By clicking “Accept All Cookies”, you agree to the storing of cookies on your device to enhance site navigation, analyze site usage, and assist in our marketing efforts. View our Privacy Policy for more information.
PreferencesDenyAccept

Mobility as a Service (MaaS)

Watch Video
Industry:
Mobility as a Service (MaaS)
Try Demo

Ride-hailing and ride-sharing services are replacing privately-owned automobiles as Mobility as a Service (MaaS) leads a new economy fueled not by vehicle manufacturing, but by vehicle telematics data. Iconic automotive brands and visionary shared mobility start-ups turn to HEAVY.AI to drive new carsharing telematics data use cases and gain competitive insights from big data mobility analytics in the Mobility Industry.

  • 21% of Americans claim the availability of shared mobility has allowed them to delay or avoid purchasing a car
    ‍
  • 63% percent of Americans expect to increase their use of ride-hailing services in the next two years
    ‍
  • In 2015, 15% of Americans used ride-sharing services; in 2018, that number grew to 43%

Whitepaper: Find Opportunity & Risk Hidden in Your Enterprise

Make time-sensitive, high-impact decisions with Big Data and HEAVY.AI

Download

Visualize and Analyze Shared Mobility Fleet Telematics Data in Milliseconds to Optimize Ride-Sharing Services

Through HEAVY.AI's accelerated analytics, data scientists, analysts, and policy-makers can now instantly visualize and analyze tremendous volumes of telematics data streaming from automobiles, mass transit, bikes, scooters, and beyond. This unlocks a wealth of new business and government use cases to capture the value from vehicle telematics data streams, optimize transportation and logistics, and improve city planning and shared mobility infrastructure.

  • Visualize millions of carsharing, e-hailing, bikesharing and delivery geo points to identify congestion and optimize safe new pick-up and drop-off zones
  • Improve urban mobility and reduce automobile traffic by layering disparate automobile, bike, and scooter telematics datasets to identify new opportunities for microtransit infrastructure
  • Optimize deployment of shared mobility fleets, track usage, and increase uptime through preventative maintenance
  • Derive spatiotemporal insights from customer behavior and discover relationships between destinations, driving habits, and infotainment preferences

How BMW Visualizes & Interacts with Extreme Datasets with Near Zero Latency

NVIDIA GTC 2019 Presentation

Mobility as a Service (MaaS)

Use Cases

Public Sector Fleet Management
Learn More
Understand Driver Behavior Data in Automotive
Learn More