Using Commodity Flow Survey Microdata and Other Establishment Data to Estimate the Generation of Freight, Freight Trips, and Service Trips: Guidebook (2016)

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124 This chapter discusses several illustrative applications of the kind that could be conducted using the models included in the guidebook. The applications describe the use of: • FTG models to estimate commercial parking needs at a commercial center; • FTG models to analyze historic trends using the ZIP Code Business Patterns database; • FTG models to support the development of a freight model at the MPO level; and • FG models to analyze the importance of freight corridors at the state level. Application 1: Quantification of Commercial Parking Needs for a Commercial Center This application illustrates the use of the guidebook models to quantify commercial (freight and service) parking needs for a commercial center. The approach used in this application could also be used to assess FSA parking needs for commercial streets, buildings, and the like. The example uses real data from a commercial center that houses 19 businesses, most of which are in the retail sector (NAICS 44-45) and accommodation and food services (NAICS 72). The remaining establishments are in finance and insurance (NAICS 52); real estate and rental and leasing (NAICS 53); professional, scientific, and technical services (NAICS 54); and other ser- vices (except public administration) (NAICS 81). In this case, given that the commercial center was already in operation, team members interviewed the staff at the stores to obtain accurate estimates of full-time and part-time employment. In applications for which the employment numbers are not known, estimates from establishments of similar sizes and lines of businesses could be used without major problems. Table 120 shows the estimated FTG. The estimates are based on equations and tabular infor- mation provided in Chapter 8. The FTA was estimated using Equation 24 and the parameters found in Table 9 for the linear models, and using Equation 25 with the parameters of Table 10 for the non-linear models. For FTP, Equation 26 and the parameters found in Table 11 were used for linear model estimates, while Equation 27 and Table 12 were used for the non-linear FTP estimates. Tables 14 and 15 present the estimates for number of service trips attracted per day. Only STA is shown because, as the reader may remember, there are no models for STP. To arrive at the estimates of STA, the parameters in Table 14 and the FTE employment for the businesses were incorporated into the formula in Equation 29. To derive the non-linear estimates, the parameters in Table 15 and Equation 30 were used. As shown in Tables 120 and 121, the estimates of total FTG range between 37.43 (24.49 + 12.94) (linear model) and 48.02 (27.79 + 20.23) (non-linear model), whereas the ones for total STA range between 5.34 (linear model) and 6.81 (non-linear model). These differences are expected given the variability in the data. Using the non-linear model, which provides a more conservative C h a p t e r 9 Illustrative Applications

Illustrative applications 125 analysis, one could estimate the number of parking spaces needed to make deliveries. To do so one needs to have an idea about the temporal distribution of deliveries and service activity, and the amount of time that the typical freight and service vehicles spend making a delivery/pick-up or performing a service activity. Six of the commercial establishments were in industry sectors (i.e., NAICS 51, 52, 53, 54, and 81) that had no FTG models. These establishments were not included in the calculations. Table 122 shows the computation of the number of parking spaces needed for FSA activity. The assumptions embedded in the calculations are as follows: 1. About 25% of the total FTG takes place during the peak hour (typically 7:00 a.m.–8:00 a.m.), which is consistent with the data collected by Holguín-Veras et al. (2007); 2. The service trips are uniformly distributed during normal office hours, which leads to 12.5% being performed in any of the 8 hours of the regular business day; and 3. Delivery and service vehicles occupy the parking space for 0.5 hour and 1.5 hours, respectively. NAICS Geography Linear Model Non-linear Model 1 44 44 CR 11.2 3.21 3.91 2 44 44 CR 3.15 2.37 2.44 3 44 44 CR 7 2.77 3.28 4 44 44 CR 4.15 2.48 2.70 5 44 44 CR 1.9 2.24 2.02 6 45 45 CR 4.25 1.11 2.29 7 45 45 CR 12.75 3.34 3.82 8 45 45 CR 4.8 1.26 2.42 9 72 72 CR 21 1.14 1.64 10 72 72 CR 9.5 1.14 1.08 11 72 72 CR 4.8 1.14 0.76 12 72 72 CR 4.9 1.14 0.77 13 72 72 CR 3.8 1.14 0.67 All FIS 93.2 24.49 27.79 24.49 27.79 NAICS Geography Linear Model Non-linear Model 1 44 44 CR 11.2 4.68 5.79 2 44 44 CR 3.15 1.32 2.61 3 44 44 CR 7 2.93 4.31 4 44 44 CR 4.15 1.73 3.11 5 44 44 CR 1.9 0.79 1.90 6 45 44, 45 CR 4.25 1.84 3.09 7 45 44, 45 CR 12.75 5.51 6.33 8 45 44, 45 CR 4.8 2.07 3.34 9 72 72 NYC + CR 21 2.39 3.86 10 72 72 NYC + CR 9.5 1.08 2.21 11 72 72 NYC + CR 4.8 0.55 1.36 12 72 72 NYC + CR 4.9 0.56 1.38 13 72 72 NYC + CR 3.8 0.43 1.16 All FIS 93.2 25.89 40.46 12.94 20.23 NAICS Description Freight Trip Production (FTP) FTP (2 shipments = 1 vehicle trip): Accommodation and Food Services Freight Trip Attraction (FTA) Model Used FTE Shipments Out/Day Retail Trade Actual NAICS Model Used FTE Deliveries Received/Day ID NAICS Description Actual NAICS FTA (1 delivery = 1 vehicle trip): Retail Trade Accommodation and Food Services ID Six of the commercial establishments were in industry sectors (i.e., NAICS 51, 52, 53, 54, and 81) that had no FTG models. These establishments were not included in the calculations. Table 120. FTG estimates for a commercial center in Troy, NY.

126 Using Commodity Flow Survey Microdata and Other establishment Data to estimate the Generation of Freight, Freight trips, and Service trips Under these assumptions, the number of parking spaces (or loading docks) needed to accommodate the FSA at the commercial center equals the peak-hour traffic (in vehicles/hour) multiplied by the parking time (in hours), as shown in Table 122. Table 122 shows that about 7 to 8 parking spaces are needed to satisfy the needs of FSA. The parking needs of service trips are more than proportional to their traffic, simply because they tend to occupy parking spaces for longer periods of time. The table also hints at the potential benefits of freight demand management. If a staggered delivery program helps spread out the deliveries during the work hours, the peak hour traffic could come down. The smallest number of parking spaces would be achieved if the FSA vehicles arrive uniformly during work hours at a rate of 12.5% of the total traffic every hour. In this case, the number of parking spaces needed by freight vehicles would be only three, meaning that four to five parking spaces would satisfy the combined needs of FSA. Application 2: FTG Trends at the County/Borough Level This application was conducted by RPI in response to a request from the New York City Department of Transportation (New York City DOT). It illustrates the use of FTG models to analyze the evolution of freight activity over time, in this case at the borough level. To design NAICS Geography Linear Model Non-linear Model 1 44 44 CR 11.2 0.20 0.19 2 44 44 CR 3.15 0.06 0.05 3 44 44 CR 7 0.13 0.12 4 44 44 CR 4.15 0.07 0.07 5 44 44 CR 1.9 0.03 0.03 6 45 45 CR 4.25 0.07 0.37 7 45 45 CR 12.75 0.21 0.37 8 45 45 CR 4.8 0.08 0.37 9 72 72 CR 21 1.14 1.72 10 72 72 CR 9.5 0.51 0.50 11 72 72 CR 4.8 0.26 0.18 12 72 72 CR 4.9 0.27 0.18 13 72 72 CR 3.8 0.21 0.12 14 Information 51 51 CR 1.45 0.02 0.03 15 Finance and Insurance 52 52 NYC+ CR 4.35 0.85 1.17 16 53 53 CR 4.9 0.08 0.08 17 53 53 CR 3.9 0.08 0.08 18 Professional, Scientific, and Technical Services 54 54 CR 1.9 0.50 0.56 19 Other Services (except Public Administration) 81 81 NY 5.6 0.57 0.60 All Sectors 115 5.34 6.81 5.34 6.81 STA (1 service call = 1 vehicle trip): Real Estate Rental and Leasing ID NAICS Description Actual NAICS Model Used Service Trips/Day Retail Trade Accommodation and Food Services FTE Table 121. STA estimates for a commercial center. Measure of freight and service activity Daily total Peak hour as % of total Vehicles/hour (peak hour) Parking time (hours) Parking spaces Freight trip generation 48.02 25.00% 12.01 0.50 6.00 Service trips 6.81 12.50% 0.85 1.50 1.28 Total 54.83 12.86 7.28 Table 122. Freight and service vehicle parking needs.

Illustrative applications 127 mobility policies in New York City, the New York City DOT needs to analyze recent trends in freight activities; however, freight activity statistics used to be available only at a broader (sub) regional level. To understand freight impacts at the sub-area level, as was mandated by the New York City Council and City Hall, such broad statistics had to be disaggregated to the borough level. The dis- aggregated freight activity statistics could provide a foundation for more effective transportation strategy design, which would be essential for NYC local task forces and working groups that involve multiple agencies, such as the Port Authority of New York & New Jersey (PANYNJ), New York State DOT and New York City Metropolitan Transportation Authority (MTA). The RPI team assembled and processed employment and establishment data at the borough level from 1998 through 2013. Employment data from the U.S. Census Bureau was first checked against the data provided by New York State Department of Labor to ensure data quality. Using the FTG models, the team then estimated the freight traffic and deliveries produced by each of the five boroughs in the New York City metropolitan area, as well as the areas below Central Park (Midtown and Downtown), which are of great interest because of their extreme congestion and pollution concentrations. The FTG trends over the last decade can be seen in Figure 6. These estimates provided the New York City DOT with great insight into the magnitude and pervasiveness of freight traffic in the city. First, they showed the differential effects produced by the fiscal crisis of 2009. As shown in Figure 6, the FTG in Manhattan experienced a drop. Staten Island, a primarily housing borough, was also visibly affected. In contrast, Brooklyn, Queens, and the Bronx maintained their previous trends. Moreover, the estimates put Manhattan—and particularly the areas below Central Park—in the spotlight as the largest FTG in the city. Up to this point, many planners had believed that Brooklyn was the most important FTG in New York City. In fact, while Brooklyn is the home of a great deal of manufacturing activity that relies on large trucks, the number of commercial establishments in Manhattan is more than double the number in Brooklyn. The small commercial establishments in Manhattan, typically using small trucks and delivery vans, produce a significant amount of freight trips. In response to these insights, New York City DOT is considering several freight initiatives with special focus on the areas below Central Park. The interest generated by the results at the borough level prompted the New York City DOT to ask RPI to conduct further analyses at the ZIP code level. Application 3: FTG Analyses to Support Development of Freight Models This application uses FTG models to estimate ground-level estimates of freight activity at the ZIP code level. These estimates are very important because, being obtained from solid data about employment, they provide a robust way to anchor the estimates of regional freight demand models. This application was conducted in collaboration with a project funded by a grant from SHRP2 Project C-20. As part of this effort, the Capital District Transportation Committee (CDTC)—the designated MPO of New York State’s Capital Region—collaborated with RPI to use different data sources to produce a unified freight dataset that comprehensively describes freight activity in the CDTC region. The readily available freight data were too aggregate for MPO purposes. CDTC lacked a solid picture of local freight activity and needs at a fine level of detail, which limited the CDTC’s ability to account for freight industry needs when making decisions about projects and policies. A key goal of the collaborative effort was to estimate freight activity at the ZIP code level, with the ulti- mate goal of producing estimates at the TAZ level. The estimates obtained using the new models could allow CDTC to make sure infrastructure investments have the highest benefit possible. To this effect, the RPI team applied the FTG models estimated to the ZIP Code Business Patterns database. Figure 7 is an example of FTG patterns by ZIP code.

128 Using Commodity Flow Survey Microdata and Other establishment Data to estimate the Generation of Freight, Freight trips, and Service trips - 50,000 100,000 150,000 200,000 250,000 300,000 350,000 1998 2000 2002 2004 2006 2008 2010 2012 2014 Manhattan and Manhattan below Central Park Manhattan Manhattan below Central Park - 20,000 40,000 60,000 80,000 100,000 120,000 140,000 160,000 180,000 1998 2000 2002 2004 2006 2008 2010 2012 2014 Brooklyn and Queens Brooklyn Queens - 10,000 20,000 30,000 40,000 50,000 60,000 1998 2000 2002 2004 2006 2008 2010 2012 2014 Bronx and Staten Island Bronx Staten Island Figure 6. FTG by borough.

Illustrative applications 129 The FTG estimates were put together with other freight-related data to form a dynamic data- base that allows CDTC to better understand how, why, and where freight moves through the region, to help facilitate the efficient and sustainable movement of goods while maintaining quality of life. The FTG estimates produced enabled CDTC to identify corridors essential to freight movement, and existing barriers to greater efficiency of freight activity. The estimates have been indispensable when: • prioritizing infrastructure investments; • forecasting transportation system performance; • mitigating the impacts of truck traffic; • determining the impacts of freight activities on quality of life; and • improving the safety and security of the network. Application 4: FG Analyses at the State/MPO Level This application illustrates the potential use of FG models to estimate, at the ZIP code level, the amount of freight being produced. As in the previous application, ground-level employment data were used to produce estimates of FP that cannot be produced using alternative modeling methodologies. This application was prompted by the 2012 passage of MAP-21. Figure 7. FTG at ZIP code level in CDTC region.

130 Using Commodity Flow Survey Microdata and Other establishment Data to estimate the Generation of Freight, Freight trips, and Service trips MAP-21 required the designation of a national freight network to assist states in strategically allocating resources to improve freight efficiency. The goals of this freight network designation were to reduce freight transportation delay time and improve reliability for each freight trans- portation mode through infrastructure improvement, technology development, regulations, and enhancement of multimodal transportation capacity and connectivity, among other methods. The highway portion of this network, called the Primary Freight Network (PFN), comprises 29,966 miles of highways and key land ports of entry. The designation of the PFN was based mainly on the national freight volume, including origins, destinations, and total freight tonnage and value by highways. Population distribution, network connectivity, and truck traffic data (including truck traffic volume, its percentage in the overall traffic, and the access to major ports of entry and main production areas) were also key considerations. An inventory of current and forecasted national freight patterns was critical to the design of the PFN. The U.S.DOT developed a draft PFN based on information provided by the FHWA. On November 19, 2013, the U.S.DOT published the draft and issued a request for comments to collect feedback from stakeholders including states, transport providers, and users of the network. In response to this request for comments, the RPI team conducted a series of analyses on freight activities in New York State, both to support the New York State DOT and New York State Thruway Authority, and to suggest changes to the PFN. The team examined the New York State employment data and used it in combination with the FG models shown in Table 25 of this guidebook to estimate FG at the ZIP code level. The FG for major New York State Interstate highways was then analyzed, based on each highway’s catchment areas. The results of this analysis are shown in Figures 8 and 9. Figure 8. FG on the East-West corridors.

Illustrative applications 131 When interpreting the results it is important to note that (1) these estimates, which correspond to local production of freight, do not include the amount of cargo at border crossings; and (2) the employment data, particularly in the metropolitan areas, could be affected by the “headquarters problem.” The headquarters problem occurs when a company’s headquarters, typically located in a city center, reports the entire company’s employment as if all the employees are located at the headquarters. This practice can artificially increase the employment numbers in city centers, and in doing so can exaggerate the FP in urban areas. Notwithstanding the potential effects of the “headquarters problem,” the FG estimates in Figures 8 and 9 provide a compelling geographic map of FG at the ZIP code level that cannot be obtained by other means. The data clearly show that I-90 and I-87 are the two most heavily used freight corridors, especially the segments close to metropolitan areas. These estimates were included in the memo to the U.S.DOT in which the agencies argued for the inclusion of the entirety of I-87 and I-90 as part of the PFN. In 2015, the Fixing America’s Surface Transportation Act (FAST Act) repealed both MAP-21 and the PFN. Figure 9. FG on the North-South corridors.