Portland Connected Intelligent Transportation - Revision history

imported>Wpinfold at 02:01, January 30, 2018

2018-01-30T02:01:53Z

← Older revision		Revision as of 02:01, January 30, 2018
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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.
	\| solutions =		\| 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
			\| requirements = NA
	\| kpi = >5% reduction in the following pollutants CO, NO2, PM2.5		\| kpi = >5% reduction in the following pollutants CO, NO2, PM2.5
	\| measurement =		\| measurement =

imported>Wpinfold at 01:04, January 30, 2018

2018-01-30T01:04:48Z

← Older revision		Revision as of 01:04, January 30, 2018
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	{{~~Infobox action-cluster~~		{{ActionCluster
	\| title = Portland: Connected Intelligent Transportation		\| 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		\| 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]		\| municipalities = [https://www.portlandoregon.gov/bps/ Portland Bureau of Planning and Sustainability], [https://en.wikipedia.org/wiki/Porto Porto Portugal]
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	\| website = http://urban.systems		\| website = http://urban.systems
	\| download = [[media:Sensor_network_recommendation_document.pdf\|Sensor Network Recommendation]]		\| 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.		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.		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 =
	~~=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
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	#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
			\| kpi = >5% reduction in the following pollutants CO, NO2, PM2.5
	~~=Performance Targets=~~		\| measurement =
	{\| ~~class~~=~~"wikitable" style="margin: auto; width: 95%;"~~
	~~!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.		# 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.		# 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		# 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 =

	~~=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.		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.		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.		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=~~		\| demonstration =
	{\| ~~class="wikitable" style~~=~~"margin: auto; width: 95%;"~~		* '''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.
	Phase I		\| supercluster = Transportation
	Pilot/Demonstration		}}
	June 2016
	!
	~~Phase II~~
	~~Deployment~~
	~~June 2017~~
	\|-
	\|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.
	\|}


	~~[[Category:Transportation]]~~

imported>Wpinfold at 11:00, January 27, 2018

2018-01-27T11:00:14Z

← Older revision		Revision as of 11:00, January 27, 2018
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	=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.		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
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	#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=
	{\| class="wikitable" style="margin: auto; width: 95%;"		{\| class="wikitable" style="margin: auto; width: 95%;"

imported>Wpinfold at 06:48, January 22, 2018

2018-01-22T06:48:39Z

← Older revision		Revision as of 06:48, January 22, 2018
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	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.		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=		=Demonstration/Deployment=
	==Phase I Pilot/Demonstration June 2016==		{\| class="wikitable" style="margin: auto; width: 95%;"
	Calibrate air quality sensors by deploying at one intersection and co-locating with high quality reference instruments.		!
	~~==Phase II Deployment June 2017==~~		Phase I
	Evaluate density, frequency, and precision of sensor measurements to secure sufficient data quality to model particulate and gaseous pollutant distributions.		Pilot/Demonstration
			June 2016
			!
			Phase II
			Deployment
			June 2017
			\|-
			\|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.
			\|}


	[[Category:Transportation]]		[[Category:Transportation]]

imported>Wpinfold: /* Performance Targets */

2018-01-22T06:44:19Z

Performance Targets

← Older revision		Revision as of 06:44, January 22, 2018
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	#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=
	{\| class="wikitable" style="margin: auto; width: 85%;"		{\| class="wikitable" style="margin: auto; width: 95%;"
	!Key Performance Indicators (KPIs)		!Key Performance Indicators (KPIs)
	!Measurement Methods		!Measurement Methods

imported>Wpinfold: /* Performance Targets */

2018-01-22T06:43:43Z

Performance Targets

← Older revision		Revision as of 06:43, January 22, 2018
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	#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=
	{\| class="wikitable" style="margin: auto;"		{\| class="wikitable" style="margin: auto; width: 85%;"
	!Key Performance Indicators (KPIs)		!Key Performance Indicators (KPIs)
	!Measurement Methods		!Measurement Methods

imported>Wpinfold: /* Performance Targets */

2018-01-22T06:41:54Z

Performance Targets

← Older revision		Revision as of 06:41, January 22, 2018
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	#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=
	{\| class="wikitable"		{\| class="wikitable" style="margin: auto;"
	!Key Performance Indicators (KPIs)		!Key Performance Indicators (KPIs)
	!Measurement Methods		!Measurement Methods

imported>Wpinfold: /* Performance Targets */

2018-01-22T06:39:14Z

Performance Targets

← Older revision		Revision as of 06:39, January 22, 2018
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	=Performance Targets=		=Performance Targets=
	{\| class="wikitable"		{\| class="wikitable"
	\|Key Performance Indicators (KPIs)		!Key Performance Indicators (KPIs)
	\|Measurement Methods		!Measurement Methods
	\|-		\|-
	\|>5% reduction in the following pollutants CO, NO2, PM2.5		\|>5% reduction in the following pollutants CO, NO2, PM2.5

imported>Wpinfold at 06:37, January 22, 2018

2018-01-22T06:37:33Z

← Older revision		Revision as of 06:37, January 22, 2018
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	=Impacts=		=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.		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=		=Demonstration/Deployment=
	==Phase I Pilot/Demonstration June 2016==		==Phase I Pilot/Demonstration June 2016==

imported>Wpinfold: /* Performance Targets */

2018-01-22T06:36:03Z

Performance Targets

← Older revision		Revision as of 06:36, January 22, 2018
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	# 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		# 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=		=Standards/Interoperability=