Freight Trip Generation and Land Use (2012) / Chapter Skim
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Pages 52-79
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48 A review of the literature and current practices in freight modeling (see Appendices D and E) revealed several information gaps.
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49 possible to approximate the number of truck trips to each store, since one routing covers 2 to 3 stores. For example, the maximum number of pallets that a store receives is 18, which is below the maximum capacity of a 53-foot container.
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50 primary industry types. As highlighted, about a quarter of the companies are in the food related sector, and another third are in the wholesale durable and non-durable goods trade.
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51 4The Tax Parcel Attributes Table also contained zoning designations. This designation was compared with the zoning polygons and found sixteen inconsistencies; upon further investigation it was found that they do not influence the outcome of the models.
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52 on their characteristics. In this system, land uses are classified by refining traditional categories into multiple dimensions, such as activities, functions, building types, site development character, and ownership constraints.
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53 of correlation between function and activity, which prevents considering both as independent variables in econometric models of FTG. NYC Whole Foods Market Dataset Whole Foods Market is a chain of grocery stores offering natural and organic foods with more than 270 stores in North America and the United Kingdom.
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54 dors serving each store. As shown in Table 32, the store at Columbus Circle (CIR)
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55 to note that the data have been made available to the team courtesy of the research team at the Puget Sound Region Council (PSRC) , which conducted the work reported in Ta et al.
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56 Following the methodology described previously, two different approaches were applied: standard trip generation rates (per establishment and per employee) ; and Ordinary Least Squares (OLS)
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57 As well as for the states' classification, standard trip generation rates and regression analyses were analyzed using store location (in-mall -M- or off-mall -OM) as a categorical factor to determine FG and FTG.
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58 size and industry sector, estimated models are discussed herein. Standard Industrial Classification (SIC)
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59 the analyses; however, the results revealed what seem to be anomalous results for establishments within the 31–40 employee bracket. This reflects the low number of observations in that range.
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60 Freight Trip Production Freight trip production refers to the number of truck trips produced by the source of the commodities, i.e., the shipper. This is captured in the carriers' surveys with the average number of trips made by each establishment in a typical day.
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61 c b 15, 16, 17 Construction* 9 0.068 E 1.586 17 Special trade contractors 8 0.065 E 1.576 4 21-39 Manufacturing 16 1.625 S 1.364 42, 47 Transportation, Communication and Utilities*
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62 egory, the table shows the error of applying the rates obtained from the models previously discussed. When FTG is constant, the delivery/trip rate per establishment found for the category is used; instead when FTG is employment dependent, MCA rates are used.
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63 The City of New York Zoning Resolution (NYCZR) Zoning ordinances adopted by municipalities regulate the size and use of land and buildings, including location and density.
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64 for establishments with less than 30 employees, but exhibited anomalies in the group of 31–40 employees. This likely results from the lack of a sufficient number of observations in the 31–40 employees group to support the MCA estimation.
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65 Comparison Between LBCS and NYCZR Upon analysis, the authors found that for both NYCZR and LBCS, most of the best models were produced using a constant coefficient only, and do not depend on business size (quantified as number of employees)
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66 tial exists for universal or transferable FTG models. To assess the potential of LBCS will require applying and comparing results to other local land use classifications (e.g., Seattle, WA, or Portland, OR)
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67 Area-Based Models As found in the review of the freight systems and land use, there are variables that play a significant role in the estimation of FTG. For example, the previous sections have shown the estimated disaggregate models for FTG based on employment for different industry segments and land use categories.
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68 models using employment or number of establishments per ZIP code as dependent variables, and the different areas as independent variables, were estimated. Table 53 shows the resulting models.
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69 case study number codification enables the reader to locate these models in the FG/FTG model relational database developed by the team. As shown in Table 54, the corrected models present conceptually valid coefficients.
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70 Source Number*
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71 Y – is the average of deliveries observed in the external validation dataset, Yˆi is the number of deliveries estimated using FTG models for each establishment on the external validation dataset, and Yˆ – is the average of deliveries estimated using FTG models for the external validation dataset. A model predicting observed data perfectly produces a straight line plot between observed Yi and predicted values Yˆi, and a correlation coefficient of 1.0.
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72 important role as categorical factors. The question remaining is: Should one create new models by state, or estimate models based on land use characteristics (such as store location)
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73 attraction, and NAICS provided better models for freight trip production. The findings indicate that the results of FTG estimates are somewhat impacted by the type of industry classification system used in the analysis.
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74 mance of model BR-1977-1, which is the only model developed for grocery stores, and the corresponding corrected model. Table 63 shows the FTG estimated using the original model, and the corrected model.
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75 • Implementing Synthetic Correction increases the performance of FTG models: This procedure consists of correcting existing employment trip rates to reflect the differences in FTG patterns for small establishments and large establishments. The application of this procedure produced a significant decrease in the estimation errors of FTG models.
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