Mobility Analytics Blog

StreetLight Data Blog

The latest news about Big Data and mobility analytics.

Blog Feature

Big Data  |  Corridor Studies  |  Transportation

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

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 live demo of this new feature and more, sign up for our webinar on December 12th. 

 

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Blog Feature

Big Data  |  Detours  |  Transportation

The Big Picture: Big Data for Detour Planning

If you’re like many drivers today, your heart sinks when you see an orange road sign with the word “DETOUR.” Suddenly, you remember the flashing highway sign that warned you in advance that construction was coming. In today’s busy world, you simply forgot and did not prepare. As you join the hundreds of other drivers who are now taking a circuitous journey around much-needed construction, you realize that you’re going to be late.

But with the right data tools, transportation planners can make these frustrating detour experiences much better. When planning major transportation projects such as construction and lane closures, it is essential to identify the best alternate routes to reduce traffic disruption – that’s something we can all understand. But it’s time to change the way we identify them. Learn more about using Big Data to improve detour planning in this blog post.

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Blog Feature

Big Data  |  Commercial Trucks  |  Traffic  |  Transportation

Are We Ready for Autonomous Vehicles?

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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Blog Feature

Big Data  |  Traffic  |  Transportation

4 Ways Big Data Helps Cities Create Better Transportation Plans

Getting the data you need to make savvy transportation planning decisions can be a lengthy, expensive process if you’re using surveys, license plate counts, or road sensors. These methods also make it difficult to evaluate the performance of a policy or project after implementation.

For example, when planners rely on old or modeled data to design their project, they don’t have a real-world baseline to evaluate current performance after a project is completed. Most road sensors are removed after a few days, so they must be re-installed to collect more data. Surveys can take months or even years to complete. If planners want to make performance-based improvements as they implement policy or infrastructure changes, they often have to start from scratch with data collection. This could create real flaws if your community has seasonal variation based on weather or school - or if your community has new transportation patterns derived from the rise of ride share or urban population growth, for example.

In today’s world, there are much more efficient, up-to-date, and accurate ways to obtain the information you need.  Nearly 77% of the US population uses a smartphone, and the data they produce is creating new options for transportation planners. In this article, I’ll share four advantages that Big Data has over conventional data sources for transportation planning.

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Blog Feature

Big Data  |  Transportation  |  public transit

Planning Effective Evacuation Routes: How Big Data Can Help

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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Blog Feature

Big Data  |  Transportation  |  public transit

Big Data and Public Transit: Measuring Vehicle Trips to Help Modeshift

Public transit is a key component of cities’ mobility networks, especially in dense urban centers. Trains and buses help commuters avoid the hassle of traffic jams on congested roadways, not to mention pricey parking. But some cities are attracting commuters and residents so quickly that public transit cannot keep up -- just ask anyone who lives in Denver, Colorado.

The population in Denver has grown by ~45% since 1996, and the average commuter there now spends 49 hours per year sitting in traffic, but only 4.4% of commuters use public transit (Source: Denver Post). Similar scenarios are playing out across the US in cities like Austin, Seattle, San Francisco, and more. Even though alternatives to driving are available in many of these growing cities, not enough commuters are using them – and congestion keeps getting worse.

Traditionally, public transit planners improve systems by looking at existing transit users’ behavior. They identify potential users as those who live and work near transit stations. But in this era of rapid urban population growth, we cannot consider these groups alone: What about the people who are driving because transit isn’t currently a viable option? What about the people who could be using the transit to commute, but aren’t? In this blog post, I’ll walk you though a few ways Big Data can help address these questions.

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