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

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 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

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 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:  U.S. Census Bureau — Economic & Census data (population, economy etc.) 

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.

Maximu m 75 th percentage 25 th percentage Median Minimum

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 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 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 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 , indicating validity of model structure

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 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 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 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 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.