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Enhancements to HIOCC: Addressing False Alerts and Detection Speed through Simulation

This overview focuses on the developments made to the HIOCC system aimed at mitigating identified problems, such as false alerts due to Heavy Goods Vehicles (HGVs) and challenges caused by low traffic flow. By integrating individual vehicle speed data and introducing the HIOCC2 algorithm, these enhancements seek to improve detection performance without altering the basic HIOCC algorithm. A detailed simulation tool has been developed to visualize performance under various traffic conditions and to facilitate testing of different parameter settings, ultimately aiming to reduce false alarms and improve alert clearance times.

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Enhancements to HIOCC: Addressing False Alerts and Detection Speed through Simulation

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Presentation Transcript

  1. HIOCC Developments SEA HA

  2. overview • problems with HIOCC • developments to address problems • simulation using Individual Vehicle data • demonstration of simulator tool

  3. HIOCC problems Key: • false alerts due to HGVs • long clearance time under low flow • poor detection during night Additional: • choosing appropriate parameter settings • control of triggering speed

  4. understanding false alarms / detection • speed-length ambiguity since using only occupancy data • 2.0s occupancy = 4m car at 6.7mph • 2.0s occupancy = 16m HGV at 20.1mph • no synchronisation between arrival of vehicle at detector and start of instantaneous occupancy measuring interval • for occupancy times 2.0s-2.8s, only some vehicles will trigger • 2.9s needed to guarantee alert  slower detection speed

  5. occupancy time vs. speed

  6. detection uncertainty

  7. detection regions green = no alert possiblealert red = alert

  8. detection regions green = no alert possiblealert red = alert length distribution (M25 site 4762, 4 May 2002 11:19-18:28)

  9. clearance under low flow • caused by suspension of smoothed occupancy calculation • feature intended to prevent alert fluctuation in stop-go traffic, but cannot distinguish from case of no vehicles after (false) alert in very low flow  alert does not clear on otherwise empty lane

  10. development of HIOCC2 • make use of individual vehicle speed data • trap and suppress false alerts raised by HIOCC • doesn’t modify basic HIOCC algorithm • maintain detection performance • tried and tested • HIOCC2 easily switched out to revert to standard HIOCC • robustness to missing/outlier speed data • robustness to single loop failure

  11. HIOCC2 detection regions

  12. HIOCC2 architecture

  13. HIOCC2 key points • Watchdog suppresses HIOCC alert if speed > threshold  removes false alerts • smoothed speed for clearance • clear only after n vehicles passed  tolerance to outlier speed • force HIOCC to clear if smoothed occupancy calculation suspended • Watchdog output forced high if loop fails  revert to HIOCC • data synchronisation to align occupancy and speed data

  14. HIOCC2 issues • algorithm introduces 3 second delay • robust detection in case of vehicle stopping on first loop but not reaching second • to ensure suppression of long vehicle false alarms • potential for using the time for pre-processing to improve HIOCC detection • significant reduction in false alarm rate • detection performance of HIOCC not changed • threshold speed controllable

  15. improving HIOCC detection • aim to remove the uncertain detection region through pre-processing occupancy • overcome the problem of synchronisation • features : • raise detection speed • self-contained, no modification to HIOCC • imposes 1 second delay 6kmh (1.68m)

  16. pre-processing detection regions

  17. simulation length/speed headway length/speed • simulation of HIOCC based on logged IVD data • software tool developed to simulate HIOCC/HIOCC2 • step through second-by-second and view/plot internal variables, inputs, outputs • measure overall performance • automatically test different parameter combinations • read IVD logged from different Outstations:[Peek, Golden River, Siemens]

  18. benefits • open up ‘black-box’ to understand / visualise operation in different traffic conditions • test algorithm on specifically generated test examples and situations • ‘what if’ testing • rapidly and automatically try out different parameters

  19. dataset from M25 • dataset collated from M25 Individual Vehicle Data (May 2002) • examples of incident / non-incident / HIOCC false alerts for use in the simulator • different location types: hill, junction, normal • different traffic flows

  20. potential development Get as much as possible from existing equipment. • aim to improve control and allow higher triggering speed • iterate HIOCC at faster rate (e.g. every 0.5s)

  21. summary • developments have been carried out that: • address problems of HIOCC • keep the underlying HIOCC algorithm, which is always used as a failsafe fallback • simulation tool now available

  22. improved

  23. current

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