Case Study: E-Scooter Monitoring and Behavior Analysis

Darryl Keeton

Founder and President of Sensagrate

In 2022, a university in Los Angeles wanted to identify the use of e-scooters on campus. The university banned e-scooters because of the challenges and issues when launched by the mobility sharing apps. 

Key ask: detecting e-scooter paths and behaviors

After pandemic of 2020 to 2021 the campus reopened and allowed e-scooters back on campus. This meant both privately owned and mobility sharing solutions. The Director of Transportation at the university wanted to identify the human behavior on campus at a major intersection. This included safety, speed, location, and movements. 

To meet these objectives Sensagrate installed its SensaVision Edge solution with camera, LiDAR and Radar sensors connected to a roadside unit (RSU) at an intersection with LTE connective to send data in real-time to the cloud and provided data and analytics through the SensaVision web page and report. 

The tracking and heats maps are a facet. It would be great to see this elsewhere on campus.
- Senior Traffic Planner​, University

For one year, Sensagrate performed analysis for e-scooter behavior monitoring and other data analytics. We found the following results:

The Results

• For over 120 days we used camera and LiDAR sensors with a 360 degree field of view and range up to 100 meters plus to detect, classify and track e-scooters, pedestrians, cyclists, and vehicles.
• We obtain counts and directions of e-scooters 24/7 (note since the site was a part of research lab the university there were gaps of days through our data collection).
  • Counts included hourly, daily, and weekly counts.
  • The university was able to understand frequency and utilization of e-scooters at a major intersection.
• We captured near-miss collisions analysis for Post-Encroachment Time (PET) and Time to Collision (TCC). The engagements included e-scooters and vehicles, e-scooters and e-scooters, e-scooters and bicyclists, and e-scooters and pedestrians. 
  • The university's transportation department was able to meet initiatives in apply solutions to meet Vision Zero. Understand near-miss collisions frequency involving all vulnerable road users, the risks, mobility objects involved, and timing allows others at the university to learn, understand and play for a smarter and safety mobility on and around campus. 
• For tracking the movements and behaviors we found e-scooters rode in the wrong direction of traffic, violated traffic signal and rightway compliances, rode between people and cars, and did not use bike paths and in some cases bike paths were not available for them.
  • Following our feedback the university implemented a bike path on the SE corner of the intersection. 

Key Takeaways

• Real-time automated counts of e-scooters, pedestrians, cyclists, and videos with a 95% accuracy counts. 
• Detection of e-scooters.
• The university gained an understand of counts and the paths e-scooters are using at a major intersection on campus. 
• The path behavior lead to a safety initiative to add a bike lane for shared multi-modal mobility. 
• The advantage of using SensaVision to perform 24/7 data collection and analysis across multiple locations on campus.
  • Expand to 5 additional sites on campus in 2024. 
  • Applying for USDOT grants to support initiatives for Vision Zero. ​

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