Low cost sensors- applications for Intelligent Transport Systems

Transcription

1 Low cost sensors- applications for Intelligent Transport Systems Gustavo Olivares National Institute of Water & Atmospheric Research Auckland, New Zealand

2 Air quality and ITS Sensors Data Impact Assessment Feedback & Management

3 Sensors Low to medium cost ~$100-$1000 per unit Non-regulatory Highly integrated Accuracy Dust/Smoke: Low to Med Gases: Low Sensitivity: Low

4 Particulate Matter

5 Gases

6 Why sensing (and ITS) is more than just monitoring Monitoring observe integrated system outcome Sensing observe system dynamics (responses to change) Sensors form an observing network Changes emphasis from performance/capability of one sensor to that of the whole network, especially after fusion of data from other sources (NEC, Google, weather model, etc)

7 Some potential/hypothetical use cases CBD clean air zones Congestion charging based on current air quality. Dynamic traffic management to divert polluting vehicles away from clean air zone. Emission models/inventories, fleet/technology evaluation Real-time emission inventory tracking Commercial/municipal fleet management (fuel consumption, emission management) Dieselgate Tunnel portal/stack management & control Cost effective operation of infrastructure responding to ambient concentrations Vehicle ventilation control Automatic vehicle cabin ventilation management responding to in/out-car air quality (road tunnels, CBD, etc)

8 Case Study RESCATAME (Salamanca, Spain) Description This project aimed at provide information for sustainable traffic management through the use of pervasive air quality sensors and prediction models The main objective of this project was the development of the sensors and network infrastructure. Project finished in 2012 Website no longer live Results Sensors developed and now commercially available through Libelium. Short term (hours) forecast of air quality for individual sensors. Decision support system trialled for Salamanca but no evidence of continued use. Shortcomings Ongoing quality issues with sensors from Libelium and lack of clarity on the assessment models developed.

9 Case Study ICT-Emissions Description Model based project that combines traffic and emission modelling at micro and macro scales and offers the necessary interfaces to shift from one scale to the other focusing on fuel consumption and traffic management. Traffic models validated in studies in Turin, Madrid and Rome. Results CO2 emissions can be quantified using traffic modelling tools and improvements on vehicle and fleet performance can be achieved by using traffic flow management measures Shortcomings No real-time air quality measurements and only focusing on fuel consumption (CO2 emissions) outcomes.

10 In NZ GW TRAP monitoring Targeted to diesel emissions Quantify the impact of changes in the public transport fleet in wellington BC, NO2, PM

11 In NZ ROAD High density NO2 passive samplers network. Before-After Adelaide Rd redevelopment Including indoor measurements.

12 ROAD - Transects A = King Street B = Drummond Street C = Hospital Road

13 In NZ POET POET Performance assessment of ODIN to Emissions from Traffic Victoria Park flyover. December 2016 February 2017

14 POET New Year s Eve

15 POET After Jan 8 th

16 The sensitivity challenge Typical roadside increment for PM10: up to 2 µg m-3 Typical accuracy of Plantower PM10 sensor: 0 5 µg m-3 Typical night-time variation in winter PM10 in Rangiora: µg m3 Typical roadside increment for NO2: up to 25 µg m-3 Typical accuracy of Alphasense NO2 sensor: µg m-3 Typical accuracy of SPEC NO2 sensor:??? µg m-3

17 Gases

18 Key Points Sensors are not instruments and low-cost sensors don t necessarily translate in cheap information. The power of low cost sensors is that it enables large networks. We re still waiting for the cheap NO2 sensor but could settle for a cheap particle counter though.