Smart cities and new mobility

Before-and-after studies

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.

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.

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.

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.

Social equity

Planners in an urban area wanted to improve public transit infrastructure in underserved communities. StreetLight’s analytics helped them identify low-income neighborhoods with high potential to re-route and expand bus lines to reduce personal vehicle congestion.

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.