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Sharing Regional Transportation Emissions Data
Learn how Streetlight helped the Twin Cities Metropolitan Council measure individual communities’ emissions from passenger and commercial vehicles.
Golden Gate Park Visitor Patterns
Golden Gate Park needed to count visitors through unsanctioned entrances. With StreetLight’s help, they got counts, trip origins, demographics, and more.
Identifying Opportunities for Bike-Ped Mode Shift
Trading in traditional travel demand models for StreetLight’s bike and pedestrian data, Jacobs projected mode shift for a bike-ped bridge in Portland.
Pinpointing Visitor Volume at California Park System
To balance public access and its environmental impact, the Nature Reserve of Orange County turned to StreetLight for accurate park visitor data.
Cost-Effective Congestion Analysis for Fort Lauderdale
Saving money over pricey bluetooth or licence plate analysis, Kittelson identified the surprising source of congestion on Ft. Lauderdale’s main corridor.
Tulsa MPO Fills Traffic Count Gaps
When jurisdictions in an Oklahoma MPO’s regions stopped collecting counts, StreetLight provided complete and reliable AADT metrics for the entire area.
Delivering Boston’s Ride-Hailing Metrics
Learn how StreetLight’s analysis confirmed that ride-hailing vehicles contribute substantially to congestion, Download now to learn more about identifying ride-hailing and delivery travel.
Choosing Locations for Uber Air
Download our case study to learn what metrics Uber Air used to optimize site locations, how many trips per day Uber Air predicts for demand, and how many terminals it needs to meet that demand
Football Hall of Fame Projects Tourism Explosion
The Pro Football Hall of Fame projected that annual visitors would jump from 300,000 to three million. To plan transportation for this tourism boost, planners turned to Big Data StreetLight for facts about where visitors came from, traffic hot spots, and parking options.
Virginia Bike Tourism: Measuring Economic Impact
Planners knew that tourists visited the Virginia Capital Trail, but they didn’t have metrics for measuring bicycle tourism’s economic impact. They turned to Big Data for detailed information about bike and pedestrian trips.
Detailed Truck Data for Virginia’s Port
To support transportation project prioritization, ongoing studies, and federal funding applications, Port of Virginia planners needed to know which routes were commonly used by port trucking. Learn how Big Data helped solve this transportation problem.
Multimodal Planning in the City of Toronto
Analytics informed planning studies for “Gateway Mobility Hubs” that will provide convenient, affordable, multimodal modes and improve connections to transit options in Toronto’s outskirts.
Transportation Demand Management in Virginia
Planners analyzed hundreds of congested road segments to diagnose areas where certain techniques may have the biggest impact and to guide planning.
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