1. 1. BIG telematics data Vehicle tracking 2 Sources: Fleet surveillance e.g. TfL iBus data Eddie Stobbart Taxis* Insurance industry GPS and CAN/OBD link white box tracking Second-by-second (1Hz) Youngdriver bias Data anonymised * Nyhan, M., Sobolevsky, S., Kang, C., Robinson, P., Corti, A., Szell, M., Streets, D., Lu, L., Britter, R., Barrett, S., Ratti, C. 2016. Predicting vehicular emissions in high spatial resolution using pervasively measured transportation data and microscopic emissions model. Atmospheric Environment 140 (2016) 352-363. http://dx.doi.org/10.1016/j.atmosenv.2016.06.018
2. 2. BIG telematics data www.thefloow.com| insights from telematics and mass mobility analysis 3 Chapman, S. 2016. Vehicular Air Pollution: Insights from telematics and mass mobility and analysis. The Floow Ltd. Routes to Clean Air Conference, Bristol, October 2016
3. 3. BENEFITS BIG telematics data 4 Emission assessments account for local, real- driving conditions: Network-wide: No boundaries Vehicle acceleration, deceleration, cruising & idling Variability in traffic flow Month of year Day of week Hour of day Holidays Special events Weather FIGURE | Sample weekday GPS data by hour
4. 4. BACKGROUND WORK Modelling vehicle movements & emissions 5
5. 5. 0 100 200 300 400 500 0 2 0406080 S p eed(km.h -1 ) 0 100 200 300 400 500 0 1 234567 C O 2(g.sec -1 ) 0 100 200 300 400 500 0 . 000.020.040.06 Time (seconds) N O X(g.sec -1 )UNDER-PINNING ";-+ ;-."! " ; (6";& Passengercar and Heavy-duty Emission Model (Euro 0 6 / VI)FIGURES | Sample time series, TfL London Drive Cycle, Euro 5 diesel MPV Modelled_NOx O b served_NOx 0.00 0.01 0.02 0.03 0.00 0.01 0.02 0.03 Counts 1 1 2 3 5 7 11 16 23 34 51 75 111 165 244 361 535 Modelled_CO2 O b served_CO2 0 2 4 6 8 0 2 4 6 8 Counts 1 1 2 3 4 6 8 11 16 22 31 43 61 86 121 171 241 Zallinger, M., Tate, J., Hausberger, S. 2008. An instantaneous emission model for the passenger car fleet. Transport & Air Pollution conference, Graz 2008 Moody, A., Tate, J. 2017. In Service CO2 and NOX Emissions of Euro 6/VI Cars, Light- and Heavy- duty goods Vehicles in Real London driving: Taking the Road into the Laboratory. Journal of Earth Sciences and Geotechnical Engineering 7(1):51-62 01 Jan 2017.
6. 6. CASE STUDIES BIG telematics data 7 Leeds Clean Air Zone study One calendar year (May 2015 May 2016) 56,000 kms quality checked telematics data Supporting data Automatic Traffic Count (ATC) data (Leeds CC on A58M) Log special events, incidents etc. Turning proportions from 2015 traffic model (SATURN) Detailed fleet analysis from ANPR study (April 2016) Met. (wind speed, direction, temp, RH, rainfall) Sheffield City Centre One calendar year (May 2014 May 2015) 15,000 kms quality checked telematics data Supporting data
7. 7. SHEFFIELD RESULTS Variability in driver behaviour by HOUR of day 8 FIGURE | Variation in positive VSP with HOUR of the day NOTE: Vehicle Specific Power (VSP) is the sum of the engine loads (aerodynamic drag, acceleration, rolling resistance, hill climbing) divided by the mass of the vehicle
8. 8. SHEFFIELD RESULTS Variability in driver behaviour on HOLIDAYS 9 FIGURE | Variation in positive VSP with type of DAY / HOLIDAY
9. 9. SHEFFIELD RESULTS Influence WEATHER conditions 10 FIGURE | Variation in positive VSP with RAINFALL NOTE: Local, hourly weather data obtained from UK Met Office datasets FIGURE | Variation in positive VSP with TEMPERATURE
10. 10. LEEDS CLEAN AIR ZONE STUDY 2017 METHOD 11 'Raw' telematics data Temporal & Spatial variationin VEHICLE EMISSIONS DATA CLEANING Kalman filter > SPEED & ACCELERATION + GRADIENT INSTANTANEOUS EMISSION MODEL [PHEM] LINK EMISSION FACTORS (EFs) grams.km-1 all vehiclesub-types WEIGHTING & SCALING EFs by local Fleet Mix & Flow in time slices Day type School term time: - AutumnA + B - Spring A + B - Summer A + B School half-terms (all) Christmas holiday Easter holiday Summer holiday Bank holidays Special events [X, Y, Z] DATA FORMAT PHEM compatible ANPR data Fleet mix and specification Traffic Count data Automatic TIME SLICE 00:00to 06:00 36 half-hourperiods: 06:00 06:30 07:00 07:30 08:00 08:30 09:00 etc 23:30 FLEET MIX Proportionsvary by hour & week / weekend A58(M) TURNING % Output SATURN 2015 CLASSIFIED LINK FLOWS all segment IDs DIGITAL TERRAIN MAP 0.5m grid link GRADIENTS
11. 11. METHOD BIG telematics data vehicle emissions process (START) 12 'Raw' telematics data DATA CLEANING Kalman filter > SPEED & ACCELERATION + GRADIENT INSTANTANEOUS EMISSION MODEL [PHEM] Day type School term time: - AutumnA + B - Spring A + B - Summer A + B School half-terms (all) Christmas holiday Easter holiday Summer holiday Bank holidays Special events [X, Y, Z] DATA FORMAT PHEM compatible ANPR data Fleet mix and specificationTIME SLICE 00:00to 06:00 36 half-hourperiods: 06:00 06:30 07:00 07:30 08:00 08:30 09:00 etc 23:30 DIGITAL TERRAIN MAP 0.5m grid link GRADIENTS
12. 12. METHOD BIG telematics data vehicle emissions process (END) 13 Temporal & Spatial variationin VEHICLE EMISSIONS INSTANTANEOUS EMISSION MODEL [PHEM] LINK EMISSION FACTORS (EFs) grams.km-1 all vehiclesub-types WEIGHTING & SCALING EFs by local Fleet Mix & Flow in time slices ANPR data Fleet mix and specification Traffic Count data Automatic FLEET MIX Proportionsvary by hour & week / weekend A58(M) TURNING % Output SATURN 2015 CLASSIFIED LINK FLOWS all segment IDs
13. 13. SUPPORTING DATA Traffic flow on A58(M): LEEDS CC Automatic Traffic Count (ATC) site 14
14. 14. BIG telematics data How good is the data? 15 Pair contrasting North-South journeys (3 of 56,000 kms data)
15. 15. BIG telematics data How good is the data? 16 Pair contrasting North-South journeys (3 of 56,000 kms data)
16. 16. LEEDS RESULTS Passenger car NOX Emission Factors (EFs) 17 FIGURE | Average (all trajectories) passenger car NOX and NO2 Emission Factors (EFs)
17. 17. LEEDS RESULTS Passenger car NOX Emission Factors (EFs) 18 FIGURE | Passenger car NOX Emission Factors (EFs) all journeys
18. 18. LEEDS RESULTS Variation in time & space 19 FIGURE | Autumn term-time (first half) 08:00 Q >GB> Direction South Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
19. 19. LEEDS RESULTS Variation in time & space 20 FIGURE | Autumn term-time (first half) 08:00 Q >GB> Direction North Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
20. 20. LEEDS RESULTS Variation in time & space 21 FIGURE | Autumn term-time (first half) 12:00 Q 5B> Direction South Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
21. 21. LEEDS RESULTS Variation in time & space 22 FIGURE | Autumn term-time (first half) 12:00 Q 5B> Direction North Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
22. 22. LEEDS RESULTS Variation in time & space 23 FIGURE | Autumn term-time (first half) 17:00 Q 5IB> Direction South Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
23. 23. LEEDS RESULTS Variation in time & space 24 FIGURE | Autumn term-time (first half) 17:00 Q 5IB> Direction North Bound Passenger car (a) speed & (b) Euro 5 diesel car NOX Emission Factors
24. 24. WORK IN PROGRESS Leeds CAZ study 25 Key tasks: Sampling calmer driving trajectories estimate LGV, HGV & Bus trajectories Weighting & scaling time & space varying EFs by classified flow levels Clean Air Zone scenarios 'Raw' telematics data Temporal & Spatial variationin VEHICLE EMISSIONS DATA CLEANING Kalman filter > SPEED & ACCELERATION + GRADIENT INSTANTANEOUS EMISSION MODEL [PHEM] LINK EMISSION FACTORS (EFs) grams.km-1 all vehiclesub-types WEIGHTING & SCALING EFs by local Fleet Mix & Flow in time slices Day type School term time: - AutumnA + B - Spring A + B - Summer A + B School half-terms (all) Christmas holiday Easter holiday Summer holiday Bank holidays Special events [X, Y, Z] DATA FORMAT PHEM compatible ANPR data Fleet mix and specification Traffic Count data Automatic TIME SLICE 00:00to 06:00 36 half-hourperiods: 06:00 06:30 07:00 07:30 08:00 08:30 09:00 etc 23:30 FLEET MIX Proportionsvary by hour & week / weekend A58(M) TURNING % Output SATURN 2015 CLASSIFIED LINK FLOWS all segment IDs DIGITAL TERRAIN MAP 0.5m grid link GRADIENTS
25. 25. OUTLOOK BIG telematics data 26 SHORT-TERM: TargetCase Study applications Traffic management interventions Variable Speed Limits (VSL) & Smart motorways Demand management to alleviate congestion Smoothing traffic flow including ecoDriving Complex, unstable, congested networks Challenging to observe & model traffic flow e.g. Leeds Inner Ring Road LONG-TERM: Network wide, system approach Real-time fusion of telematics, fast IEM & in-situ flow monitoring All vehicle types: Buses (e.g. iBus London) and HGVs