Case Study: Pedestrian Behavior Monitoring

City of Scottsdale - Scottsdale Rd and Camelback Rd
Key Achievements:

• See the final report for reference. Go to pages 12 through 43 for the report for the City of Scottsdale. https://www.scottsdaleaz.gov/Assets/ScottsdaleAZ/Boards/Transportation/agendas-minutes/2023-Agendas/03-16-23-regular-agenda-packet.pdf
  
• To perform the pedestrian analysis, we identified counts of pedestrians and their movements at the intersection, including the crosswalk waiting area, time in the crosswalk, and assessed safety using near-miss collision analysis. It was critical to identify if pedestrians were experiencing any safety concerns. 
  
• This data helps understand the correlation between real-life crashes and near-miss collisions to help validate the overall traffic safety trends. Real-time analytics enables proactive assessment of an intersection's health and safety within a short timeframe, rather than waiting for patterns to emerge over 1 to 5 years. 
  

​Objectives/Project/Challenge:

• The City of Scottsdale Department of Transportation (DOT) wants to collect data on pedestrian counts and safety by assessing the movements on roads, sidewalks, and curbside utilization. The city can use the data and analytics to understand pedestrian safety, plan capital projects, and design planning. 
  
• Use Cases:
  
  • Pedestrian counts
    
  • Pedestrian movements
    
  • Near-miss collisions involving pedestrians
    
• Objectives
  
  • Understand the counts and risks of pedestrians, cyclists, and other mobility road users' vehicle engagement.  
    
  • Data can be used to support obtaining capital projects and development.
    

The Solution:

• The solution is out-of-the-box with a camera or LiDAR sensors; an environmental enclosure is preassembled with an IoT edge device and an LTE router with a sim card. No connection to the city’s network is required as our IoT edge devices are connected to an LTE 4G router. Sensagrate is a Verizon partner and connects to their LTE 4G network. If the 4G connection is impossible or the selected location has weak signal solutions, Sensagrate will work to find alternative solutions. 
  
• We leverage existing infrastructure to deploy SensaVision Edge on the traffic control signal, street light, or utility poles with an accessible power source for a successful deployment. SensaVision is powered through luminaires using a streetlight power adapter. To power the SensaVision Edge system through an enclosure that has 120 V AC (with two (2) outlets), and SensaVision Edge uses ~ 84V DC per deployment.
  
• For this project, Sensagrate collected data to assess pedestrian, cyclist, and vehicle movement for analysis to improve pedestrian and cyclist safety through predictive analytics and safety improvement recommendations. SensaVision Edge has an IoT monitoring and reporting system managed and maintained by Sensagrate. The IoT system is secured, and the edge device is encrypted for all data transactions. Sensagrate tracks, addresses, and supports all issues based on our Service Level Agreement to provide timely maintenance to resolve any pro
  

Results:

• During the data collection period, we counted 29,276 pedestrians in the southbound facing camera and 38,064 pedestrians in the westbound facing camera. We identified that the following days had the highest daily volume in this order. We observed that the daily traffic volume counts are the same for vehicles and cyclists. 
  
• We detected 2,664 PET near-miss collisions in the westbound (WB) camera, with 252 involving pedestrians. Of the 252 PET near-misses with pedestrians, thirty-three (33) were severe near-misses. We measure severe PET near-misses as a vehicle going above 30 miles per hour (mph) with a severity rating of 0 to 1.5. Of the 33 severe PET near-misses, nine (9) (or 0.34% of total PET near-misses) involved vehicles above 30 mph.
  
• We detected 1,830 PET near-miss collisions in the southbound (SB) camera, with 321 involving pedestrians. Of the 321 PET near-misses with pedestrians, thirty-eight (38) were severe near-misses. Of the 38 severe PET near-misses, five (5) (or 0.27% of total PET near-misses) involved vehicles with speeds above 30 mph.
  
• We detected 3,719 TTC near-miss collisions in the westbound camera, with 514 involving pedestrians. Of the 252 TTC near-misses with pedestrians, fifty-eight (58) were severe TTC near-misses. We measure severe TTC near-misses as a vehicle going above 30 miles per hour (mph) with a severity rating of 0.0 to 0.6. Of the 58 severe TTC near-misses, twelve (12) (or 0.32% of total TTC near-misses) were at vehicles above 30 mph.
  
• We detected 2,225 TTC near-miss collisions in the southbound camera, with 361 involving pedestrians. Of the 361 TTC near-misses pedestrians, forty-two (42) were severe TTC near-misses. Of the 42 severe TTC near-misses, seven (7) (or 0.27% of total TCC near-misses) were at vehicles above 30 mph. 
  
• We overlaid the tracking objects onto the field of view of cameras to assess the common and unique behaviors of pedestrians on a heat map, thereby identifying the common path areas taken by pedestrians. The color representation with more points will be red, orange, and yellow. The darker the color, the more it represents common trajectory paths. We identified that not all paths are in the crosswalk; during late times in the early morning, with no vehicles, people would cross diagonally at the intersection.
  
• We overlaid the near-miss events of PET and TCC, showing where they occur with the pedestrian trajectory data. With the trajectory paths and near-misses, you can identify locations of high-density near-misses.