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Temporal Aggregation Effects on ITS Data Applications

This report by Alex Bigazzi from Portland State University examines the impacts of time-aggregating data on sustainability performance measures in intelligent transportation systems (ITS). Utilizing 24-hour data collected over five weeks on a 2-mile stretch of M4 in London, the study analyzes individual arrival times and speeds to explore how aggregated data affects speed distributions, travel time estimation, and related metrics. The findings highlight potential underestimations of emissions and fuel consumption due to speed consolidation and other sampling errors, while discussing future implications for data management in sustainable transportation.

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Temporal Aggregation Effects on ITS Data Applications

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  1. Temporal Aggregation Effects on ITS Data Applications Sustainable transportation performance measures Alex Bigazzi Portland State University ITS Data Aggregation Effects – Alex Bigazzi

  2. Objective • Effects of time-aggregating data on sustainability performance measures ITS Data Aggregation Effects – Alex Bigazzi

  3. Data Source • ~2 miles of M4 in London • Individual arrival times and speeds • 24hr data over 5 weeks (1998) • 500 meter spacing ITS Data Aggregation Effects – Alex Bigazzi

  4. and effects on related measures Speed Distributions ITS Data Aggregation Effects – Alex Bigazzi

  5. Speed Consolidation 11-16-98, station 172 ITS Data Aggregation Effects – Alex Bigazzi

  6. ITS Data Aggregation Effects – Alex Bigazzi

  7. Travel Time ITS Data Aggregation Effects – Alex Bigazzi

  8. Endpoint method of travel time estimation ITS Data Aggregation Effects – Alex Bigazzi

  9. Sampling Error – neglected here x Sampling Error Detector Speed-Estimated Trajectory True Trajectory Travel Detector t ITS Data Aggregation Effects – Alex Bigazzi

  10. Grouped Speed Error x Grouped Speed Error Detector Individual Speed Trajectories Travel Average Speed Trajectory Detector t ITS Data Aggregation Effects – Alex Bigazzi

  11. Harmonic Mean Speed (mph) Arithmetic Mean Speed (mph) 1 week of speeds ITS Data Aggregation Effects – Alex Bigazzi

  12. Estimate space mean speed from time mean speed Rearranging: Ref: Lindveld and Thijs ITS Data Aggregation Effects – Alex Bigazzi

  13. 1min data (one day, one station) CV CV ITS Data Aggregation Effects – Alex Bigazzi

  14. ITS Data Aggregation Effects – Alex Bigazzi

  15. Combined effects of speed distribution and travel time errors Delay ITS Data Aggregation Effects – Alex Bigazzi

  16. 80 Speed FFS 60 40 20 0 16:00 20:00 4:00 8:00 12:00 ITS Data Aggregation Effects – Alex Bigazzi

  17. One week of delay (all stations) Disaggregate ITS Data Aggregation Effects – Alex Bigazzi

  18. ITS Data Aggregation Effects – Alex Bigazzi

  19. Conclusions • With respect to disaggregate data: • Speed consolidation • Underestimate emissions, fuel • Travel time • TMS underestimates TT and delay • Est. SMS from TMS and variance ITS Data Aggregation Effects – Alex Bigazzi

  20. Considerations • Costs of data management • Future uses for data • Missing or bad data • Other summary stats • Variance • Harmonic mean • Median ITS Data Aggregation Effects – Alex Bigazzi

  21. Thank you! Questions? ? ? ? Acknowledgements Dr. Robert Bertini, Helene Siri, Stuart Beale, Tim Rees, and OTREC ITS Data Aggregation Effects – Alex Bigazzi

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