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Refining the Spatial and Temporal Inputs From Travel Demand Models

Refining the Spatial and Temporal Inputs From Travel Demand Models. Deb Niemeier Dept. Civil and Env. Engineering University of California Davis, CA. Travel Demand-AQ Models. Running Stabilized – South Coast Inventory 60% Organic gases, 90% NOx

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Refining the Spatial and Temporal Inputs From Travel Demand Models

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  1. Refining the Spatial and Temporal Inputs From Travel Demand Models Deb Niemeier Dept. Civil and Env. Engineering University of California Davis, CA

  2. Travel Demand-AQ Models • Running Stabilized – South Coast Inventory • 60% Organic gases, 90% NOx • Travel demand models – designed primarily to produce estimates of volume for assessing congestion • Produces volumes by “periods” of 3-18 hours • AQ models needs volumes by hour

  3. Current Method Travel demand models produce: Linkj (AM:3hrs) = 2000 AQ Models require hourly breakdown From travel diaries:

  4. New Method • Observe actual hourly volumes on link • Statistically cluster observed 24-hour patterns into groups • Example: Two clusters from San Diego 3. Statistically estimate hourly factors for each cluster

  5. Early Testing: Sacramento • Used observed volumes from 88 highway locations in • the Sacramento region • Estimated the allocation factors (one set for the whole • region) = New Method • Estimated the hourly proportions using the travel diary • survey = Old Method • Ran DTIM with both proportions for running stabilized • Conducted an hourly emissions comparison

  6. Findings • Differences in hourly emissions variation between the two scenario’s can be as large as 15% for the region-wide estimation • Differences in hourly CO estimates between the two scenarios occurs mainly in the off-peak (more than 5 tons in hour 13) • Differences in hourly NOx estimates between the two scenarios occurs mainly in the off-peak (more than 16% also in hour 13)

  7. Sac Hourly Profile - NOx

  8. Application to S. Coast • As part of SCOS97 we monitored • 1609 traffic count locations in Los Angeles • 162 locations in San Diego • Travel demand models for both regions • Networks and link volumes • Study uses matched count to link locations • 1244 traffic count locations in Los Angeles • 140 locations in San Diego

  9. LA Clusters Cluster 2 Cluster 1 Proportion of ADT Time of Day

  10. SD Cluster 1 Average Proportional Traffic Pattern SD Cluster 2 Average Proportional Traffic Pattern 0.12 0.12 0.10 0.10 0.08 0.08 0.06 Proportion of ADT 0.06 Proportion of ADT 0.04 0.04 0.02 0.02 0.00 0.00 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 Time of Day Time of Day SD Cluster 4 Average Proportional Traffic Pattern SD Cluster 3 Average Proportional Traffic Pattern 0.12 0.12 0.10 0.10 0.08 0.08 0.06 Proportion of ADT 0.06 Proportion of ADT 0.04 0.04 0.02 0.02 0.00 0.00 0 2 4 6 8 10 12 14 16 18 20 22 0 2 4 6 8 10 12 14 16 18 20 22 Time of Day Time of Day SD Cluster 5 Average Proportional Traffic Pattern 0.12 0.10 0.08 0.06 Proportion of ADT 0.04 0.02 0.00 0 2 4 6 8 10 12 14 16 18 20 22 Time of Day San Diego Clusters Proportion of ADT Time of Day

  11. % Daily NOx Emissions (SD) New Method -Normal New Method - Ozone Day % Daily NOx Emissions Default Time of Day

  12. Portion of San Diego Network

  13. High Ozone Compared to Default% Diff in Daily Emissions by Cluster Cluster 1 produces a greater share of NOx emissions on a high ozone day than predicted by the default method 300% 200% 100% -50% 9a-4p 12a-7a 7a-9a 4p-6p 6p-12m

  14. Conclusion • New Method: • Based on observed flows • Allows spatial variability to be incorporated • Can result in as much as 300% diff. in hourly emissions estimates compared to default • Will allow potential targeting of roadway improvements, TCM development/enforcement • Next Steps: • Application to non-highway roads

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