17851928. ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS simplified abstract (Microsoft Technology Licensing, LLC)
Contents
- 1 ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS
Organization Name
Microsoft Technology Licensing, LLC
Inventor(s)
Leon Oliver Stenneth of Chicago IL (US)
Catalin Bogdan Capota of Chicago IL (US)
ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS - A simplified explanation of the abstract
This abstract first appeared for US patent application 17851928 titled 'ESTIMATING A NUMBER OF PEOPLE AT A POINT OF INTEREST USING VEHICLE SENSOR DATA AND GENERATING RELATED VISUAL INDICATIONS
Simplified Explanation
Methods and systems for estimating the number of people at a specific location using vehicle data are described in this patent application. The system receives vehicle data from multiple vehicles within a geographic boundary that includes the location of interest. From this data, a subset of information related to door sensors is extracted. Using this subset, the system determines an estimated number of people present within the geographic boundary during a specified time period. Finally, the system generates an indication of the estimated number of people at the location.
- Vehicle data is collected from multiple vehicles within a specific geographic boundary.
- The data includes information from door sensors in the vehicles.
- A subset of data related to door sensor information is extracted.
- Using this subset, the system estimates the number of people within the geographic boundary during a specified time period.
- An indication of the estimated number of people at the location is generated.
Potential Applications
This technology has potential applications in various fields, including:
- Crowd management: Estimating the number of people at events, public spaces, or tourist attractions can help authorities manage crowds more effectively and ensure safety.
- Urban planning: Understanding the population density in different areas can aid in urban planning, such as determining the need for additional infrastructure or services.
- Retail analytics: Estimating the number of people visiting a store or shopping center can provide valuable insights for retailers, helping them optimize staffing and marketing strategies.
- Transportation planning: Knowing the number of people at specific locations can assist in planning transportation routes and schedules to accommodate the demand.
Problems Solved
This technology addresses the following problems:
- Lack of accurate and real-time data on the number of people at specific locations.
- Difficulty in estimating crowd sizes and managing crowds effectively.
- Limited insights into population density and movement patterns for urban planning purposes.
- Inefficient staffing and resource allocation in retail and other service industries.
Benefits
The use of vehicle data to estimate the number of people at a location offers several benefits:
- Real-time and accurate estimation of crowd sizes, allowing for better crowd management and safety measures.
- Improved urban planning by understanding population density and movement patterns.
- Enhanced retail analytics for optimizing staffing and marketing strategies.
- Efficient transportation planning based on actual demand at specific locations.
Original Abstract Submitted
Methods and systems for estimating a number of people at a point of interest using vehicle data are provided. In some examples, vehicle data is received, from a plurality of vehicles, that corresponds to a geographic boundary encompassing a point of interest. From the vehicle data, a first subset of data corresponding to door sensor information is extracted. Based on the first subset of data, an estimated number of people within the geographic boundary, during a specified period of time, is determined. An indication corresponding to the estimated number of people within the geographic boundary is generated.
