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The Potential Use of Inexpensive Pervasive Sensors for Managing Traffic-Related Air Quality and Noise Problems

Abstract

This paper describes a novel inexpensive wireless sensor network that allows simultaneous, real-time measurement of traffic using an acoustic sensor, carbon monoxide, nitrogen dioxide, noise, temperature and humidity. The sensors are deployed onto street furniture with spacing of up to about 100m. The sensors have GPS and an accelerometer which will enable them to be deployed also in vehicles, on bicycles and carried by pedestrians so that mobile exposure measurements can be captured within the vicinity of the static sensor array. The data is temporarily logged and processed within the mote before using a licence-free, low power radio technology, known as zigbee, to pass the data from mote to mote (daisy-chaining) and on into a computer that serves as a gateway. This forwards the data, using a messaging service, to a server either by a hard-wired link or GPRS. This data is captured into a UTMC compliant real-time database along with other data from legacy systems including demand responsive control systems, meteorological conditions, roadside pollution monitors and the Automatic Urban Rural Network monitoring systems etc. Following a brief overview of the sensor system, the results of the evaluation of the accuracy of the individual sensors against precision systems will be given. The future role of the pervasive sensor technology in supporting the delivery of good air quality and informing noise action planning will be described. Their use in the evaluation of supporting the delivery of good air quality and informing noise action planning will be described. Their use in the evaluation of the implementation of traffic management measures, in validation of models and assessing trends will be demonstrated as an important contribution to informing policy, enhancing decision making and educating the public to changing life styles and attitudes to travel. This paves the way for addressing the radical changes in the need to travel, especially in single occupancy cars, if a reduction of 60% in carbon dioxide emissions is to be achieved by 2020.

Margaret C. Bell CBEScience City Professor of Transport and EnvironmentTransport operations Research Group (TORG)School of Civil Engineering and GeosciencesCassie BuildingNewcastle UniversityNewcastle upon TyneNE1 7RUTel: +44(0) 191 22 7936margaretbell@ncl.ac.ukIAPSC Conference 11th December 2008

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The Potential Use of Inexpensive Pervasive Sensors for Managing

Traffic-Related Air Quality and Noise Problems

Transport Operations Research GroupNewcastle University

Professor Margaret Bell CBEProfessor Margaret Bell CBE

IASPC 11th December 2008

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Presentation

�Background

�Pervasive mote sensor design

�Mote Calibration

�Gateshead Demonstration: Mobile Sensor Validation�Gateshead Demonstration: Mobile Sensor Validation

� Integration with Legacy Systems

�Newcastle University: Architecture for Data Flow

�Uses of Data

�Leicester Demonstration

�Summary

4

Background

�Policies

�Early decades reduced delay to traffic

�1996 Ambient air quality assessment

�2002 EU Noise Directives

�2006 Eddington Report

�2006 Stern Report�2006 Stern Report

� Intelligent Transport Systems Important Role

�Supplier of data to provide network status, validate

models and enhance technical solutions

�Control & manage traffic and demand to reduce

demand

�Deliver *Information *Education *Change Attitudes

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MESSAGE Project

�Funded by the EPSRC and DfT

�University Collaboration

�Imperial

�Newcastle�Newcastle

�Cambridge

�Southampton

�Leeds

�3 Years finishing September 2009

�e-Science

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Pervasive Sensor (MOTE) Design Strategy

�Target cost of <£200 per node for components

�Highly modular approach to design.

�Standard CPU and comms module

�Same design for mobile and fixed nodes�Same design for mobile and fixed nodes

�Add on modules for sensor payload.

�Low power consumption to reduce overhead costs

�Prototype LED 1 year @ 1minute averages

�Several scavenging power supply options to be considered.

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Chemical sensors

�CO and NO2 - principle gases to be measured.

�Semiconductor sensors or electrochemical cells only realistic options.

Electrochemical Semiconductor

Cost ~£40 <£10Cost ~£40 <£10

Operating life ~2 years 5-10 years

Power consumption Few µW 100-300mW

Performance Good sensitivity but drift & cross sensitivity issues.

Less sensitive than electrochemical

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Noise sensor module

�MESSAGE = low cost electret microphone and conditioning circuit has been designed

Electret Microphone

Pre-amplifier Weighting filter

Envelope detector

Integrator (LP filter)

CPU ADC input

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Traffic sensor module

�40kHz ultrasonic module

developed with range 5-10m

�Low cost (<£10) and low power

�Quickly/easily deployed anywhere

�30°beam width means close queued traffic may not be

discriminated

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Sensor node architecture

Central Processor

‘Zigbee’ wireless

transceiver

Real time clock

Temp sensor

3-axis accelerometer

GPS

Core modulePower Supply

transceiver

Chemical sensor module

Noise sensor module

Traffic sensor module

Other future

modules?

analog/digitalports

CO2 sensor being considered

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MOTE: Calibration and validation

� Dimensions 140 x 85 x 65mm

� IP67 weatherproof housing

� Up to 1 yr battery life from single Lithium cell.

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Housing and Electronics

0

2

4

6

8

10

0 5 10 15 20 25 30 35

Time (min)

CO

(p

pm

)

Cal

Sensor

Laboratory Calibration CO

Phase One: 5 sensors

Manufactured

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Sensor signals varied with Meteorological conditions…

Co-located sensors with Precision Precision sensors at the Kirkstall iC Site

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Effect of CO Calibration

�Strong daily cycle of temperature when removed when removed leaves mote better matching precision sensor

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Uncalibrated and Calibrated Mote Data Correlation with Precision Data

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Kirkstall Site – For CO precision versus mote data (4 motes)

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

00:00:00 02:00:00 04:00:00 06:00:00 08:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00 00:00:00

CO

(p

pm

)

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Kirkstall Site – For NO2 precision versus mote data (4 motes)

0

20

40

60

80

100

120

140

00:00:00 02:00:00 04:00:00 06:00:00 08:00:00 10:00:00 12:00:00 14:00:00 16:00:00 18:00:00 20:00:00 22:00:00 00:00:00

NO

2 (

pp

b)

�Temperature and humidity

�Cross-Sensitive to ozone

�Good correlation mote on mote

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Mote 3 vs. B&K 2260 LAeq, 333 hours~20,000 1min samplesa)1- min datab) 1- hour data

Daytime Noise Events-sirens

Very short duration night

Wind?

NoiseKirkstall Site

Very short duration night

time events

R2 = 0.85

R2 = 0.98

Goodman (2008) ITS Leeds

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On Street Validation - Simple CountNewcastle

�26 more vehicles in a count of 690

�Pedestrian �Pedestrian Interference

�Acoustic sensor not waterproof

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Off-Site (Car Park) TrialsGosforth

�10mh, 20mph 30mph at a distance 2, 2.5, 3, 3.5 metres form kerb at

�Newcastle Great Park 20º acoustic sensor

3.5 metres form kerb at different heights

�Height of 0.8m

�Shows promise

�Reflection from road surface - noisy signal-software filter

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Roving Mote - Campus

Campus Walk

0

2000

4000

6000

8000

10000

14:4

3:1

0

14:4

4:3

0

14:4

5:5

0

14:4

7:1

0

14:4

8:3

0

14:4

9:5

0

14:5

1:3

0

14:5

2:5

0

14:5

4:4

0

14:5

6:0

0

14:5

7:2

0

Po

sit

ion

(la

st

4 d

igit

s G

PS

)

Campus Walk

�Walking

�95% of time GPS fix

Roving Mote

0

100

200

300

400

500

600

14:3

6:3

0

14:3

8:1

0

14:4

0:1

0

14:4

2:2

0

14:4

4:3

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14:4

6:4

0

14:4

8:5

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14:5

1:0

0

14:5

3:1

0

14:5

5:2

0

14:5

7:3

0

14:5

9:4

0

Time (10second sampling)

Noise

Carbon monoxide

Nitrogen Dioxide

Temperature

Humidity

14:4

3:1

0

14:4

4:3

0

14:4

5:5

0

14:4

7:1

0

14:4

8:3

0

14:4

9:5

0

14:5

1:3

0

14:5

2:5

0

14:5

4:4

0

14:5

6:0

0

14:5

7:2

0

Time seconds

�Noise level mainly from rucksack movement

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Pervasive Sensor Array Demonstration Gateshead

� Carbon Monoxide

� Nitrogen Dioxide

� Temperature

� Humidity

� Acoustic Counter

� GPS

Pervasive Sensors

Mobile

Static

� GPS

� Accelerometer

� Datalogger

� Wireless communication

Mobile

UTMC Compliant Database

GATEWAY

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Field Trials of MESSAGE within Gateshead

�Static: Sensor Validation

�Mobile : Sensor Validation and measurement of

environment in- out- vehicleenvironment in- out- vehicle

� Traffic Model validation

�Information Platform

Leicester noise and air quality model validation

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Gateshead Demonstration

Phase 1

Air Quality Management Area

Micro-simulation Boundary

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MECCA

CAR PARK

TYNE BRIDGE NEWCASTLE DIRECTION

CITY COUNCIL

METRO

STATION

Pervasive sensor network

area

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�10 Street Boxes �2 NOx &AURN as benchmark

�40 Static motes �10 dynamic (cars and cycles)

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Motes fixed to street furniture

Mote on Lamp post

Mote co-located with noise meter on a tripod

GatewayHouses computer GPRS link with University Server

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Line of site problems

Gateway

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Preliminary Results

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Sensor Data Calibration

�Manufacturer’s calibration constants specific to each sensor

�Electrochemical sensors - typically sensitive to

Calibration (based on co-location at Kirkstall) is carried out in the database before storage

�Electrochemical sensors - typically sensitive to both humidity and temperature

�Manufacturers initially led us to believe that there would be minimum sensitivity to humidity

�This was not the case…

�NO2 sensor cross sensitive to ozone

Recently received funding from OneNE to purchase an environment chamber

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NO2 co-located Lychgate AURN

�No CO validation - waiting for ratified data

�AURN at 15 minutes avearging not ratified yet

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GA

TE

SH

EA

D S

UR

VE

Y

CIRCUIT 1

(mobile motes)

GA

TE

SH

EA

D S

UR

VE

Y

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GA

TE

SH

EA

D S

UR

VE

Y

CIRCUIT 2

(mobile motes)

GA

TE

SH

EA

D S

UR

VE

Y

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Survey snapshot

CO, NO2, PM and Mote CO, NO2, PM and Mote

measurement inside and outmeasurement inside and out

CO, noise and Mote CO, noise and Mote

measurement inside measurement inside

and outand out

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Communication between mobile motes

Car ParkPark

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Validation of GPS on probe vehicle

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�Passenger noted vehicle in front and any smell, visual fumes etc

�Higher inside the car

�Similar response to those outside but with a smoothed profile and time lag

Old petrol car

Pollution inside and outside vehicle

Bus

�Clipping affect in CO - feature of amplifications of chemical sensor

�Sensors configured to detect background & transient peaks from traffic

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Technologies

Integration with legacy systems

Pervasive Sensors

MESSAGE

UTMC – Basis of open architecture

Common Database

ProcessingInformation

Platform

Technologies You Do Not See

TRAFFIC, AQ,Noise, Models

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Uses of Data (1)

� Display Current Situation

� Statistical Analysis to

�Develop typical profiles

�Monitor current network status�Monitor current network status

� Mathematical modelling

�Data Mining

�Data Fusion

�Empirical relationships

�Forecasting

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Data Mart

Evaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

Evaluation

Data Mart

Evaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

EvaluationEvaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

Evaluation

Newcastle: Architecture for Data FlowStatistical Analysis -Typical Profiles, Data Mining, Reporting

Data interpretation, UTMC compliant

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Typical profile compared with real-time data - events

Filter, Verify, Calibrate sensor data, interpolation

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41

42

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Data Integration with Legacy Systems: Real Time and Historic

Historic Profile for Congestion Analysis

High smooth free flowE= f(flow ?)

Unstable flowE=f(stop and start?)

Low flowEmissions=f(Speed)?

CongestionE=f(idling)?

Street ActivityE=f(stop and start)?

Data Mining

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Uses of Data (2)

� Validation of Models

�Traffic

�Air Quality

�Noise

�Carbon Emissions (Future)

Delivers Material for Information Platforms

�Carbon Emissions (Future)

� Evaluation

�Impact of interventions – direct measurement

�Predictive capability of models- how accurate?

�Feedback to enhance decision making

�Benchmark progress of interventions

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Data Mart

Evaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

Evaluation

Data Mart

Evaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

EvaluationEvaluation

DataWarehouseStatistical processingAPPS SCENARIO

MODELS

MONITORED DATA MODELLED DATA

Evaluation

Newcastle Architecture for Data Flow

Validation of modelsAuthenticity of models

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Sensors

SCOOT

Real Time

Database

(UTMC Compliant)

QA

APPSReal Time

Event

Processing

ONLINE

MODELS

Modelled Data

Measurement of Change

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Validation of Simulation Models

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Tyne BridgeSouth Shore

Road

Hawks Road

Oakwellgate

Park Lane

VISSIM SNAPSHOT

Park Lane

Highway

Source: Gateshead City Council

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Validation of Modelled Journey Times - Gateshead

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Diurnal Variation in Noise Levels

3-D Noise modelling and mapping

� Data Intensive� Speeds, Flows, Compositions� Building, surfaces, Road &

Building surfaces etc

Model Validation Leicester Demonstration

Paul Goodman Leeds University

Anil Namdeo Newcastle University

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Leicester Demonstration

�Static sensor network - measure effects of:�School holidays�Football events�Episodes

Library

�Validate models:�Traffic� Noise�Pollutants�Predict effects�Compare with measured

College

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Summary

� Technology and Innovation fundamental to delivery of environmental solutions

� Data management, quality assurance, processing, analysis, forecasting – clever analysis

� Developing Information Platform

� Improve models and Feedback on Change

� Education and Decision Support� Education and Decision Support

� Integration Essential :

� Vehicle – Systems – People

�Monitoring – Modelling - Mapping

� Tough decisions will be needed by Governments

� Success rests with radical change of attitudes and lifestyles

� Villages, Towns, Cities will look different

However

Intelligent Transport Systems have an important role to play

52

Questions?

Margaret C. Bell CBE, PhD, BSc, CMILT, MIHT

Science City Professor of Transport and Environment

Transport Operations Research Group (TORG)

School of Civil Engineering and Geosciences Cassie

Building Newcastle University Newcastle upon Tyne

NE1 7RU

margaret.bell@ncl.ac.uk

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Acknowledgement

�EPSRC and Dft UK for funding the research presented.

�Colleagues at

�TORG, Newcastle University

�ITS, University of Leeds �ITS, University of Leeds

� Please take opportunities to reference the research presented here and consult the Reference list on the Personal webpage at Newcastle University

� http://www.ceg.ncl.ac.uk/profiles2/margaret.bell

� or

� Contact Margaret Bell directly.

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Media Launch Day

The final act of our Gateshead field trial has been to

generate some interest within the media for our work.

This has been done with two local TV news groups

filming some of the motes in action and featuringfilming some of the motes in action and featuring

interviews with some of the lead instigators of this

technology.

MEDIA VIDEO