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Big Data | public transit | Smart Cities | Traffic | Transportation

Our New Multimodal Measurement Initiative

June 6, 2018

We are rapidly moving into a world where one person can easily use six different modes of transportation in a single day. Mobility behavior is becoming more complex. As a result, transportation planners need to be able to analyze past behavior, measure current changes, and forecast future demand for more modes of travel than their predecessors.

Every type of travel counts, from bike shares to the "gig economy" created by transportation network providers and delivery apps. Unfortunately, not every mode is being counted. It’s simply not possible with conventional transportation data collection methods.

That's why StreetLight Data launched our new Multimodal Measurement InitiativeOur goal is to launch a new suite of Metrics. These Metrics will measure brand-new modes, older modes of transport that have always been difficult to measure, and how all of these modes interact. Do you want to participate?

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autonomous vehicles | Big Data | Events | Smart Cities | Traffic | Transportation

StreetLight Data Powers UberFlux for Aerial Urban Transportation

May 11, 2018

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 roll out 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.”

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Big Data | Smart Cities | Traffic | Transportation

Visualizing Big Data for Transportation: Our Latest Updates

This week, we updated StreetLight InSight®, our on-demand platform for turning Big Data from mobile devices into actionable transportation analytics – just like we do every month. The improvement we’re most excited about for May is our interactive visualization builder. In a nutshell, the interactive visualization tool is a more powerful tool for StreetLight InSight users to analyze transportation behavior within our platform. It’s also a major step forward towards our goal of helping transportation professionals put Big Data to work for planning, modeling and engineering.

Let’s face it: it’s always easier and faster to understand trends with a heatmap or a chart than with a giant spreadsheet. It’s even better when you can dynamically manipulate the data behind those charts and heatmaps to see how behavior changes in different conditions. (But don’t worry, long time users, the spreadsheets are not going away!)

 

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Big Data | Corridor Studies | Software Updates | Traffic

The Congestion Analysis for StreetLight InSight®

February 21, 2018

Note: Since publishing this blog post, we have renamed the Congestion Analysis to "Traffic Diagnostics." 

“Big Data” is becoming buzzier and buzzier, but the adage “data is just a cost until you use it” is an increasingly common refrain. As the CEO of a transportation analytics company, I’m a true believer in the power of data. It frankly makes me sad to see so many groups investing in the idea of Big Data only to feel disappointed when they can’t get any useful information out of it. At StreetLight Data, our top priority is to help the transportation industry “put Big Data to work”.

That’s why I’m thrilled to announce the latest feature for StreetLight InSight (that’s our on-demand platform for turning mobile device data into actionable transportation analytics.) It’s called the Congestion Analysis, and it’s designed to help you put Big Data to work to solve traffic jams. Lots of tools (including your eyes!) can tell you where traffic jams are occurring. The Congestion Analysis can tell you why the jam occurs, and suggests data-driven ideas for the best long-term solutions.

The Congestion Analysis is very different from every other feature that we’ve introduced to StreetLight InSight because – and this may surprise you – all of the analytics it provides were already available in our platform. The change is how we weave the Metrics together to tell a story, making StreetLight InSight Metrics much easier to use for congestion busting in the real world of transportation planning, modeling, and engineering.
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Big Data | Location-Based Services | Smart Cities | Transportation

4 Big Data Trends in Transportation to Watch in 2018

January 18, 2018

It’s no longer news that Big Data is a big topic in transportation. Many people in our industry have been exploring how to use Big Data for years. But the technology landscape is evolving quickly, and in ways that may drive more widespread adoption of this type of data. In this post, I’ll share the four most important trends in Big Data to watch in 2018.

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Our 2017 Year in Review

January 4, 2018

It’s been an exciting year for StreetLight Data. From bringing on major new customers like Minnesota DOT and Ohio DOT to doubling the size of our Big Data sample, we have been on a roll. And that’s not all – we also added more than 25 new features and Metrics to StreetLight InSight®. In this blog post, I’ll share some of our top highlights from the last year.

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Blog Feature

Big Data | Corridor Studies | Transportation

The Segment Analysis: A Better Way to Measure Corridor Travel Behavior with StreetLight InSight®

December 7, 2017

We’ve added a brand-new type of analysis to StreetLight InSight®: the Segment Analysis. This feature helps you measure corridor travel behavior faster and more comprehensively. In this blog post, we’ll show you how the feature works and walk you through three great ways to use it:

  • Diagnosing the Cause of Congestion
  • Multimodal Planning
  • Before-and-After Studies

For a demo of this new feature and more, watch our recorded webinar.

 

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Big Data | Commercial Trucks | Traffic | Transportation

Are We Ready for Autonomous Vehicles?

November 16, 2017

At this point, it seems clear that autonomous vehicles are on the verge of technical feasibility. Just last week, Waymo announced that it is testing self-driving minivans without a human back-up in the front seat. Its employees will be riding in the back with an emergency stop button – but no steering wheel. But do these technical advances mean that we’re ready for AVs? How should we manage the non-technical aspects of AV deployment to ensure they achieve promised improvements in safety and accessibility?

I decided to write this article to address these issues after participating in the Intelligent Transportation System World Congress earlier this month. There were tons of panels focused on autonomous vehicles, and I was lucky enough to be speak on one that dove into the critical questions for civic leaders and transportation professionals. We went beyond technical readiness to ask ourselves if should we deploy AVs, and, if so, how should we deploy them?

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Big Data | public transit | Transportation

Planning Effective Evacuation Routes: How Big Data Can Help

October 26, 2017

As a San Francisco-based company, the wildfires that recently spread across northern California have been extremely troubling for our team at StreetLight Data. For us, this went beyond poor air quality in the Bay Area. The fires impacted the homes and personal well-being of our employees, our clients, and our families. While it is always difficult to see tragic events occur anywhere in the world, watching fires destroy places we love was something else entirely.

The experience forced us to think harder about what we, as a company, can do to help. Our product, StreetLight InSight®, helps transportation professionals use Big Data from mobile devices to understand travel patterns – but what specific information can it provide to aid in evacuations? At a personal level, how can we help communities in need, at least in the continental US and southern Canada, where we currently operate?

Before I dive into details, I want to stress that we’re here to help. If your community is facing an imminent evacuation, and our Metrics could help you get people get out of harm’s way, email me (I’m the CEO) directly. Tell me what you need, and we’ll skip the formalities and paperwork to provide the data for free as quickly as we can.

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Big Data | Mobile | Software Updates

How We Doubled Our Sample Size in One Year

September 28, 2017

We just passed our one-year anniversary of using Location-Based Services (LBS) data, so we decided to update some key sample size figures. The results are exciting: Our sample size has doubled to more than 62 million devices in the US and Canada in the past year. In other words, now our analytics anonymously describe the travel behavior of 23% of the US and Canadian adult population.

There are many reasons for this increase, including our main LBS data partner, Cuebiq, doing a great job. However, the most important reason is that Location-Based Services are becoming more and more widely adopted by consumers. As a result, our clients can now analyze the aggregate travel patterns of nearly ¼ of the population in just a few mouse clicks.

That’s a large sample by any measure, but when you consider the “status quo” methods of collecting travel behavior data, it’s even more dramatic. Imagine how much it would cost – and how long it would take – to collect household travel surveys from 62 million people, or to install sensors and traffic counters on the roads they use every day. It just wouldn’t be feasible. In this blog post, I’ll explain how we calculate sample size (hint: accuracy is more important to us than flashiness) and why it’s grown so much in just one year.

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