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{{Infobox | {{Infobox action-cluster | ||
| title | | title = Portland: Connected Intelligent Transportation | ||
| team-members | | team-members = [https://www.intel.com Intel Inc.], [http://www.ibigroup.com/ IBI Group], [http://www.netcityengineering.com/ NetCity], [http://urban.systems/ Urban.Systems Inc.], [https://openbike.com/ OpenBike], [https://www.dksassociates.com/ DKS], [http://www.seabourneinc.com/ Seabourne], [http://seradesign.com/ Sera Architects], [https://www.urbi.co/en/ Urbi], [http://www.ubiwhere.com/en/ Ubiwhere], [http://www.mobilitycubed.net/ Mobility Cubed], [http://www.interinnov.eu/ InterInnov], [http://www.upm.es/internacional Technical University Madrid] | ||
| | | poc = [http://www.techoregon.org/who-we-are/staff/skip-newberry Skip Newberry] | ||
| image | | image = [[File:PortlandSmartTransport.png|350px]] | ||
| imagecaption | | imagecaption = Above: Portland Skyline, Below: Powell-Division corridor | ||
| | | municipalities = [https://www.portlandoregon.gov/bps/ Portland Bureau of Planning and Sustainability], [https://en.wikipedia.org/wiki/Porto Porto Portugal] | ||
| | | status = Complete | ||
| website = http://urban.systems | | website = http://urban.systems | ||
| download | | download = [[media:Sensor_network_recommendation_document.pdf|Sensor Network Recommendation]] | ||
}} | }} | ||
=Description= | =Description= | ||
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Achieving adequate density, frequency, and precision of environmental sensor measurements to model pollution spatial distributions and the effects of mitigation strategies on pollutant concentrations with a limited budget for sensors. | Achieving adequate density, frequency, and precision of environmental sensor measurements to model pollution spatial distributions and the effects of mitigation strategies on pollutant concentrations with a limited budget for sensors. | ||
=Major Requirements= | =Major Requirements= | ||
Step 1: Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments | #Step 1: Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments | ||
Step 2: Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions | #Step 2: Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions | ||
Step 3: Deploy at a sufficient number of junctions to model air pollution on the corridor | #Step 3: Deploy at a sufficient number of junctions to model air pollution on the corridor | ||
Step 4: Deploy across the city | #Step 4: Deploy across the city | ||
Step 5: Share what is learned about sensor performance and quality control procedures developed with other cities to improve deployments of low cost air quality sensors | #Step 5: Share what is learned about sensor performance and quality control procedures developed with other cities to improve deployments of low cost air quality sensors | ||
=Performance Targets= | =Performance Targets= | ||
==Key Performance Indicators (KPIs)== | ==Key Performance Indicators (KPIs)== | ||
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Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments. | Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments. | ||
==Phase II Deployment June 2017== | ==Phase II Deployment June 2017== | ||
Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions. | Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions. | ||
Latest revision as of 07:03, January 27, 2018
| Portland: Connected Intelligent Transportation | |
|---|---|
| |
 Above: Portland Skyline, Below: Powell-Division corridor | |
| Team Members | Intel Inc., IBI Group, NetCity, Urban.Systems Inc., OpenBike, DKS, Seabourne, Sera Architects, Urbi, Ubiwhere, Mobility Cubed, InterInnov, Technical University Madrid |
| Point of Contact | Skip Newberry |
| Participating Municipalities | Portland Bureau of Planning and Sustainability, Porto Portugal |
| Status | Complete |
| Website | http://urban.systems |
| Download | Sensor Network Recommendation |
Description
This project focuses on developing a sensor-connected “smart” corridor in Portland where transit data, traffic signalization, and air quality sensing are made available in a data portal with data visualization and analytics to improve transportation options, public health, economic development and civic engagement.
Challenges
Achieving adequate density, frequency, and precision of environmental sensor measurements to model pollution spatial distributions and the effects of mitigation strategies on pollutant concentrations with a limited budget for sensors.
Major Requirements
- Step 1: Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments
- Step 2: Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions
- Step 3: Deploy at a sufficient number of junctions to model air pollution on the corridor
- Step 4: Deploy across the city
- Step 5: Share what is learned about sensor performance and quality control procedures developed with other cities to improve deployments of low cost air quality sensors
Performance Targets
Key Performance Indicators (KPIs)
>5% reduction in the following pollutants CO, NO2, PM2.5
Measurement Methods
- Describe the methods to measure the performance/KPI impact to assess the benefits to the residents/citizens.
- Use electrochemical sensors for gases. Reduce cost to <$250 per sensor.
- Test various particle counters ranging from $100- $1000 to understand the level of data quality achievable from various products on the market and how this relates to the level of data needed to achieve our performance targets
Standards/Interoperability
The project will explore the use of FIWARE, a set of tools and libraries with public and open-source specifications and interfaces. FIWARE is contributing to the International Technical Working Group on IoT-Enabled Smart City Framework launched by NIST. FIWARE brings the NGSI standard API which represents a pivot point for Interoperability and Portability of smart city applications and services.
Replicability, Scalability, and Sustainability
The FIWARE NGSI API is one of the pillars of the Open & Agile Smart Cities initiative (oascities.org), a driven-by-implementation initiative that works to address the needs from the cities avoiding vendor lock-in, comparability to benchmark performance, and easy sharing of best practices. There are currently 89 cities from 19 countries in Europe, Latin America and Asia-Pacific who have officially joined this initiative, including the city of Porto.
Impacts
There is a large and growing need for low cost environmental sensors which is emerging technology. The development and evaluation of new low cost sensor packages will support regional economic growth and provide new knowledge about sensor performance so that deployments across any city can ensure the use of sensors and better data quality to improve health and environmental quality.
Status of Commitment
Deployed
Demonstration/Deployment
Phase I Pilot/Demonstration June 2016
Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments.
Phase II Deployment June 2017
Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions.