Traffic congestion negatively effects the economy, roads and our quality of life. Some people tend to blame pass-through trips, commercial attractions or employers, but expectations don’t always line up with hard data about what really causes congestion.
In California’s Napa County, for example, it is common to blame traffic on wine-tasting tourists. However, when planners used Big Data to map out travel behavior in traffic congestion studies, they found out that commuters actually contributed as much to congestion as tourists. High housing costs in Napa are a major part of the problem.
It’s not easy to figure out why congestion happens, especially in downtown districts. Learn how to improve the accuracy of your traffic congestion studies by using analytics derived from Big Data.
This year, the Eastern Research Group (ERG), Coordinating Research Council (CRC), and StreetLight Data teamed up to validate one of the big as yet unmet promises for Big Data – the ability to better model and thus manage criteria pollutant air emissions from vehicles.
The results of our work show that using Big Data to model emissions at the county level is more accurate than industry-standard practices today. Of three different counties we analyzed, we found that:
Modeling emissions accurately matters: It allows air quality models to better predict concentrations of the regulated air pollutants ground-level ozone and particulate matter in different counties, which informs air quality planning and control strategies at the local level. In this blog post, we will walk you through the new methodology and some of our key findings.
Get the latest news about Big Data and mobility analytics for the transportation, retail, and real estate industries.
Locational Big Data – the geospatial data created by mobile devices – is ubiquitous. Smartphones, connected cars, fitness trackers, and more create trillions of location records as their users go about their daily lives. There are many benefits of this Big Data, but one, of course, is large sample size. But just knowing something is “large” is not always enough. Many of our clients want to know more detailed info on sample size for individual projects. It helps them understand certainty of results.
In this blog post, I’ll explain how we’ve updated StreetLight InSight® (that's our easy-to-use online platform for transforming Big Data into transportation analytics) so that our clients always know the size of the sample they’re working with.
In my hometown of San Francisco, California I grew up riding the municipal bus home from school with a group of classmates. As a group, we saw the San Francisco Municipal Agency (SFMTA) try a multitude of strategies to make the system more efficient and cost-effective. For example, SFMTA transformed car lanes into dedicated bus rapid transit (BRT) lanes to increase the fleet’s speed. When many of these decisions were made, most of my bus crew was under the voting age, and as teenagers, public forums were not an exciting Friday night activity.
I remember that we complained almost every day about how slow or inefficient the bus was, yet we never did anything to try to fix public transit. We never participated in the public forums or in outreach programs that gathered feedback on BRT. Now, as BRT expands, huge changes are happening to the system that affect my current commute. However, at the same time as SFMTA was inviting the public to share their feedback, I bought a smartphone. To the dismay of my parents, I used that smartphone constantly. Now imagine if my teenage obsession had allowed me to be a more active participant in decisions like the BRT, simply because SFMTA could use the location data created by my phone to develop their future plans.
When it comes to transportation planning, it seems like Big Data—location data created by connected cars and trucks, smartphones, and wearables—is the next big thing. How public agencies will actually use and implement this data is the big question on people’s minds.
With 2.5 quintillion bytes of Big Data being created daily, and much of this data offering valuable insights for transportation planning, it almost seems negligent for government agencies to not use this information source. Brookings Institution, a non-partisan research institute, recently looked into why government agencies are lagging behind on Big Data adoption. With the help of our CEO, Laura Schewel, the Brookings Institution has brought to light the core disconnects between government agencies and Big Data, and some of their findings may surprise you! In this blog post, I’ll take you through our key takeaways from the report.
These days, the shopping mall industry just can’t seem to shake the doom and gloom headlines. According to Business Insider, a “Retail Apocalypse” has officially hit America. While it’s true that declining in-store sales are forcing some retailers to close stores, those headlines don’t tell the full story. Jumping to the conclusion that it’s “the end of the world” for the modern American mall discounts their potential to adapt and thrive in today’s world. Given the socioeconomic impact that shopping centers have on our communities, we should celebrate and encourage their potential to adapt – not discourage it.
At StreetLight, we know how important shopping malls are for our communities, and we’re committed to building the tools they need to thrive. When malls and shopping centers close their doors, towns and cities don’t just lose a place to shop. People lose a place to connect to face-to-face and communities lose employers. Abandoned shopping malls can even become hotspots for petty crime. But that doesn’t have to be the future of the shopping mall.
The successful malls of the future will be those that recognize the changing desires of today’s consumers, then adapt to meet them. In this blog post, we will share a few ways for real estate and shopping center professionals to turn today’s shopping centers into the malls of the future.