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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 l.jpg

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 l.jpg
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


Current method l.jpg
Current Method

Travel demand models produce:

Linkj (AM:3hrs) = 2000

AQ Models require hourly breakdown

From travel diaries:


New method l.jpg
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


Early testing sacramento l.jpg
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


Findings l.jpg
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)



Application to s coast l.jpg
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


La clusters l.jpg
LA Clusters

Cluster 2

Cluster 1

Proportion of ADT

Time of Day


San diego clusters l.jpg

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

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14

16

18

20

22

0

2

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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

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22

0

2

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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


Daily nox emissions sd l.jpg
% Daily NOx Emissions (SD)

New Method -Normal

New Method - Ozone Day

% Daily NOx Emissions

Default

Time of Day



High ozone compared to default diff in daily emissions by cluster l.jpg
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


Conclusion l.jpg
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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