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Big Data | public transit | Smart Cities | Transportation

From Multimodal to Omnimodal: Living Today’s Transportation Reality

July 20, 2018

After nearly 6 years at StreetLight Data, my transportation nerdiness is firmly cemented. Every time I take a trip, I research the various modes of transportation available to reach my destination, then carefully plan my itinerary. Frankly, I enjoy the challenge of combining modes in the most efficient way possible. Trip-planning smartphone apps like GoogleMaps are simplifying this process for non-transportation nerds, too. For me, the most efficient mode of travel generally means the fastest, but carbon emissions contributions and cost are always important considerations as well.

My personal multimodal challenge was elevated to a new level on a recent trip that combined business travel for a customer training with personal travel for a family wedding. Not only were these two locations in different states (neither of which I live in), but the wedding was somewhat of a destination, and it was not terribly accessible for visitors from far away. In this article, I’ll use my travel experience to illustrate how today’s journeys can quickly become so multimodal that you might even call them “omnimodal.”

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

Transportation Policies and Equity: A Bay Area Regional Ballot Initiative

June 3, 2018

StreetLight Data is located in San Francisco CA, so many of our staff are voting next week on a Regional Ballot Initiative with considerable equity implications. It’s called Regional Measure 3. In short, it would increase tolls on all eight Bay Area bridges that are currently tolled. Thus, by the end of the six years, each toll will be $3 more expensive than it is now. The would raise an estimated $4.5B during the six years, all of which would go to fund a set of 35 highway and transit projects across the Bay Area. If you want more details, here’s an article from our local National Public radio station, KQED: “10 Things to Know: Regional Measure 3, the Proposed Bay Area Bridge Toll Increase”.

I used StreetLight InSight® (that’s our on-demand transportation analytics platform) to get a better understanding of the equity implications of the toll increase.  (Note: StreetLight Data does not currently endorse any political candidates or initiatives.)

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Big Data | public transit | Transportation

Measuring Travel Behavior for Special Events: Who Goes to Warriors Games at Oracle Arena?

December 14, 2017

2017 has been a really busy year for the StreetLight Data Engineering team – and as Sr. Sales Engineer, I really appreciate their hard work. In today’s blog post, I’ll give you an in-depth introduction to one of my favorite new features in the StreetLight InSight® platform - “Customizing Your Own Data Period.” (For those that don’t know: StreetLight InSight is our easy-to-use web application for transforming Big Data into transportation analytics).

When we launched this feature, I was immediately reminded of the work I used to do at San Francisco Bay Area Rapid Transit District (BART), the regional heavy rail transit system that many Californians rely on everyday. Ensuring we had sufficient capacity in the system to accommodate special events was important – and it could be a challenge. We would regularly develop new service plans and even add special event trains to the schedule to deal with the crows and their unique travel patterns.

To show you how this new feature works, I’ll walk you through an analysis I did on home games for the Golden State Warriors basketball team in March 2017. I was working at BART at this time, and it was a big year for the Warriors – they won the NBA championship, and they also made a major decision to relocate from their home in Oakland’s Oracle Arena to a new venue in the Mission Bay neighborhood of San Francisco, California. That move is anticipated in 2019, and it promises to change the travel behavior of home game attendees.

Keep reading for all the details on our new Customize Your Data Period feature – and to find out about what the Warriors’ move might mean for travel patterns on game days in the San Francisco Bay Area.

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public transit | Transportation

Our Top 5 List of Transportation Entertainment: Transit Edition

November 21, 2017

Can’t get enough transportation in your life? Neither can we! Here at StreetLight Data, we’re pretty serious transportation nerds! As we settle down to enjoy the Thanksgiving holiday with our loved ones, we’ve been thinking about all the different transportation-related transportation movies, books, and more that we can enjoy.

Since we're also getting ready for a great webinar on multimodal planning with the City of Toronto (register here!), we’re sharing our top 5 transit-related movies, books, and TV shows in this blog post. Enjoy!

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Blog Feature

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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Blog Feature

Big Data | public transit | Transportation

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

October 11, 2017

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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