1. Analysis and Multi-Level Modeling of Truck Freight Demand Huili Wang, Kitae Jang, Ching-Yao Chan California PATH, University of California at Berkeley TRB SHRP 2 Symposium Innovations in Freight Demand Modeling and Data September 14, 2010

2. 22 Overview  Background  Motivation & Objectives  Data Sources  Analysis  Temporal & Seasonal Analysis Hourly, daily and monthly trends  Multi-level Modeling Methodology and estimation  Model Validation Prediction outcomes  Concluding Remarks  Summary of Analysis and Modeling Results  Future work

3. 3 Research Background  Trucks carry three-fourths of the value of freight shipped and two- thirds of the weight in the United States  8.78 billion tons of shipments were transported annually by trucks for an average of 206 miles per carry in 2007  According to the FHWA, the nation’s freight tonnage is projected to increase nearly 70% by 2020 (versus 2008)  Freight is an important part of the transportation sector, and the transportation sector is in itself a major component of US economy. Source: Bureau of Transportation Statistics and USDOT FHWA http://www.bts.gov http://www.fhwa.dot.gov

4. 4 Motivation and Objectives  Motivation  Truck movements have significant impacts on infrastructure and environment.  It is important to assess the exposure of freeway segments to truck operations.  However, there is a limited number of weigh stations and freight data at the freeway levels are not widely available.  Objectives  Combine truck traffic data and socio-economic indicators to establish a truck freight demand model.  Use the model to construct “virtual” weigh stations to estimate freight demand at locations where data is lacking  Provide a tool for infrastructure and operation management.

5. 5 Data Sources  Weigh-in-Motion Stations Data in California  WIM allows efficient operation of weigh stations without requiring truck stopping  Capturing truck data Axle weight Truck volume Vehicle Classification  Source: http://www.dot.ca.gov/hq/traffops/trucks/datawim  U.S. Census Bureau — Economic & Census data (population, economy etc.)  https://www.census.gov

6. 6 Temporal & Seasonal Analysis  Data from 66 WIM stations were processed and reviewed.  Data from all stations showed similar patterns.  Exemplar figures below are from one station from Caltrans District 1 in Humboldt County on route US-101 from March 2008 to February 2009.

7. Maximu m 75 th percentage 25 th percentage Median Minimum

8. 8 Temporal & Seasonal Analysis  Hourly Variation  Single truck peak volume versus “double peaks” for regular traffic  Heavy load during night time  Daily Variation  Greater traffic volume and heavy load on weekdays  Monthly Variation  Greater traffic volume in summer months  The average truck weight is fairly constant over the year

9. 9 Multi-level Modeling  Freight Operations and Network Characteristics  Freeways cut across multiple regions (counties)  Socio-economic conditions vary in different counties  Regions (counties) contain multiple Weigh-in-Motion Stations Conceptual Hierarchy of Model

10. 10 Multi-level Modeling  Model Specification Level 1: WIM -- truck weight and volume (AADT) Level 2: County -- social and economic indicators (Population, Median Income, Firms) Level 3: Freeway -- varying random around the mean

11. 11 Multi-level Modeling  Model Estimation  Intraclass Correlation Level 1: Truck AADT as the most relevant variable, statistically significant at 1% Level 2: Median income, population, firms significant at 10% Intraclass correlation at 0.45-0.55, indicating validity of model structure

12. 12 Discussions of Modeling Results  Freight demand is dominantly reflected in truck traffic volume.  Population was found to have a positive effect on the freight demand.  The median income was found to have a negative relationship with freight demand.  The number of firms was also found to decrease with the estimated freight demand.

13. 13 Model Validation  Freight weight was estimated using the multi-level model with economic data as input and predicted by STATA. The estimation results are compared with the actual values to determine the validity of the model.

14. 14 Model Validation  Using the multi-level regression model, freight demand was estimated at 46 WIM stations and compared with the actual freight weight in the following map.

15. 15 Conclusions  A multi-level model was developed to incorporate truck operation data as well as socio-economic variables for freeway-level freight demand assessment.  The model was validated against actual WIM data and showed promising results in estimating truck freight demand.  The model can be utilized to predict freight demand at the freeway segment levels where (WIM) data are not available, thus offering a great tool for regional planning, traffic operation and freeway maintenance.

16. 16 Future Work  Expansion of Database  Data acquisition in process for decade-long state-wide WIM and Free-Pass stations in California  Model Refinement  Verifying model performance with expanded data set  Evaluating model performance with additional socio- economic indicators, e.g. regional agricultural activities and demographics  Adopting alternative modeling approach, such as the use of number of shipment as the dependent variable, and comparing model performance.