location.

From the O-D to Pre-Set Geography analysis, I learned that the next stop for visitors traveling to the top five rest stops were generally clustered around the coast or around major metropolitan areas. Travelers departing from rest stops in Connecticut tended to go to New England’s beaches, while travelers departing from rest stops in Maryland and Delaware had a broader range of coastal destinations across the Chesapeake Bay and in New Jersey, Delaware, Maryland, and New York.

From the Visitor Home-Work analysis, I learned that visitors to these rest stops reside fairly close-by – check out the charts below. This makes sense given that over 30% of trips from the top five rest stops are fewer than 20 miles long. Plus, families traveling with children tend to stop more frequently for bathroom breaks. I’m not at all surprised that Richmond, Virginia is also a popular home destination of visitors, seeing as my parents live there, and it’s home to StreetLight’s East Coast offices!

Conclusion

Whether you’re planning a road trip and want to avoid the crowds, a transportation planner looking to understand travel behavior for road maintenance and construction projects, or an economic development professional interested in the impact of rest stops, StreetLight InSight can provide a wealth of information in a matter of minutes.

Have you stopped at any of these rest stops – do they feel as busy as the numbers say they are? Let us know in the comments below!

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StreetLight Data Powers UberFlux for Aerial Urban Transportation

StreetLight Data Powers UberFlux for Aerial Urban Transportation

At this week’s Uber Elevate Summit in LA, I saw the power of using StreetLight Data’s transportation analytics for a brand-new mobility challenge: Optimizing the rollout of new infrastructure for aerial urban transport. Uber hosted the conference to “explore the exciting future of urban aviation” (read: flying shuttles). While lots of the press focused on the new models of Electric Vertical Take-Off and Landing Vehicles (eVTOLs), I know readers of this blog will be even more excited by the use of data-driven planning to design their infrastructure!

What is UberFlux and How Did it Use StreetLight InSight?

At the opening address, and again at a deep dive panel, Uber showed off updates to the UberFlux tool. This tool pulls in data from several sources, including StreetLight InSight, our on-demand platform for turning Big Data into transportation analytics. UberFlux is used, among other things, to find optimal solutions to a complex problem: Where are the best places to site “nodes” for Uber’s upcoming UberAIR program? It is a stellar example of putting Big Data to work to drive transportation forward (should I say “fly” transportation forward?).

Jon Petersen, Head of Data Science at Uber Elevate, said: “StreetLight InSight and the StreetLight team play an important part to our modeling efforts and remain wonderful partners for the Elevate team.”

You might ask – why does Uber, a company with so much transportation data, benefit from StreetLight analytics? The answer is that Uber knows about Uber trips. But they don’t know about non-Uber trips. That’s where we come in.

The Uber team used StreetLight InSight to measure the volume and detailed characteristics of trips and travel activity between every block in the LA Metro area – to understand all 45+M daily trips. In the words of Eric Allison, Head of Elevate at Uber, “We’ve taken our own Uber data, which we have a lot of, and combined it with third-party data…and thus we can build a picture of close to 100% of the movement in cities.” StreetLight InSight is a key source of that third party data. You can watch this beautiful animation of total activity and origin and destinations derived from our Metrics by watching Eric’s address on YouTube. In the video embedded here, you can start at around minute 42 and keep watching through minute 57 to see how it all fits together: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Here’s why using third-party data like ours is so important: When planning for long distance aerial transportation, you have to know about long distance trips that currently take place in Ubers, but you also have to know about the trips that take place in private cars and via other modes.

By mixing together both data sources, as well as other information from a detailed adoption survey done by RSG, the team at Uber Elevate was able to create a tool dedicated to creating the most cost-, time-, and energy-efficient strategy to kick off this bold new concept for urban transportation.

From StreetLight’s perspective, this is an excellent example of clients integrating their own data with ours to solve complex problems. It demonstrates the power of data to push transportation infrastructure planning beyond the status quo. We see other partners moving in this direction (though not necessarily with flying cars) and can’t wait to share more examples.

Should We Be Excited or Worried about Flying Shuttles?

The conference brought up several other questions for me in regards to StreetLight’s Data’s broader mission to reduce vehicle miles traveled (VMT) in gas-powered, privately owned cars:

1. I still have outstanding concerns about the greenhouse gas efficiency of electric aerial transport compared to electric vehicle transport. Ride-sharing is key for both and Uber Elevate seems dedicated to shared access, which is good.

2. Uber stated that the availability of this option will probably unlock latent demand for longer distance trips that don’t occur today because the travel times are just too long. This raises a flag to me. I think they’re right because lowering travel costs (in time or dollars) usually does increase VMT. While there is some economic benefit in this, such as linking smaller, co-located cities so they can operate like a megacity. However, we’ve seen historical evidence that opening up new long-distance options (even transit!) may drive up total regional VMT. No matter how efficient the mode may be, we cannot achieve our sustainability and quality of life goals if VMT increases.

3. A lot of people have instinctive concerns about the equity of aerial transportation. I share those concerns. As with the greenhouse gas emissions, sharing is again critical to make this mode as affordable and equitable as possible and I’m glad to see the emphasis of the Elevate program on shared rides. In addition, it’s critical that Elevate achieve its vision for a multimodal transport future that includes transit, bike/scooter/vehicle shares, and other options in a network with aerial transport. Good corporate and public policy and political courage will be required to mitigate negative equity consequences. (Of course, we’ll also need data to measure how the launch of new modes like UberAIR affects everyone else in the system to make such policy.)

4. If you have a chance, listen to the presentation from Uber Elevate about sound psychology by Joseph Schittenlacher! There’s a lot of complexity when it comes to urban aerial transport sound and nuisance. As someone with some serious misophonia, I was intrigued on a personal level. Uber’s goal is to be half as loud as an urban truck. I propose a noise cap-and-trade system. For every eVTOL that Uber (or anyone else) gets licensed to fly, it has to pay for an electric (noiseless) retrofit of two locally owned trucks.