The Role of Classification Systems in Freight Trip Generation (FTG) Modeling

Transcription

1 1 The Role of Classification Systems in Freight Trip Generation (FTG) Modeling Shama Campbell, M.S. Researcher Rensselaer Polytechnic Institute VREF Center of Excellence for Sustainable Urban Freight Systems

2 Acknowledgements 2 Co-authors Miguel Jaller, Ph.D. jallem@rpi.edu Rensselaer Polytechnic Institute Iván Sánchez-Díaz, M.S. sanchi2@rpi.edu Rensselaer Polytechnic Institute sanchi2@rpi.edu José Holguín-Veras, Ph.D., P.E. (PI) jhv@rpi.edu Rensselaer Polytechnic Institute Catherine Lawson, Ph.D. lawsonc@albany.edu University of Albany Funded by the National Cooperative Freight Research Program (NCFRP- Project # 25)

3 Outline 3 Introduction Classification Systems New York City Case Study Description of the Data Methodology Comparative Analysis Conclusions /Recommendations

4 Introduction

5 Introduction 5 NCFRP 25 research identified premises central to the development of freight generation (FG) and freight trip generation (FTG) models: 1. The distinction between FG and FTG FG is an expression of the economic activity performed at a business establishment generating an output (e.g. tons of goods) FTG is the result of the logistic decisions concerning how best to transport the FG, in terms of shipment size, frequency of deliveries, and vehicle/mode choice.

6 Introduction 6 The other FG/FTG premises: 2. The effectiveness of the measure of business size used to capture the intensity of FG/FTG 3. The validity of the statistical techniques used to estimate the models. e.g. Ordinary Least Square (OLS) regression analysis, Multiple Classification Analysis (MCA) 4. The correctness of the aggregation procedure used and how it is applied (if needed). 5. Accuracy of the classification system used to group the commercial establishments

7 Purpose of the Study The purpose of the study is to analyze the merits of using various types of classification systems in freight trip generation (FTG) modeling. The study will identify how classification systems affect the ability of the modeling process to efficiently estimate FTG models.

8 Classification Systems

9 Classification Systems 9 A classification system is a systematic way to group individual entities into pre-defined groupings or classes with which they share common features. Needs to reflect the underlying economic activity of the grouped establishments. Major challenge - identifying a set of homogeneous businesses in a dynamic economy with a mix of establishments conducting a variety of activities. Even more difficult is identifying geographic areas that concentrate these establishments.

10 Classification Systems 10 Two types of classification systems: based on economic activity based on land use Economic Based Standard Industrial Classification (SIC) System; North American Industry Classification System (NAICS) Land Use Codes Land-based Classification Standards (LBCS) City of New York Zoning Resolution (NYCZR)

11 Economic Activity Based Classification Systems Standard Industrial Classification (SIC) The final version (1987) had 1,004 different industries. 4-digit coding system: first 2 digits -major industry; 3 rd digit - industry group; 4 th - industry type. North American Industry Classification System (NAICS) The latest version of the NAICS has 1,170 industries 6-digit coding: first 2 digits - the sector; 3 rd - subsector; 4 th - industry group; 5 th - industry; 6 th digit - Country code (i.e., U.S., Canada, or Mexico).

12 Land Use Classification Systems There are land use codes for different municipalities as well as within. Efforts to create a more universal set of land use code resulted in the development of the Standard Land Use Coding Manual (SLUCM). It identified different dimensions to describe land use (i.e. land parcel, structure, space usage) The study also concluded that no rigid system was applicable across all communities.

13 Land Use Based Classification Systems The land-based classification system (LBCS) was developed as an update to the SLUCM. It classifies land use in the following dimensions: activity function, structure type, site development characteristics and ownership constraints. Function dimension classifies the land use according to the type of establishment. Activity dimension classifies the land use according to the physical activity taking place.

14 City of New York Zoning Resolution (NYCZR) 14 The NYCZR is a local classification system developed by the City Council of New York in Commercial districts allow (through special permits) retail, service or office uses. Manufacturing districts allow manufacturing uses, most commercial uses and some community facility uses. Residential districts designs zoning districts in which only residences and community facilities are permitted, though some districts (i.e., residential district equivalents) with commercial developments are assigned a residential code.

15 Case Study

16 Description of the Data The dataset used in the study was collected from receivers located in Manhattan and Brooklyn in New York City in The survey gathered company characteristics, operational patterns and FTG patterns. Due to the nature of the study heavier weight was placed on freight-related businesses in the random-selection for the sample. The final sample of receivers consisted of 362 complete observations.

17 Methodology 17 Recode from SIC to NAICS. Code dataset in NYCZR by geo-locating the businesses then mapping to zoning polygons. For LBCS the function dimensions was compared to the SIC industry code. Regression analysis Models that are employment dependent are re-estimated using Multiple Classification Analysis (MCA)

18 Comparative Analysis

19 Comparative Analysis 19 From the regression model analysis, comparison were made of similar industries between the classification systems. The findings were that the application of the different classifications system resulted in: Different subsample sizes for the industry Different model types Different trip rates

20 Comparative Analysis Group Classification System Code/Function Description Obs. Construction Manufacturing Wholesale Trade Const./Empl. c b SIC 15, 16, 17 Construction - group model S NAICS 23 Construction sector S LBCS Construction; services; and other Construction-related business; Communications and information; Education, public admin, health care, and other institutions S SIC Manufacturing - group model S NAICS 31, 32, 33 Manufacturing sector S NYCZR LBCS M1-1, M1-2, M1-2/R6A, M1-2D, M1-6, M3-1 Textiles & Manufacturing Manufacturing districts S Manufacturing: Food, textiles, and related products; all other manufacturing S SIC 50, 51 Wholesale trade - group model C NAICS 42 Wholesale trade sector C LBCS Wholesale Trade Durable and nondurbale goods C Notes: Coefficients are statistically significant at 95% level Model Types: S - Constant trip rate (c); C - Combined linear model with intercept (c)and dependent on business size (b) Best Model

21 Comparative Analysis 21 Group Classification Const./Empl. Best Code/Function Description Obs. System c b Model SIC 52, 53, 55, 56, 57, 59 Retail trade - group model S NAICS 44, 45 Retail trade sector C LBCS Retail & Pharmacy Pharmacy or drugstore; cosmetic and beauty supplies; business, professional, scientific, and technical services; all other retail S Retail Trade Food SIC 20, 54, 58 Food stores, restaurants and bars C NAICS 72 Accommodations and Food C LBCS Grocery and Food Services Grocery store, supermarket or bakery; specialty food store; fruit and vegetable store; beer, wine and liquor C Commercial NYCZR C1, C4, C5, C6 C2, C8 Small retail & service shops: grocery stores, restaurants & beauty; large stores with general goods: specialty & department stores, theaters & other commercial and office uses. Small retail & service shops: same as C1 but permits funeral homes & repair services; heavy repair shops and automotive C S Residential NYCZR R6, R6A, R6B, R7-1, R7-2, R7A, R8 Residential Districts S Notes: Coefficients are statistically significant at 95% level Model Types: S - Constant trip rate (c); C - Combined linear model with intercept (c)and dependent on business size (b)

22 Comparative Analysis 22 For the majority of the industry sectors the economic based classification systems outperformed the land use classification systems. Industry Sector Sample FTA RMSE Obs. FTA SIC NAICS NYCZR LBCS SIC NAICS NYCZR LBCS Construction Manufacturing Wholesale Trade Retail Trade Food

23 Comparative Analysis 23 The constant trip rates (type S models) from the regression models and the MCA trip rates for those that are employment dependent (Type E and C models) were applied to the entire dataset and compared to the observed data. The economic based classifications systems provide more efficient models as indicated by the RMSE. Classification System RMSE SIC NAICS NYCZR LBCS 4.529

24 Conclusions

25 Conclusions 25 Use of different classification systems in FTG modeling may result in differences in sample sizes, model types, and trip rates. Economic based models offer more efficient trip rates than land use for this dataset, as they are able to directly capture the economic activity, that may differ with establishment type. This is not easily captured by land use codes as land use patterns vary in different areas. Also due to mixed land use it is difficult to achieve the level of homogeneity that economic based classification systems are able to.

26 Conclusions 26 Economic based models lend itself better to transferability. As economic activity of a specific type of establishment will be similar despite location. This may not be the case for land use based models due to variations in land use patterns based on geographic locations. NYCZR shows better performance of the two land use codes. This is strongly due to the dataset being from NYC. But for this reason these models may not be likely to be applicable for other cities/areas

27 Conclusions 27 LBCS may be more transferable as function and activity dimensions captures some economic activity. There needs to be more work done to integrate transportation considerations in the formulation of land use codes. Further applications needs to be done with different datasets to solidify the findings in this research.

28 References 28 Holguín-Veras, J., M. Jaller, I. Sanchez-Diaz, J. Wojtowicz, S. Campbell, D.M. Levinson, C. Lawson, E. Powers, and L. Tavasszy. Freight Trip Generation and Land Use Campbell, S., M. Jaller, I. Sanchez-Diaz, J. Holguin-Veras, and C. Lawson. Comparison Between Industrial Classification Systems in Freight Trip Generation (FTG) Modeling. In 91st Annual Meeting of the Transportation Research Board. 2011: Washington, D.C. Lawson, C., J. Holguín-Veras, I. Sánchez-Díaz, M. Jaller, S. Campbell, and E. Powers. Estimated Generation of Freight Trips Based on Land Use. Transportation Research Record, Vol. 2269, No. 2012, pp 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 Holguín-Veras, J. Potential for Off-Peak Freight Deliveries to Congested Urban Areas Albany, NY, 2006.

29 Thanks! Questions? Shama Campbell, M.S.