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SATC 2014 7 July 2014

SATC 2014 7 July 2014. Uses of Automatic Number Plate Recognition (ANPR) in Traffic Management and Transport Modelling Alan Robinson Hatch Goba Alex van Niekerk SANRAL. Acknowledgements. Alex van Niekerk and SANRAL – content, ideas and use of the GFIP model access to ORT (ANPR ) data

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SATC 2014 7 July 2014

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  1. SATC 20147 July 2014 Uses of Automatic Number Plate Recognition (ANPR) in Traffic Management and Transport Modelling Alan Robinson Hatch Goba Alex van Niekerk SANRAL

  2. Acknowledgements • Alex van Niekerk and SANRAL – content, ideas and use of the GFIP model access to ORT (ANPR) data • ETC - ANPR data • Nicholas Robinson – Data mining software development

  3. Uses of Automatic Number Plate Recognition (ANPR) in Traffic Management and Transport Modelling Contents • Data sets Traditional versus Big Data • GFIP and ANPR data • Overview of the ANPR data and outputs • Trend analysis, benchmarks • Traffic management • ANPR data uses in traffic modelling • Methodology for comparing ANPR data to model OD matrices • GFIP 2015 forecast validation • Conclusions and the way forward

  4. Traditional Data versus Big Data Traditional data; manual traffic data collection i.e. RSI, household surveys, trip diaries. • Pros: All data is obtained • Cons: Small samples so questionable accuracy Relatively high cost Big data; the utopia of traffic data using technology, includes: GPS tracking, Blue tooth tracking, GSM tracking, ANPR • Pros: Very large data samples • Cons: • Not all the data is there • It may include unwanted information, • It may need to be disaggregated using traditional data of questionable accuracy

  5. The GFIP Project • Comprises: • ±185km of Urban Freeway • 42 Open Road Toll Gantries • Sections with >150 000vpd • Systems on line in January 2011 • Tolling commenced 3 Dec 2013 • ORT systems records include: • Location (gantry number) • Number plate • Time / date • Vehicle classification • All other transaction data

  6. ANPR Data • Data collected from 4 weeks 24/7 during February 2014, • Raw data comprised 63 751 618 records • Number plates replaced by unique vehicle ID • Vehicle classes: • A1 – motorcycle • A2 – light vehicle <2.5m high • B – small heavy >2.5m high < 12m long • C – large heavy > 2.5m high > 12m long Raw ANPR/ORT data Sample data courtesy SANRAL/ETC Central Operations Centre

  7. ANPR Data Manipulation • Data Miner filters by day, time and vehicle class • Output: • Classified gantry counts in 15min time periods for each vehicle class • Number of records with no number plate (1%) • 32 652 058 trip records • Start and end gantry • Start and end time • Distance start to end gantry • Gantry to gantry speed • Gantry to gantry count matrices Data Mining Software Courtesy of NT Robinson

  8. ANPR Data Outputs • Detailed Traffic Counts per Gantry

  9. ANPR Data Outputs • Speeds between Gantries by time of day

  10. TRIP Data • Entries rolled up into trips

  11. ANPR Data • Gantry to Gantry counts (“OD”),

  12. Traffic Management Opportunities • Trend analysis of flows and travel times between gantries • Daily and weekly profiles • Seasonal profiles • Abnormal days • Set benchmarks • Real-time monitoring • Comparison to benchmarks • Exception reporting and alarms • CCTV verification • ITS driver information (VMS) • Response dispatch • Time series analysis • Trend analysis • Forecasts

  13. ANPR Data Uses in Traffic Modelling • Continuous traffic counts for assignments (calibration and validation) • 24/7 traffic counts • Standard ANPR cannot classify vehicles • Vehicle classification from ORT profilers • Journey times for volume-delay functions (validation only) • Speeds between gantries (over sections of freeway) by time of day • Can be related to the traffic volumes • Need single point speed and volume measurements for volume delay function calibration • Gantry to gantry Origin – Destination (partial for validation) • 24/7 continuous data by vehicle class • Relates to trips over the entire extent of the freeway network • Only partial trip related to the freeway ANPR data is comprehensive and accurate, we must relate this data to the model’s OD trip matrices

  14. The GFIP Traffic Model • Developed using SATURN • Based on provincial model • Comprises: • ±900 traffic zones • ± 22 000 road links • 6 user classes • 5 time periods • 2006 Base Year Calibrated to : • Land use data • HHS trip distribution • Journey time data from F/W and alternative routes • >600 automatic/manual traffic counts • Design Years 2011, 2015, 2020, 2025

  15. Comparison of 2014 ANPR Data to GFIP 2015 Design Year Forecasts • Comparison of traffic counts at gantry locations • Class A2: 14% high • Class B: 24% low • Class C: 22% low • All vehicles: 12.5% high Light Vehicles: 2014 Gantry Count vs 2015 Model Forecast Heavy Vehicles: 2014 Gantry Count vs 2015 Model Forecast

  16. Comparison of 2014 ANPR Data to GFIP 2015 Design Year Forecasts • Comparison of journey times and average speeds • Northbound OK • Southbound has a section where the model is too slow

  17. How to use Gantry-to-Gantry “OD” Data • Gantry-to-gantry “OD” id not a model OD • It is a count of the trips that only pass through the selected gantries and those along the route between them, i.e. A to B, B to C or A to C passing through B. • B to C would include from zones (4, 5, 6 and 7) to (12, 13 and 14) • In the overall model each approach would comprise trips from many of the model network zones

  18. Relating OD Information • Select Link through all gantries • Select Link through each gantry • Combination highlights cell groups only related to gantry combinations • A: only through A • AB: through A and B only • ABC: through A and B and C only • Etc. We can isolate model matrix cells that correspond to ANPR gantry-to-gantry counts

  19. The Workings • = Select link matrix through all gantry locations • = Select link matrix through all gantry locations • = Select link matrix through all gantry locations • = Trips from Gantry(a) to Gantry(b) only

  20. Examples of Gantry-to-Gantry X  X  X X  X  X X Network simplified for faster computer run times

  21. Comparison of Gantry-to-Gantry Trips to Model’s OD Matrix Sectors Comparison of Class A2 morning peak hour between: ANPR gantry-to-gantry counts and GFIP 2015 Model Forecasts

  22. Conclusions • All “Big Data” is good data but each set has limitations • ANPR data from toll gantries is comprehensive 98.9% sample of classified traffic movements on a freeway system. • The data is freeway focussed; not related to the complete trip. • We can use the data to validate various aspects of a model. • We devised a methodology to compare February 2014 data to the 2015 forecast from the 2006 GFIP traffic model • We are working on the functionality to calibrate and OD matrix using this data.

  23. For more information, please visit www.hatch.ca

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