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Using a Virtual Reality Walking Simulator to Investigate Pedestrian Behavior
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Performance measures and input uncertainty for pedestrian crossing exposure estimates.

Craig Milligan1, Rob Poapst, Jeannette Montufar

  • 1University of Manitoba Department of Civil Engineering, E1-327 EITC, 15 Gillson Street, Winnipeg, MB, Canada. milligan.craig.a@gmail.com

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Estimating annual pedestrian exposure is crucial for safety measures. Using local vehicle counts to expand short-term pedestrian data yields more accurate annual exposure estimates than using external pedestrian data, reducing uncertainty.

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Area of Science:

  • Transportation Engineering
  • Traffic Safety
  • Urban Planning

Background:

  • Pedestrian safety performance measures rely on annual crossing exposure estimates.
  • Significant uncertainty exists regarding the accuracy of these exposure estimates.
  • Limited information is available on methods to improve the temporal expansion of short-term pedestrian counts.

Purpose of the Study:

  • To compare the effectiveness of different temporal information sources for expanding short-term pedestrian counts into annual estimates.
  • To quantify the uncertainty associated with pedestrian exposure estimates derived from various expansion methods.
  • To identify the most reliable method for improving pedestrian exposure estimation for safety analysis.

Main Methods:

  • Utilized a 12-month pedestrian flow database with over 350,000 observations as a reference.
  • Compared two temporal information sources: a composite of pedestrian counts from other cities and local vehicle counts.
  • Expanded short-term pedestrian counts using both sources to generate 200 annual estimates and compared them to the reference volume.

Main Results:

  • Temporal patterns derived from local vehicle counts more closely matched observed pedestrian patterns than external composite pedestrian patterns.
  • Exposure estimates expanded using local vehicle factors showed significantly lower errors (mean: -2%, median: -3%, std dev: 33%) compared to those using external composite pedestrian patterns (mean: 27%, median: 9%, std dev: 73%).
  • Local vehicle count expansion resulted in 90% of errors falling between -53% and 50%, while external composite data had errors between -62% and 170%.

Conclusions:

  • Local vehicle counts are a more reliable predictor for expanding short-term pedestrian data to estimate annual exposure.
  • Improved methods for obtaining pedestrian exposure estimates, particularly using local traffic data, can increase confidence in transportation safety performance measures.
  • Further research into refining short-term count expansion techniques is warranted to enhance the accuracy of pedestrian safety assessments.