We provide empirical data that empowers smarter cities. Across North America, StreetLight’s analytics help guide decisions about emerging technology and infrastructure for autonomous vehicles, ride-hailing services, electric cars, and more.
Covering virtually every road, sidewalk and park lane, our metrics span multiple travel modes and time periods. Use StreetLight InSight® to make decisions and “travel back in time” to determine the impact.
Following an investment in dedicated bike lanes, a city used StreetLight’s bicycle analytics to show a clear shift in off-network bike traffic to the new infrastructure, as well as an increase in bike ridership overall.
Review our multi-mode case studies for more examples of Big Data insights without the lead time and expense of physical sensors.READ CASE STUDY
Greenhouse gas emissions and VMT
A large city wanted to install electric vehicle charging stations to encourage EV use and lower vehicle emissions. StreetLight’s metrics pinpointed optimal charger locations and helped planners estimate grid load and electrical cost.LEARN MORE
New Mobility deployment
Uber is expanding its options to help create a new future of transportation. Uber Elevate used StreetLight’s data to help model eVTOL demand in Los Angeles and other target markets.
New Mobility companies may have trip data for their own service; our travel data is universal. Review the New Mobility deployment metrics helping forecast demand based on real-world travel behavior.LEARN MORE
Ride hailing and delivery
Residents complained that ride-hailing and delivery vehicles were causing congestion during peak travel times. In contrast, StreetLight analytics revealed that in some neighborhoods, high “gig driving” mode share (ride hailing and delivery vehicles as a percentage of total) actually correlated with lower congestion, directing planners to mitigate other causes.LEARN MORE
Transportation demand management
A city had growing congestion but limited room for highway expansion. StreetLight InSight® enabled analysis of hundreds of road segments to diagnose areas where shuttles and transit could have the biggest impact.READ CASE STUDY