Mobility Sensing and Data Analytics for Smart Cities

By Kade Townsend
Slide 1: Title slide presenting Mobility Sensing and Data Analytics for Smart Cities

Slide-1

Kade Townsend

Dr. Jason Hallstrom and Dr. Jiannan Zhai

Slide 2: Background information covering smart cities, mobility sensing, economic development, and service optimization

Slide-2

BACKGROUND INFORMATION

  • Smart Cities
  • Mobility Sensing
  • Economic Development
  • Service Optimization
Slide 3: MobIntel system overview showing how it works and challenges faced

Slide-3

MobIntel

How it works

  • Sensors
  • MAC Address
  • RSSI
  • Privacy-First

Challenges

  • Unchecked Data
  • Loss of Power
Slide 4: Project goals including power determination, forecasting, data verification, comparison with Google Maps, and data description

Slide-4

PROJECT GOALS

Describe Data

Seaborn and Matplotlib

Verify Data

Compare with Google Maps Popular Times and Sensor Correlation

Determine Sensor Power

Trendline Forecasting

Slide 5: Bar chart showing probe counts per day of week with total average data

Slide-5

PROBE COUNTS PER DAY OF WEEK

Total and Average

This chart displays probe count data averaged across different days of the week, showing patterns in mobility sensing data collection by day.

Slide 6: Line graph showing probe counts per day over time with average trend

Slide-6

PROBE COUNTS PER DAY

Average

This line graph shows the daily variation in probe counts over time, displaying the average trend of mobility sensing data collection on a daily basis.

Slide 7: Bar chart displaying probe counts per hour of day showing total average hourly patterns

Slide-7

PROBE COUNTS PER HOUR

Total and Average

This chart shows the hourly distribution of probe counts throughout a 24-hour period, revealing patterns in mobility activity by hour of day.

Slide 8: a line graph centered on a dark background with a green border

Slide-8

The image shows a line graph centered on a dark background with a green border. The x-axis is labeled "Hour" with ticks from 0 to 20. The y-axis has no label. A single blue line traces a curve on the graph, starting high on the left, dipping down to a low point around the 10-hour mark, and rising again towards the right. To the right of the graph are three blue triangles pointing to the right. The lower-left corner of the image contains three wavy, white horizontal lines. The bottom-right corner shows a small white shape with diagonal lines.

Slide 9: Sensor correlation analysis with multiple data visualization charts

Slide-9

SENSOR CORRELATION

This slide presents correlation analysis between different sensors, showing relationships and patterns in the mobility sensing data through multiple visualization charts and correlation matrices.

Slide 10: Google Maps Popular Times comparison data and analysis

Slide-10

GOOGLE MAPS POPULAR TIMES

This slide shows comparison analysis between the mobility sensing data and Google Maps Popular Times data, used for validation and verification of the collected sensor data.

Slide 11: Trendline forecasting methodology showing quadratic and linear approaches

Slide-11

TRENDLINE FORECASTING

Calculating next value from trendline of previous data points

LINEAR

  • First-Order

QUADRATIC

  • Second-Order
Slide 12: Forecasting results showing linear and quadratic model performance with on/off states

Slide-12

Forecasting Results

LINEAR

OFF and OFF

Two scatter plot graphs showing linear model performance with off/off states

Slide 13: Forecasting results showing linear and quadratic model performance with on/off states

Slide-13

Forecasting Results

QUADRATIC

ON and OFF

Two scatter plot graphs showing quadratic forecasting models, with indicators showing which models are active (ON) or inactive (OFF) under different conditions.

Slide 14: Future project goals including data verification and machine learning applications

Slide-14

FUTURE PROJECT GOALS

Machine Learning

Verify More Data

Slide 15: Acknowledgments thanking supporting institutions

Slide-15

Thanks

Southwestern University

Florida Atlantic University

National Science Foundation

Slide 16: Credits

Slide-16

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For a downloadable version of this presentation, email: I-SENSE@FAU.

Additional Information
The Institute for Sensing and Embedded Network Systems Engineering (I-SENSE) was established in early 2015 to coordinate university-wide activities in the Sensing and Smart Systems pillar of FAU’s Strategic Plan for the Race to Excellence.
Address
Florida Atlantic University
777 Glades Road
Boca Raton, FL 33431
i-sense@fau.edu