Predictive Vehicle and Pedestrian Traffic Flows & Patterns

Government:

City of San Diego

Category:

Data Analytics, Data Collection, Geo Services, Mobility, Digitization, Intrastructure Assessment, Public Safety, Resident Engagment

Budget:

Budget Not Determined Yet

Procurement Method:

Other
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Application Period:

October 16, 2019 - November 20, 2019

Q&A Period:

October 16, 2019 - November 6, 2019

Challenge

The Economic Development Department & Downtown San Diego Partnership is seeking a comprehensive solution to simplify vehicle and pedestrian traffic flows/patterns from existing freeway exit/entrance ramps, sidewalks/streets for our downtown community.

Background

The mission of Downtown San Diego Partnership’s “Clean & Safe” Team is to provide downtown San Diego with enhanced maintenance and safety services, including beautification efforts and a comprehensive homeless outreach program on behalf of downtown property owners. Clean & Safe’s service area covers 275 blocks of Downtown and includes the following neighborhoods: City Center, Columbia District, Cortez Hill, East Village, Gaslamp Quarter, and Marina. The cleaning and maintenance of sidewalks and streets is very labor intensive. Currently there are no adequate tools available to identify the vehicle and pedestrian traffic flow and patterns in Downtown to better access where resources/services need to be allocated. 

San Diego is installing 3,200 smart sensors/nodes, implementing what will be the world’s largest urban IoT platform. The nodes would be connected to a digital network that will collect and use real-time anonymous sensor data. These smart sensors are equipped with several features such as; thermometer, barometer, hygrometer, accelerator (3-axis), acoustical sensor. 

Requirements & Outcome

Downtown San Diego Partnership’s Clean and Safe Program seeks a software solution/mapping visualization tool that would address the following outcomes: 

  • Utilize machine learning, data analysis, or cellular data analysis to track and count vehicles from freeways exit/entrance ramps. 
  • Utilize machine learning, image recognition, or cellular data analysis to track and count pedestrian movements and flow 
  • Utilize predictive analytics to identify traffic flow patterns from vehicles entering and existing the downtown area. 
  • Analyze and identify how vehicle/pedestrian/bike flows and patterns could help with future streetscape projects.