Latest revision as of 02:01, January 30, 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.

Solutions

Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments
Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions
Deploy at a sufficient number of junctions to model air pollution on the corridor
Deploy across the city
Share what is learned about sensor performance and quality control procedures developed with other cities to improve deployments of low cost air quality sensors

Major Requirements

NA

Performance Targets

Key Performance Indicators (KPIs)	Measurement Methods
>5% reduction in the following pollutants CO, NO2, PM2.5	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, Replicability, Scalability, and Sustainability

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.

Cybersecurity and Privacy

None at this time

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.

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.

@@ Line 1: / Line 1: @@
-{{Infobox action-cluster
+{{ActionCluster
 | title             = Portland: Connected Intelligent Transportation
-| 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]
+| team              = [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]
+| leader               = [http://www.techoregon.org/who-we-are/staff/skip-newberry Skip Newberry]
-| image             = [[File:PortlandSmartTransport.png|350px]]
+| image             = PortlandSmartTransport.png
 | 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]
@@ Line 9: / Line 9: @@
 | website           = http://urban.systems
 | download          = [[media:Sensor_network_recommendation_document.pdf|Sensor Network Recommendation]]
-}}
+| description       =
-=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=
+| 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=
+| solutions         =
-#Step 1: 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
-#Step 2: 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
-#Step 3: Deploy at a sufficient number of junctions to model air pollution on the corridor
+# Deploy at a sufficient number of junctions to model air pollution on the corridor
-#Step 4: Deploy across the city
+# 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
+# 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=
+| requirements      = NA
-{| class="wikitable"
+| kpi               = >5% reduction in the following pollutants CO, NO2, PM2.5
-|Key Performance Indicators (KPIs)
+| measurement       =
-|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.
-|>5% reduction in the following pollutants CO, NO2, PM2.5
+# 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
-|Describe the methods to measure the performance/KPI impact to assess the benefits to the residents/citizens.
+| standards         =
-|-
-|
-|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
-|}
-==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=
+| replicability     =
 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=
+| cybersecurity     =
+None at this time
+| 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==
+| demonstration     =
-Deployed
+* '''Phase I Pilot/Demonstration, June 2016:''' Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments.
-=Demonstration/Deployment=
+* '''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.
-==Phase I Pilot/Demonstration June 2016==
+| supercluster        = Transportation
-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.
-[[Category:Transportation]]

Portland Connected Intelligent Transportation: Difference between revisions

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