Sidewalk Labs KC Region Pilot
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February 21, 2018 presentation at KC Digital Drive Mobility Innovation Team meeting, Jim Hubbell, Mid-America Regional Council
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Sidewalk Labs KC Region Pilot
- 1. Sidewalk Labs KC Region Pilot Mobility Innovation Team
- 2. Outline Regional transportation plan Travel behavior data Traditional sources BIG data Sidewalk Labs pilot
- 3. Regional transportation plan Transportation Outlook 2040 goals: Economic vitality Transportation choices Equity Place making Safety and Security System Efficiency System Condition Public Health Environment Climate Change and Energy Use
- 4. Travel behavior data What? Where? How? Who? Why?
- 6. Traditional sources Pros Cons
- 7. Big data
- 8. Sidewalk Labs pilot BikeWalk KC Kansas City, MO KC Streetcar KCATA MARC Olathe, KS Overland Park, KS
- 9. REPLICA Typical weekday (in a quarter) Synthetic activity model Mobile location data No personal data Maps interface
Hinweis der Redaktion
- Build and reflect regional consensus about goals and investments. Incorporate public engagement. Incorporate land-use considerations. Include performance measures. Include forecasts of future travel. Include a list of financially constrained projects. Analyze environmental justice and social equity. Have a 20+ year horizon and be updated every 5 years.
- What – what is being transported? What path is it taking? Where – where is it coming from, going to, and where might it stop on the way? What is the path? How – How is it being transported (i.e. what means?) How much? Who – Who are these people? What are the characteristics of people/goods, how many, etc. Why – Why is travel ocurring? Work? Recreation? Shopping? Entertainment? Why was a particular mode chosen (or not chosen)? Why was a particular route taken?
- Sensors collect data on how much, mode, sometimes where… Surveys collect data on the where, how, who and why, include Census and household travel surveys
- Pros – science of travel surveys has evolved over 50 years; control over sampling frame, design, etc; very high confidence in HHS methods Cons – often data sources provide only a piece of the puzzle; traditional surveys are expensive and therefore can only be done periodically
- Wireless tech (incl. GPS), smartphones and computing power have all contributed to the rise of big data There have been interim technologies that involve sensors interacting with these technologies (ex. roadside Bluetooth) Transition from active to passive data collection Big data has pros/cons, too Tradeoffs between quantity and quality Still in the R&D stage for transportation behavior data (proving itself)
- 1-year pilot Coming in late March 2018 (through early 2019)