Sense, Report, Act
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This document outlines a plan for a smart city collaboration between a Business Improvement District and various partners to improve public space waste management using sensors, data analysis, and citizen reporting. The plan involves installing trash bin sensors to provide real-time fill level data, collecting micro-reports from an app, analyzing the data to predict waste levels and optimize routes, and establishing metrics to evaluate the results with goals of reducing labor/costs and improving cleanliness perceptions.
Sense, Report, Act
- 1. Sense, Report, Act, and Refine: A Smart City Collabora8on
- 2. • Rou8ne opera8ons • Surge condi8ons • Situa8onal awareness • Mul8-‐objec8ve resource op8miza8on • Service Rou8ng • Changing uses Management Expecta8ons • Clean • Safe • Walkable • Reliable opera8ons • Special events • Livability • Healthy environment • Fresh food • Web sources and blogs • Real8me informa8on (traffic, weather, transit, …) • Ci8zen par8cipa8on vs Our Challenge
- 3. The relevant Sustainable DC goals to this project include: • Develop a Zero Waste plan for the city • Ban polystyrene from the city • Decrease all citywide waste streams • Increase recycling bins in public realm • Coordina8ng a city-‐wide educa8on programs The Sustainable DC Plan, developed with extensive ci8zen input, established 2032 goals and ac8ons designed to make Washington DC the most sustainable city in North America. The 2016 BID management goals include: • More efficient use of labor and equipment • Ability to adapt to address special event impacts • Reducing contamina8on in recycling bins • Enhancing public percep8ons regarding cleanliness • Effec8ve use of technology and data How to Improve Public Space Waste Management Our Goals
- 4. Real-‐8me Trash Situa8ons from Sensors and Micro-‐Reports Trash Bin Sensors Data Micro Reports from Krumbs Filter Aggregate Filter Real-‐Time Trash Fill Level Situa8on in EventShop
- 5. Predic8on based on Events History Events Data Real-‐Time Trash Fill Level Situa8on 5 32 Now Predicted Trash Fill Level in 30 minutes at a given loca8on 45 95 30 minutes 10 30 40 70 90 100 20 0 20 40 60 80 100 120 7:30 8:00 8:30 9:00 9:30 10:00 10:30 Projected Trash Fill Level at a given loca8on based on Event History 0 35 50 90 0 20 40 60 80 100 7:30 8:00 8:30 9:00 9:30 10:00 10:30 Real-‐Time Fill Level Situa8ons at a given loca8on of an event
- 6. Situa8on Recogni8on Model in ES OP OP OP OP OP DS DS DS Situa8on
- 7. DowntownDC BID’s plans have so far focused on: • Refining routes for personnel and equipment • Gaining an understanding of the 8ming of services • Cataloging major events and ac8vi8es • Mapping all public space elements in GIS This project will enable the BID to effec8vely use data, technology and analy8cs to: Micro-‐reports Event data GIS data Route informa8on Sense, Report and Refine Our Approach
- 8. Timeline and KPI’s April -‐ June 2016 – Install sensors, established baselines, develop incen8ves, and determine metrics. Ini8al prototype. June – October 2016 Prototype system and evalua8on for trash management, Evalua8on for scaling efforts and incorpora8on into other services November -‐ March 2017: Review program and revise, create winter services plan, develop city-‐wide expansion plan, April -‐ June 2017 Scaled effort and KPI evalua8on • Reduc8on in 8me spent servicing bins • Par8cipa8on in Micro Repor8ng • Reduc8on in Recycling Contamina8on • Improved percep8ons of downtown cleanliness • Enhanced reputa8on of BID services
- 9. Our Team ScoH Pomeroy Bill Updike Dr. Dhananjay Anand Dr. Eswaran Subrahmanian Dr. Ramesh Jain Dr. Asquith Bailey Marvin Kau DowntownDC Business Improvement District DC Department of Energy and Environment Na8onal Ins8tute for Standards and Technologies Carnegie Melon University/NIST University of California Irvine Krumbs Enevo