Transferability of Freight Trip Generation Models. Iván Sánchez-Díaz

Transcription

1 1 Transferability of Freight Trip Generation Models Iván Sánchez-Díaz Researcher Rensselaer Polytechnic Institute VREF S Center of Excellence for Sustainable Urban Freight Systems Co-Authors: José Holguín-Veras, Catherine Lawson, Miguel Jaller, Shama Campbell, Herbert S. Levinson, Hyeon-Shic Shin October 9 th, 2013

2 Outline 2 Introduction Summary of NCFRP 25 Project Freight Trip Generation Models Considered External Datasets Assessment of Transferability A Technique to Increase Transferability: Synthetic Correction Conclusions

3 Introduction 3

4 Introduction 4 The development of freight demand models for freight transportation is difficult due to: Lack of proper balance: knowledge, models and data Poorly understood system Freight Trip Generation (FTG) estimation is critical for traffic impact analyses and long term transportation plans

5 Summary of NCFRP 25 5

6 Summary of NCFRP 25 6 NCFRP 25 studies: Differences Freight Generation (FG) & Freight Trip Generation (FTG) FG/FTG & economic activity/land-use Implications of different modeling structures

7 Summary of NCFRP 25: FG vs. FTG 7 FG and FTG are two different things Generation of demand, e.g., tons, economic manifestation of production/consumption Generation of traffic, e.g., truck trips generation of traffic is the result of logistical decisions Main implications (Holguín-Veras et al., 2011): Increase in FG -> less than proportional increase in FTG A change in shipment size -> vehicle/mode changes Using FTG rates as a function of a single independent variable (e.g., employment) may not be universally valid

8 Summary of NCFRP 25: FTG Modeling 8 Three different modeling approaches: Trip generation rates (per establishment, per employee) Regression Analysis (Ordinary Least Squares) Multiple Classification Analysis (MCA) Four different classification systems: Industrial classification systems (Campbell et al., 2012) Standard industrial classification (SIC) codes North American Industry Classification System (NAICS) Land use classification systems (Lawson et al., 2012) The City of New York Zoning Resolution (NYCZR) Land-Based Classification Standards (LBCS)

9 Summary of NCFRP 25: FTG Rates 9 Type S: Constant FTG per Establishment Type E: Proportional to number of employees Type C: Combined model with intercept and rate per employee Total Cases % Cases % Cases % SIC 12 57% 5 24% 4 19% 21 NAICS 6 60% 0 0% 4 40% 10 NYC Land-Use 13 72% 4 22% 1 6% 18 LBCS 4 80% 0 0% 1 20% 5 Results are consistent in most cases FTG is constant This confirms the concerns about using employment as the only independent variable, because it will lead to estimation errors

10 10 FTG Models Considered for the Assessment

11 Freight Trip Generation Models Considered 11 NCFRP 25 Freight Trip Generation and Land Use: Data from about 700 establishments in New York City 4 classification systems: SIC,NAICS,LBCS and NYCZR Independent variables: employment and area Quick Response Freight Manual (QFRM): Data from a freight origin-destination survey in Phoenix, Arizona (Ruiter, 1992-USDOT, 2007) Classification system used: SIC Independent variable: employment at zonal level ITE s Trip Generation Manual (ITE, 2008): Data from different cities submitted by various entities FTG models available only for some land uses Main independent variables: employment and area

12 External Datasets 12

13 External Datasets 13 The research used FG/FTG validation data from 30 cities in 20 states, using 8 different industry sectors and establishment size from employees: New York City (NYC): 362 receivers in Manhattan and Brooklyn New York Capital Region (NY-CR): 26 establishments located in Albany, Troy, Latham, New Rochelle, and other small cities Mid-West Furniture Chain (MW-FC): 76 stores in 18 states from a furniture store chain in Midwestern States NYC Grocery Stores (NYC-GS): 5 stores in Manhattan Seattle Region Grocery Stores (SR-GS): 8 supermarkets in the Puget Sound metropolitan area

14 14 Assessment of FTG Models Transferability

15 Assessment of Transferability 15 The analysis considers industry segments where: NCFRP 25, QRFM, and ITE provide models there are external data to use as ground truth Two approaches to assess transferability: (1) Application of the models to estimate FTG for establishments where the FTG is known (2) Pooled data collected from establishments in the same industry sector located in different parts of the country is used to estimate econometric models that include location specific binary variables. A statistical significance test assesses prevalence of geographic effects

16 Assessment of Transferability: (1) 16 Classification Description Sample Size Validation Data Mean Employment Dataset Model Applied NCFRP 25 QRFM ITE Model RMSE Model RMSE Model RMSE NAICS 72 Accomodation/ Food NYS-CR *E *E 6.51 n/a n/a ITE 816 Hardware/ Paint Stores NYC 0.369*E *E *E; Trucks: 2% 2.04 LBCS Activity Restaurants NYS-CR *E 6.51 n/a n/a ITE 890 Furniture Stores NYC *E *E; Trucks: 5% 3.39 LBCS Function Retail NYS-CR *E n/a n/a ITE 890 Furniture Stores MW-FC *E *E; Trucks: 5% 1.25 ITE 860 Wholesale Markets NYC *E *E *E; Trucks: 30% SIC 56 Apparel/ Accessory Stores NYS-CR *E *E n/a n/a NAICS 44 Grocery Stores SR-GS *E *E n/a n/a SIC 58 Eating/Drinking Places NYS-CR *E *E 6.51 n/a n/a SIC 52 Building Materials Stores NYS-CR *E n/a n/a LBCS Activity Goods NYS-CR *E *E n/a n/a SIC 54 Food Stores NYS-CR *E *E n/a n/a NAICS 44 Grocery Stores NYC-GS *E *E n/a n/a NAICS 44 Retail Trade NYS-CR *E *E n/a n/a LBCS Function Grocery NYS-CR 0.217*E *E n/a n/a (1) NCFRP 25 performs better than QRFM and ITE in most cases (2) Geographic location does not seem to be correlated with RMSE (3) Samples with high employment present larger errors and differences of performance between NCFRP 25 and QRFM

17 Assessment of Transferability: (2) 17 First case study: Furniture chain in Midwest and Northeastern states Observations: 58 Variables: 5 locational variables (Illinois, Ohio, Michigan, Midwest States, and Northeastern States), type of establishment, employment and interactions Fixed effects model is: FTA= z (E*s 1 ) R^2 adj.=0.47 (23.20) (7.03) (2.36) Where: FTA: Freight Trip Attraction per day E: Number of employees on a typical day z 1 : The establishment is a combo stores s 1 : The establishment is located in Michigan Only one locational variable (Michigan) is statistically significant

18 Assessment of Transferability: (2) 18 Second case study: Retail grocery stores in New York City and Seattle Observations: 37 Variables: 2 locational variables (New York and Seattle), employment and interactions Fixed effects model is: FTA= E R^2 adj.=0.42 (3.22) (5.23) Where: FTA: Freight Trip Attraction per day E: Number of employees on a typical day None of the locational variables are statistically significant: Same model works for NY and Seattle

19 19 A Technique to Increase Transferability: Synthetic Correction

20 Synthetic Correction: Background 20 Research indicates that FTG models accuracy is hampered by incorrect specification: FTG rates do not capture FTG behavior of large numbers of industry sectors rates are only valid in 33% of industry sectors (Holguín-Veras et al., 2011) Synthetic Correction convert FTG rates to an equivalent formulation with a constant and a slope to better represent FTG patterns

21 Number of deliveries Synthetic Correction: Methodology Deliveries Constant FTG rate: y = 0.10x TG r * E a=2.07 is an empirical intercept * A pivot around the midpoint (*) will improve accuracy Equivalent Model: y=0.05x E Number of employees

22 Synthetic Correction: Application 22 Case Study Number Industry Segment / Land Use FTG Rate Synthetic Correction Intercept Slope (1) Slope (2) Notes: (a) Case study numbers from the models in the database ( (b) SC: Synthetic Correction that uses as pivot point the average employment of (1) the calibration dataset and (2) the validation dataset. FTG Rate RMSE SC (1) SC (2) Production Models RU Retail Trade (SIC 52-59) UT Urban Downtown Retail- Knoxville UT Urban Downtown Retail- Modesto Slope decreases UT Urban Downtown Retail- Rochester n.a n.a. UT Urban Downtown Retail- Saginaw (but 0.15 is 6.45 always ) 5.86 UT Urban Downtown Retail Attraction Models RU Retail Trade (SIC 52-59) BR Furniture Establishments Synthetic Correction decreases RMSEs by up to 25.54%, and 12.93% on average

23 Conclusions 23

24 Conclusions 24 Lack of data continues to be the biggest constraint for a more extensive study. This is to the best of the authors knowledge the largest efforts to date of FTG validation Results show that RMSEs for NCFRP 25 models are about ¼ of QRFM s models Locational effects are of limited importance Synthetic correction enhance the transferability of FTG rates in the literature. It transforms FTG rates into an equivalent model with a constant and a slope

25 Conclusions Cnt. 25 This procedure could enhance the accuracy, and extend the usefulness of the hundreds of constant FTG rates in the literature In spite of these encouraging findings, the reality is that the explanatory power of most FTG models is very low A long road ahead before considering that we have conquered the challenge of developing accurate FTG models

26 Conclusions Cnt. 26 The second phase of NCFRP 25 is already in progress. It uses the Commodity Flow Survey disaggregated data for FG model development

27 Acknowledgement 27 This research was supported by the National Cooperative Freight Research Program (NCFRP) Project 25 Freight Trip Generation and Land Use. The authors would like to also acknowledge the support and guidance from Dr. Bill Rogers (NCFRP); and the gracious support of Mr. E. McCormack and Mr. A. Bassok from the Puget Sound Regional Council that shared with the team the Seattle data NCFRP 25 Report is published and available as NCFRP Report 19/ NCHRP Report 739

28 Thanks Questions? Iván Sánchez-Díaz

29 References Holguín-Veras, J., M. Jaller, L. Destro, X. Ban, C. Lawson, and H. Levinson. Freight Generation, Freight Trip Generation, and the Perils of Using Constant Trip Rates. Transportation Research Record, Vol. 2224, No. 2011, pp Campbell S., M. Jaller, I. Sanchez-Diaz, J. Holguin-Veras, and C. Lawson. Comparison Between Industrial Classification Systems in Freight Trip Generation Modeling. TRB 91st Annual Meeting Compendium of Papers, Lawson, C., J. Holguin-Veras, I. Sanchez-Diaz, M. Jaller, S. Campbell, and E. Powers. Estimation of Freight Trip Generation Based on Land Use. Transportation Research Record, Vol. 2269, No. 2012, pp Ruiter, E.R. Development of an urban truck travel model for the Phoenix Metropolitan Area. Arizona Department of Transportation, U.S. Department of Transportation. Quick Response Freight Manual II Institute of Transportation Engineers ITE Trip Generation: : An ITE Informational Report. Washington, D.C., 2008.