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Refining the Spatial and Temporal Inputs From Travel Demand Models

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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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