Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools (2022)

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27   This chapter provides a comprehensive characterization of freight activities within U.S. cities by focusing on their economies, key supply chains, and freight traffic generated. The chapter describes the key features of typical cities from the perspective of the economy and freight activity, and the relationship between the two, so that readers have a better understanding of the key generators of freight traffic in their area. The analysis presented in this chapter results in a series of indicators that land-use and transportation planners can use to get an idea about freight activity and the economy in their regions. The economy of metropolitan areas is driven by the needs of businesses and residents. The businesses employ residents to produce goods and services, and in turn, residents consume these and other goods and services. Supply chains match one entity’s supply with another entity’s demand. A supply chain for a single good or product can contain numerous stages that connect the suppliers of raw materials, manufacturers, distribution centers, retail stores, and customers. As stated in Chapter 1, the term freight activity is used in this Guide to refer to all manifestations of the movement of goods (i.e., the flows of supplies, the freight trips generated, the shipments sent, and deliveries received). Freight activity occurs throughout metropolitan areas, not only at the large cargo facilities traditionally associated with heavy freight traffic, such as intermodal ports, rail terminals, and distribution centers. In fact, the majority of all freight activity takes place in urban areas, where the primary freight vehicles are delivery vans and small and midsize trucks. Several U.S. cities and U.S. MSAs were selected for the quantitative analyses described in this chapter. The purpose of selecting these areas was to represent a diverse set of sizes and regions of the country. The final selection included nine cities and 11 MSAs. Eight of the nine cities were chosen based on city size, as reported in the 2017 Census Bureau population estimates (U.S. Census Bureau 2017). A ninth city, Washington, DC, was added because of its unique economic base. Similar to the selection of cities, a list of 11 MSAs was developed to represent the freight activity across a variety of region sizes and geographic areas in the United States. The final set of cities and MSAs is shown in Figure 8. 5.1 Urban Economies and Metropolitan Economies The production and consumption of goods are a physical expressions of the economy. An area that produces more output and has a higher demand for goods will likely have a larger economy and generate more freight activity. On a smaller scale, an individual business establish- ment that has more employees generally produces more goods. It is not surprising that in urban and metropolitan areas, where the vast majority of production and consumption takes place, freight activity is an essential component of metro economies. C H A P T E R 5 Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity

28 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools As defined by the U.S. Census Bureau, a MSA is “that of a core area containing a substantial population nucleus, together with adjacent communities having a high degree of economic and social integration with the core” (U.S. Census Bureau 2010). The economic activities in these adjacent communities are interconnected and interdependent with those in the principal city. To illustrate the importance of freight activity in urban and metropolitan areas, it is useful to identify the sectors of the economy that directly or indirectly depend on freight to perform their activities. Industry sectors are classified into two main clusters: FIS and SIS. The FIS consist of those industry sectors for which the production and consumption of freight is the most essential component of the economic activity. The SIS are those industry sectors where the provision of services is the main activity. Based on the North American Industry Classification System (NAICS), the various industry sectors were classified as FIS and SIS, as shown in Table 1 (Holguín- Veras et al. 2017b). Alternative methods of studying this clustering, such as economic base theory that divides urban economies into exporting and supporting industries, offer other insights into economic structures (Andrews 1953). However, in this analysis, the focus is on FIS and SIS because they have a more direct correlation with freight transportation. Having an understanding of the breakdown of the economy by FIS and SIS provides a better idea of the intensity of the activity in these regions. At the broadest scale, the economy can be defined as freight-inclined or service-inclined, depending on whether the majority of the employees work in FIS or SIS. The team analyzed the 381 MSAs in the United States using the 2015 County Business Pattern Data from the Census Bureau. The analyses showed that 67% of the MSAs have freight-inclined economies, while 33% of the MSAs have service-inclined economies. Figure 9 shows the difference between employment in the FIS and the SIS against the MSA population. Positive values in the vertical axis indicate a freight-inclined economy, and Figure 8. Map of selected cities and MSAs with population.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 29   negative values indicate a service-inclined economy. While some MSAs may be classied as service- intensive, freight activity remains signicant in all cities. For example, in Washington, DC, one of the most service-inclined cities, there are over 50,000 freight trips every day, creating signicant externalities to the city. Overall, SIS establishments still depend on freight activities to function. A map illustrating the inclination of the 381 MSAs in the United States can be found in Figure 10. Figure 10 shows some noteworthy patterns within freight-inclinations can be observed. For example, freight-inclined MSAs line the majority of the Gulf Coast, where the primary economic activities include shing, port operations, and oil and gas production. Freight-inclined MSAs N A IC S Freight-Intensive Sectors (FIS) N A IC S Service-Intensive Sectors (SIS) 11 Agriculture, Forestry, Fishing, Hunting 51 Information 21 Mining, Quarrying, and Oil and Gas Extraction 52 Finance and Insurance 22 Utilities 53 Real Estate and Rental and Leasing 23 Construction 54 Professional,Scientific,Tech. Services 31-33 Manufacturing 55 Management of Companies and Enterprises 42 Wholesale Trade 56 Administrative,Support,Waste Manag. 44-45 Retail Trade 61 Educational Services 48-49 Transportation and Warehousing 62 Health Care and Social Assistance 72 Accommodation and Food Services 71 Arts, Entertainment, and Recreation 81 Other Services 92 Public Administration Table 1. Classication of Industry sectors based on freight and service activity. Trenton, NJ Washington, DC Boston, MACalifornia, MD New York, NY Durham, NC Bridgeport, CT Philadelphia, PA Springfield, IL Baltimore, MD Homosassa Springs, FL San Francisco, CA Des Moines, IA Albany, NY Minneapolis, MNCorvallis, OR Los Angeles, CA Chicago, ILSeattle, WA Dallas, TX Portland, OR Toledo, OH New Orleans, LA Houston, TX Louisville, KY Charleston, SC Grand Rapids, MI Riverside, CAStockton, CA Myrtle Beach, SC Las Vegas, NV Atlantic City, NJ Kingsport, TN Greeley, CO Kokomo, IN El Centro, CA Merced, CA Midland, TX Odessa, TX Altoona, PA Dalton, GA Elkhart, IN -40% -30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 50,000 500,000 5,000,000 D iff er en ce in Pe rc en ta ge of To ta lE m pl oy m en t % in FI S- % in SI S Population Fr ei gh t-i nc lin ed Se rv ic e- in cl in ed Figure 9. Economic inclination of MSAs versus population.

30 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools Figure 10. Location of freight- or service-inclined MSAs. are found along the Coastal Plain and the coastlines from North Carolina to Florida, represent- ing industries including fishing, farming, and forestry. Across the Great Plains and the Wheat Belt, extending north to south from North Dakota and Montana to Texas, clusters of freight- inclined MSAs can be found. In California, the Central Coast and Central Valley regions are mainly freight-inclined, as the primary industry is agriculture and food processing. Figure 11 shows the percent breakdown between FIS and SIS for employment in Seattle-Tacoma-Bellevue, Washington, and Dayton, Ohio, two distinct MSAs in terms of size and geography. The urban cores in Seattle and Dayton are located in King County and Montgomery County, respectively. As shown, the higher the level of urbanization, the larger the significance of service activities in the local economy. One of the key reasons is that the SIS are better positioned to be willing and able to pay the high land costs at city centers. In contrast, the only FIS that can typically afford these land costs are consumer-oriented establishments, such as restaurants and hotels. The rest of the FIS establishments tend to be located outside of the principal urban core where land costs are lower. Their production supports cities and downtown areas. Data for this analysis includes the percent rural at the county level, provided by the U.S. Census Bureau (2010). 5.1.1 Urban Economies This section discusses the interconnections between population, employment, number of establishments, and FTG. The research conducted has revealed that these variables are highly

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 31   correlated with each other. The reason is that in freely mobile economies, people move to the metropolitan areas where the economy is good, and employment is high. These conditions also lead to large numbers of commercial establishments, large amounts of supplies produced and consumed, and large volumes of FTG. It should be noted that the analyses in this section only include the FTG associated with B2B transactions. Figure 12 illustrates the relationship between these variables for the nine cities. The largest U.S. city, NYC (population of 8,560,072), has 245,009 establishments, with about 3.8 million employees and about 87,000 daily freight trips, while Boca Raton Florida (population of 91,702), the smallest city included in the analysis, has 11,071 establishments, 140,448 employees, and 33,852 daily freight trips. A midsize city, such as Albuquerque, New Mexico (population of 556,859), has 15,403 establishments, 269,752 employees, and 64,428 daily freight trips. 43% 57% 54% 46% 59% 41% FIS SIS 51%49% 49%51% 68% 32% FIS SIS Inner Ring: King County (3.2% rural) Middle Ring: Pierce County (6.6% rural) Outer Ring: Snohomish County (10.8% rural) Seattle, Washington Inner Ring: Montgomery County (4% rural) Middle Ring: Greene County (15% rural) Outer Ring: Miami County (31% rural) Dayton, Ohio Figure 11. Comparison of FIS and SIS employment across counties in an MSA. 1,000 10,000 100,000 1,000,000 50,000 500,000 5,000,000 Em pl oy m en t, B 2B F TG , or E st ab lis hm en ts Population Figure 12. Indicators of the size of urban economies relative to population.

32 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools The case of Boca Raton deserves specific mention because of its tourist-based economy, its large numbers of restaurants, retail stores, and hotels, and its floating population. As shown, the employ- ment is higher than its population. On account of these factors, Boca Raton’s FTG is far higher than a more typical city of similar size. This explains why the FTG for Boca Raton is above the FTG trendline in Figure 12. The statistical assumptions between variables could be exploited to develop techniques to estimate order-of-magnitude of FTG in a city as a function of its population, employment, or number of establishments. The regression model results are statistically significant, as shown in Table 2. It should be noted that the estimates produced by the models that use population or number of establishments are more reliable than the one that uses employment. The reason is that large numbers of establishments have constant FTG that does not depend on employment. Table 3 shows the breakdown of establishments and employment for FIS. The results show that in most cases, the shares of FIS hover around 39% for the number of establishments, and 41% for the employment. The remaining establishments and employment R2 Estimated FTG = 0.1034*City Population Estimated Employment = 0.4443*City Population Estimated Establishments = 0.0289*City Population 0.986 Urban Area Models 0.976 0.984 Table 2. Urban economy models. NA IC S Description Ne w Yo rk , NY Lo s A ng el es , CA Ph ila de lp hi a, PA Au sti n, T X W as hi ng to n, DC Al bu qu er qu e, N M Ci nc in na ti, O H Ja ck so n, M S Bo ca R at on , FL 44-45 Retail Trade 35.1% 27.3% 37.0% 31.9% 31.1% 31.9% 38.7% 31.9% 72 Accommodation and Food Svcs. 23.5% 20.4% 32.4% 46.8% 22.0% 22.7% 22.6% 18.5% 42 Wholesale Trade 16.0% 22.2% 8.4% 8.4% 12.8% 15.5% 14.7% 20.9% 23 Construction 14.6% 12.7% 11.0% 8.0% 20.4% 14.7% 11.3% 19.2% 31-33 Manufacturing 5.2% 11.9% 5.8% 2.0% 8.5% 10.2% 6.4% 4.6% 48-49 Transp. and Warehousing 5.6% 5.5% 5.5% 2.9% 5.2% 5.1% 6.3% 4.8% Establishments (FIS) in 1,000 103.2 46.3 12.4 5.6 6.5 8.9 2.7 3.4 Establishments (Total) in 1,000 245.0 123.0 28.0 22.9 15.4 21.2 6.3 11.1 % FIS Establishments of Total 42.1% 37.6% 44.2% 24.7% 42.1% 42.2% 42.9% 30.4% 44-45 Retail Trade 28.7% 23.6% 29.0% 19.9% 30.5% 25.3% 30.7% 33.6% 72 Accommodation and Food Svcs. 31.0% 28.4% 33.4% 62.1% 29.6% 24.8% 24.0% 34.1% 42 Wholesale Trade 13.7% 15.3% 9.1% 4.2% 9.7% 13.3% 13.9% 15.2% 23 Construction 12.4% 7.6% 6.7% 9.0% 14.5% 9.7% 10.0% 9.8% 31-33 Manufacturing 5.7% 16.7% 11.2% 1.2% 10.0% 19.9% 10.7% 4.6% 48-49 Transp. and Warehousing 8.6% 8.4% 10.6% 3.6% 5.6% 7.2% 10.8% 2.7% Employment (FIS) in 1,000 Employment (Total) in 1,000 % FIS Employment of Total 36.1% 44.4% 36.3% 24.8% 49.1% 44.7% 48.6% 36.5% Population in 1,000 Establishments (FIS)/1,000 residents Employment (FIS)/1,000 residents Employment / establishment (FIS) % of Establishments % of Employment Indicators 1,366.6 756.4 201.9 127.0 132.5 220.1 60.2 51.3 3,786.2 1,705.5 555.6 512.0 269.8 492.5 124.1 140.4 8,560.1 3,918.9 1,559.9 672.4 556.9 298.0 172.0 91.7 12.1 11.8 7.9 8.4 11.6 30.0 15.8 36.7 159.6 193.0 129.4 188.9 238.0 738.7 350.1 559.0 13.2 16.3 16.3 32.2% 26.8% 12.4% 17.9% 6.8% 4.0% 11.7 34.8 33.7% 27.5% 34.2% 11.3% 12.3% 9.3% 5.3% 41.2% 268.5 651.9 916.9 12.8 292.9 22.9 22.5 20.5 24.6 22.2 15.2 Table 3. Establishments and employment by FIS for cities.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 33   correspond to SIS. ese percentages vary depending on the local economy. In NYC, 42% of establishments and 36% of employment are in FIS, while Boca Raton has only 30% of establish- ments in FIS, but still 36% of employment. e exception is Washington, DC, where only 27% of establishments are in FIS, representing just 25% of the city’s employment. ese statistics make it clear that Washington, DC, is more service-inclined than the typical city. e table also shows a number of indicators that reect the statistical interconnections between the variables considered. e number of FIS establishments per 1,000 residents ranges from 8 to 13 across medium and large cities. In small cities, this statistic ranges from 16 to 37 establishments per 1,000 residents. e average employment at FIS establishments ranges from 13 to 25 among all cities. e top two FIS are consumer-oriented establishments. Retail Trade (NAICS 44-45) is at the top of the list, with 27% to 38% of all establishments, and 20% to 34% of all employment in the FIS. Together with Accommodation and Food Services (NAICS 72), they represent about half of the FIS in urban economies (48% to 78% of all establishments and 50% to 82% of employment in the FIS). In contrast, companies in the Transportation and Warehousing sector (NAICS 48-49) only represent a small fraction of the urban economies (3% to 6% of all establishments and 3% to 10% of employment in the FIS). Ironically, these companies are frequently believed to be LTGs because of their typically large facilities. ese results make clear that, as a proportion of the total FTG in the areas, their contributions to FTG are relatively minor. 5.1.2 Metropolitan Economies An analysis similar to that in the previous section was conducted at the level of MSAs. e results, shown in Figure 13, clearly indicate that the statistical assumptions between popu- lation and the other variables are even tighter than in the urban case. As shown in Table 4, the regression models are statistically signicant, and the order-of-magnitude models have stronger goodness-of-t statistics. Table 5 contains the percentage breakdown of FIS establishments and employment for the selected MSAs. As in the results for urban areas, the sectors with the highest percentage of 1,000 10,000 100,000 1,000,000 10,000,000 250,000 2,500,000 25,000,000 Em pl oy m en t, B2 B FT G , or Es ta bl is hm en ts Population Figure 13. Indicators of the size of MSA economies relative to population.

34 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools employment are Retail Trade and Accommodation and Food Services, both sectors that require high levels of interaction with consumers. The percentage of total establishments and total employment for consumer-oriented sectors is slightly lower than the ones reported in the cities, an indication that these activities are more concentrated in urban areas where the population is also more concentrated. In contrast to the industry sector’s composition in the urban areas, Construction is the third largest in terms of employment and establishments for the analyzed MSAs, indicating that there is a greater concentration of construction establishments outside of urban areas. This type of establishment usually requires facilities with large floor areas. Suburban areas have land availability and costs that are favorable for those types of facilities. Similar to the urban areas, the Transportation and Warehousing sector only represents a small proportion of establishments and employment (less than 10%). The average number of establishments per resident in FIS is steady across the metropolitan areas (between 9 and 12 establishments per 1,000 residents), which shows that the demand for FIS establishments is steady across MSAs regardless of the MSA’s population. On the other hand, employment in FIS per 1,000 residents varies from 151 in the Washington, DC, MSA to 243 in the Toledo MSA. This variation can be explained by the different composition of industry sectors across MSAs. N A IC S Description N ew Y or k M SA L os A ng el es M SA C hi ca go M SA H ou st on M SA W as hi ng to n, D C , M SA Se at tle M SA Po rt la nd M SA N ew O rl ea ns M SA A lb an y M SA T ol ed o M SA K in gs po rt M SA 44-45 Retail Trade 72 Accommodation and Food Svcs. 23 Construction 42 Wholesale Trade 31-33 Manufacturing 48-49 Transp. and Warehousing Establishments (FIS) in 1,000 Establishments (Total) in 1,000 % FIS Establishments of Total 44-45 Retail Trade 72 Accommodation and Food Svcs. 23 Construction 42 Wholesale Trade 31-33 Manufacturing 48-49 Transp. and Warehousing Employment (FIS) in 1,000 Employment (Total) in 1,000 % FIS Employment of Total Population in 1,000 Establishments (FIS)/1,000 residents Employment (FIS)/1,000 residents Employment / establishments (FIS) % of Establishments % of Employment Indicators 32.2% 20.6% 19.6% 15.0% 6.3% 6.2% 238.4 550.2 43.3% 30.6% 23.3% 12.1% 14.8% 10.4% 8.7% 20,275 11.8 165.8 14.1 3,362.0 8,032.2 41.9% 26.6% 20.3% 14.0% 21.3% 11.8% 6.0% 145.9 357.6 40.8% 24.8% 25.1% 9.2% 15.3% 18.8% 6.7% 13,328 10.9 199.0 18.2 2,652.9 5,501.1 48.2% 26.1% 18.9% 20.1% 13.5% 9.6% 11.5% 108.7 245.8 44.2% 26.3% 21.7% 9.3% 13.4% 20.2% 9.0% 9,546 11.4 214.5 18.8 2,047.7 4,263.7 48.0% 30.5% 20.1% 15.3% 15.8% 9.0% 6.8% 59.9 134.0 44.7% 23.3% 21.6% 14.3% 12.1% 16.7% 7.8% 6,798 8.8 205.5 23.3 1,396.7 2,633.6 53.0% 32.7% 25.7% 24.3% 8.1% 4.0% 5.1% 48.3 143.6 33.7% 32.9% 31.9% 17.1% 6.7% 5.5% 5.8% 6,151 7.9 151.1 19.2 929.5 2,604.5 35.7% 25.8% 21.6% 25.4% 12.8% 8.3% 6.0% 42.4 99.5 42.6% 26.7% 23.6% 15.2% 11.6% 14.8% 8.0% 3,803 11.1 199.7 17.9 759.3 1,608.5 47.2% 25.4% 20.8% 23.7% 12.6% 11.1% 6.3% 29.7 67.5 44.0% 25.5% 21.7% 12.6% 12.5% 20.3% 7.3% 2,423 12.2 220.2 18.0 533.5 1,054.9 50.6% 32.7% 25.5% 16.1% 11.5% 5.3% 7.9% 12.6 27.6 45.8% 26.0% 32.1% 11.0% 9.5% 9.6% 10.0% 1,271 9.9 201.8 20.3 256.5 484.0 53.0% 32.0% 23.8% 23.9% 9.3% 6.4% 4.5% 10.3 22.4 45.9% 34.2% 23.3% 12.1% 9.5% 13.5% 7.0% 883 11.6 194.9 16.8 172.0 375.9 45.8% 30.7% 23.7% 15.3% 11.8% 11.3% 6.9% 6.6 13.9 47.1% 25.4% 21.7% 9.4% 9.4% 26.5% 7.5% 605 10.8 242.9 22.4 146.9 276.2 53.2% 39.7% 19.8% 14.2% 9.6% 9.6% 5.9% 2.8 5.7 49.7% 30.7% 20.9% 10.5% 6.0% 24.2% 6.0% 306 9.2 188.2 20.4 57.6 94.3 61.0% R2 Estimated FTG = 0.1033*City Population Estimated Employment = 0.4073*City Population Estimated Establishments = 0.0262*City Population 0.990 MSA Linear Models 0.986 0.996 Table 4. MSA economy models. Table 5. Establishments and employment by FIS for MSAs.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 35   5.1.3 Geographical Distribution of Economic Activity The analyses of the composition of the economy provide insight into the main contributors of freight activities. However, it is equally important to understand how these economic activities are distributed across the geographic areas. Of great interest to the study of freight efficiency is the analysis of the physical separation between the establishments that primarily produce goods and the establishments that primarily consume goods. Parts of the metro areas with a large number of manufacturing establishments are associated with the production of supplies con- sumed elsewhere. In contrast, areas with a high density of retail establishments tend to consume the supplies produced by others. The spatial separation between the production (supply) and consumption (demand) zones has a direct impact on the distance for deliveries. An important component of the analyses of the geographic patterns of economic activities requires considering how these activities are located in relation to the economic pole, which is the economic heart of the metropolitan area that underpins the metropolitan economy. Once the loca- tion of the economic pole has been determined, it is possible to assess how spread out the economic activities are, by a simple tabulation of the total employment, number of establishments, or FTG that takes place at various intervals of distance. However, the analyses in this section are based on the density per miles squared of total employment, number of establishments, and FTG. Densities are used because, since the analyses use zip code level data, they eliminate the distorting effect of the size of the zip code (larger zip codes tend to have larger numbers of employment, establishments, and FTG). In addition, using densities is convenient for land-use analyses because they provide an intuitive metric of the intensity of the use of space. See Figure 14. - 500 1,000 1,500 2,000 2,500 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Es ta bl ish m en t D en sit y (E st ab lis hm en ts /m i2 ) Distance from the Main Economic Pole (miles) Los Angeles MSA FIS SIS - 500 1,000 1,500 2,000 2,500 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Es ta bl ish m en t D en sit y (E st ab lis hm en ts /m i2 ) Es ta bl ish m en t D en sit y (E st ab lis hm en ts /m i2 ) Distance from the Main Economic Pole (miles) Washington, DC, MSA FIS SIS - 25 50 75 100 125 150 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Distance from the Main Economic Pole (miles) Albany MSA FIS SIS Figure 14. Spatial distribution of establishment density from economic pole.

36 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools The distribution of establishments is not homogenous throughout the entire metropolitan area. Most of the large metropolitan areas in the United States have an urban core with a high density of both population and businesses. The expectation is that the various density metrics decrease with distance from the economic pole. This trend is evident in all sizes of MSAs, although the rate of decline varies. The larger MSAs, such as Los Angeles, California, and Washington DC, tend to have a sharp decline, while smaller MSAs, such as Albany, New York, exhibits a much more gradual decline. Both FIS and SIS establishments are found throughout the MSA. Close to the urban core, the density of SIS establishments is generally higher than the density of FIS establishments. Farther away from the economic pole, SIS and FIS establishments are concentrated more evenly. This is an expected result: SIS establishments tend to be located closer to economic poles because they rely on densely populated areas to have access to employees and customers. In contrast, FIS establishments tend to be located further away due to high land costs near economic poles. Figure 14 shows the spatial distribution of economic activities. In Los Angeles, the high density of commercial establishments at the economic pole—downtown Los Angeles—exhibits high density for both FIS and SIS. In the case of Washington, DC, there is a dominance of SIS activities across all distances. This is a reflection of its predominantly service economy. Albany, New York, notwithstanding being the state capital, reflects the more typical pattern of midsize metropolitan areas, where the economy is split in half between FIS and SIS. Economic activities such as Retail Trade, Accommodation and Food Services, and SIS tend to peak at the economic pole and rapidly decrease with distance from the pole. Other activities, such as Transportation and Warehousing, tend to peak elsewhere. This pattern could be problematic, because it could create large inefficiencies as the bulk of the establishments that need supplies are far away from the warehouses and distribution centers that deliver the supplies needed. 5.1.4 Natural Clusters of Metropolitan Statistical Areas The main objective of these analyses was to identify MSAs that exhibit similar economic char- acteristics despite coming from different geographical regions of the United States. Although it is not possible to find two MSAs that are exactly the same, creating clusters with statistically similar MSAs could still be useful. The resulting clusters could be used by planners to identify peer MSAs. Among other benefits, lessons learned in one MSA are more likely to apply to other cities in the cluster. To explore this idea, a statistical technique called cluster analysis was used to identify natural groupings of MSAs. The clusters group MSAs based on characteristics relevant to freight activity. The characteristics used include socioeconomic criteria, such as population and employment, the distribution of freight activities, and average commute time. Five variables were found to be significant: population, population density, average commute time, FTG within the MSA, and the interaction index described in Appendix B. The interaction index was found to be the most important variable. The clustering algorithm separated all the country’s MSAs into six clusters. These six clusters were further split into freight-inclined and service-inclined economies. Two of the algorithm-defined clusters were eliminated because they did not contain any freight-inclined economies, which resulted in a total of 10 clusters. Table 6 lists the range of the values of the variables for each cluster, as well as examples of MSAs present in each cluster. The list of MSAs is shown in Appendix C. Cluster 1 contains NYC, which due to its size, connections to the rest of the United States, and high freight traffic, is not comparable with any other MSA in the country. Cluster 2 contains four national centers with service-inclined economies, including Chicago and Los Angeles, which are by far the largest MSAs in their respective regions. Houston, the only MSA in Cluster 3, is the largest MSA with a freight-inclined economy. Cluster 4 contains six cities with service-inclined economies that are major regional centers, such as Atlanta and Boston. Clusters 5 and 6 are medium-large MSAs that serve as major regional centers. Clusters 7 and 8 are mostly medium-sized

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 37   MSAs. Clusters 9 and 10 contain the many small MSAs in the United States, with no member of these two clusters having a population over 1 million. 5.2 Characterization of Supply Chains Economies, whether urban or metropolitan, are the result of the interactions between suppliers and their consumers. Each interaction between these agents represents a single component within a particular supply chain. The concatenation of these interactions combine to shape supply chains. The main function of supply chains is to manage the interconnections between the places where the supplies are produced and the places where they are consumed, although supply chains are typically associated with the transportation function. This section describes the key characteristics of supply chains. A supply chain can be differentiated based on the extent of its network or market: (1) local, (2) metropolitan, (3) national and regional, or (4) global. On a global scale, a store in the Midwest may sell T-shirts made in Bangladesh; a diner at a Seattle restaurant may enjoy Kobe beef; a shopper in Memphis may be pondering whether to buy high-end leather boots from Finland or a pair made in Australia. On a regional and national scale, Florida oranges, Idaho potatoes, and California wine travel to grocery stores throughout the country. On a local scale, farm-to-table restaurants source their ingredients from their local suppliers. Even though the supply chains may appear to operate in different networks, the levels of the supply chain are interconnected. Figure 15 is a representation of a generic supply chain that participates in the four network levels, from global to local. Also, this supply chain has participants from the urban, metropolitan, and country areas, and abroad. The schematic provides an example of how an end receiver located at the core of the urban area may obtain its products not only from a local distribution center or supplier, but also from a port located in its metropolitan area. Similarly, this local distributor could have obtained MSA Cluster Number of MSAs in Cluster Economy Type Popu- lation (millions) Popu- lation Density Interaction Index (millions) Average Commute Time (min) Freight Trips Generated (thousands) Examples of MSA 1 1 Service- Inclined 20.2 2,234 559 35.6 1,816 New York. NY 2 4 Service- Inclined 6.1 - 13.3 951 - 2,711 176 - 224 29.0 - 34.3 402 - 1,217 Chicago, IL; Los Angeles, CA; Washington DC 3 1 Freight- Inclined Houston, TX 4 6 Service- Inclined Atlanta, GA; Baltimore, MD; Boston, MA; Detroit, MI 5 1 Freight- Inclined Riverside, CA 6 24 Service- Inclined Austin, TX; Indianapolis, IN; Orlando, FL; Seattle, WA 7 32 Freight- Inclined Fresno, CA; Memphis, TN; New Orleans, LA 8 35 Service- Inclined Albany, NY; Portland, OR; Toledo, OH; Trenton, NJ 9 220 Freight- Inclined Burlington, VT; Kingsport, TN; Napa, CA; Savannah, GA 10 57 Service- Inclined Anchorage, AK; Charleston, WV; Iowa City, IA 0.0 - 0.9 1 - 1,616 0 - 10 11.3 - 38.7 0 - 77 0.4 - 2.4 97 - 1,631 10 - 33 19.0 - 28.1 34 - 183 0.9 - 4.7 282 - 1,819 36 - 73 23.2 - 31.4 98 - 385 2.8 - 7.1 645 - 1,298 90 - 126 26.6 - 30.7 224 - 641 Table 6. Clusters of MSAs.

38 Planning Freight-Efcient Land Uses: Methodology, Strategies, and Tools its merchandise from a national distributor or supplier, or from the same port in the metropolitan area. In this schema, the national distributor obtained its goods from abroad. us, to provide goods to the end consumer at the local level, the globally traded goods have to move from the global network to the regional and metropolitan network, before reaching the local network. Understanding the true magnitude of freight activity in metropolitan areas is imperative for freight planning purposes, because all levels of the supply chain come together in a metropolitan area. e ow of the global, national, and regional networks can be observed coming through the gateways, such as airports and marine terminals. e goods are distributed to the metro- politan network through suppliers, distributors, and receivers that serve the metropolitan area. e metropolitan networks are constrained by the boundaries of the metropolitan areas, while the local network is limited to a small area within the metropolitan area, such as a county or neighborhood. Understanding the key aspects of supply chains allows for better characterization of them. e primary function of supply chain activity is to ensure that all participants in a production system—from manufacturers to consumers—have access to the supplies needed. Due to the ow of goods between participants, each participant typically performs at least one of the following functions: production, distribution, or consumption of goods. For example, an establishment has the function of production when it is shipping out goods from its facility, but at the same time, it consumes goods (consumption) when it receives raw materials from a supplier. Beyond this, when looking at a metropolitan area of interest like the one depicted in Figure 15, each participant can be classied to be playing one of the following main roles: • Gateways are transportation facilities—marine ports, airports, intermodal terminals, highway access points and the like—that provide critical interconnections with supply chains beyond the metropolitan region. In most cases, these gateways interact primarily with suppliers and distributors. • Suppliers are commercial establishments that mainly undertake the physical transformation of input supplies into either nal or intermediary goods. ey could be physically located inside or outside of the metropolitan region. In both cases, they likely ship goods to distributors. Legend: Global Metropolitan National/Regional Local Figure 15. Schematic of generic supply chain.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 39   • Distributors are commercial establishments—mostly warehouses and distribution centers— that store, process, and distribute supplies from the suppliers to the receivers, and, in some cases, get involved in reverse logistics. They represent the middleman in the supply chains. Distributors could be located inside or outside of the metropolitan region. • Receivers are commercial establishments whose primary function is consumer-oriented (e.g., restaurants and retail stores). These establishments are frequently the terminus of supply chains. They usually receive goods from distributors but may receive them from suppliers in certain cases. They also contribute to the bulk of freight trips in metropolitan areas. House- holds are be included as receivers, which is critical in accounting for the surge of internet deliveries. Readers interested in additional information about typical supply chains are advised to consult Appendix D, which contains descriptions of typical supply chains. 5.3 Freight Trip Generation Cities and metropolitan areas rely on the movement of freight to sustain modern life and the economy. However, the associated freight activity generates high volumes of vehicle trips, creating challenges such as congestion, pollution, noise, and safety concerns. Business estab- lishments in densely developed urban areas need freight activity to be concentrated in the same areas as residents live, which further contributes to these externalities. It is important to understand freight activity patterns to foster growth and prosperity while minimizing negative externalities. The term freight activity collectively refers to all manifestations of production and supply chain systems—the flows of freight (the supplies) and freight trips (the vehicles), and the associated pickups and deliveries—that materialize in the economy. Freight activity is generated at both commercial establishments and households. In the com- mercial case, it is a result of the logistics decisions made by both the business that sends out the shipment, and the business that receives it. In making these decisions, managers must determine the best combination of delivery frequency and shipment size. In the household case, these decisions are determined by the needs of household members. These deliveries and shipments are transported by freight vehicles, which could transport multiple deliveries and shipments in the same vehicle trip. The larger the number of deliveries and shipments trans- ported, the lower the number of freight trips created. Two important expressions of freight activity are freight generation and FTG, which were defined in (Holguín-Veras et al. 2012a; Holguín-Veras et al. 2017b) as follows: • Freight generation is the amount of cargo—typically measured in units of weight such as pounds/day or tons/day—generated by a commercial establishment. Freight generation is the sum of freight attraction and freight production, which are defined as follows: – Freight attraction is the amount of cargo that is brought to the establishment to be processed, stored or sold to customers. Most establishments receive supplies. – Freight production is the amount of cargo sent out of the establishment for use at another establishment. Typically, establishments that sell final products to consumers, such as retail stores, do not have any freight production. • FTG is the number of freight vehicle trips generated by a commercial establishment. FTG is the sum of the freight trip attraction and freight trip production, which are defined as follows: – Freight trip attraction is the number of freight vehicle trips arriving at the establishment to transport the freight attraction. Most establishments receive freight vehicle trips. Delivering supplies to an establishment will create two vehicle trips (inbound and outbound).

40 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools – Freight trip production is the number of freight vehicle trips that depart from the establish- ment to transport cargo to other destinations. Establishments that do not send cargo out or have freight production will not generate freight trip production. In most cases, picking up a shipment from an establishment will necessitate two vehicle trips (inbound and outbound). FTG is an important expression for land-use planning and policy. FTG models estimate the number of freight trips that would be generated by a given land-use development. Equally important is that the estimation of the FTG, as part of the NCFRP Report 37, has simplified the modeling process. These FTG models are used in this section. The models predict freight ship- ments sent and freight deliveries received as a function of the establishment’s industry sector (two-digit NAICS) and its employment, which is a measure of the intensity of economic activity at a location. Employment is used for several reasons. First, an establishment with more employees is expected to conduct more business activity than an establishment with fewer employees, resulting in higher FTG. In addition, data on employment are readily available, meaning that these models can be applied without the need to collect costly data. The empirical tests conducted found that these models are significantly more accurate than any of the models from the litera- ture (Holguín-Veras et al. 2011a; Holguín-Veras et al. 2013a). The main reason is because they use employment as the key explanatory variable to capture the intensity of the economic activity being performed at an establishment, which leads to a better prediction of FTG. The NCFRP models have been found to be transferable across U.S. cities (Beagan et al. 2007; Institute of Transportation Engineers 2008). To estimate freight trip patterns in cities, the 2016 County Business Patterns (CBP) database was used, where data for the number of establishments by industry sector (up to six-digit NAICS) and by employment bin in each zip code are available for the entire United States (U.S. Census Bureau 2018a). The models were applied to the database at a zip code and two-digit NAICS level, and then aggregated at the city and MSA levels. The analyses shown below correspond to both freight trips generated by business establishments—collectively called B2B trips—and freight needs at households as a result of internet purchases, referred to as B2C trips. B2B trip estimates are from the models developed by the team and will be referred to as B2B FTG, while B2C FTG trips are estimated by applying a rate that has been determined from data collected about the number of internet purchases to households for different periods (2009 and 2017) using the National Household Travel Survey (NHTS) (Wang and Zhou 2015). 5.3.1 Freight Trip Generation Patterns in Cities To give an indication of the level of congestion created by B2B FTG, some assumptions are made to convert deliveries and shipments to vehicle trips. In the case of FTG, the analy- sis considers one delivery to be equal to one trip, the reason being that the carrier market is highly competitive, and there is much less consolidation than with the B2C deliveries and ship- ments. In the latter case, with only a handful of companies making deliveries, it is reasonable to assume that each vehicle makes multiple deliveries as part of a single trip. For this analysis, the assumption made was five B2C deliveries per freight trip. Table 7 shows the results for the selected cities. The FTG estimates in this section were obtained using the FASTGS, which uses the econometric models from (Holguín-Veras et al. 2017b). The results for the smaller cities indicate that approximately 6,000 to 11,000 establishments generate approximately between 27,000 to 34,000 daily freight trips. The larger cities generate at least 10 times more. In Los Angeles, 123,000 establishments generate 443,000 daily freight trips. The table also shows the magnitude of B2C at the city level, which is quite significant in the densest cities. The largest and densest city, New York, generates 257,000 additional daily freight trips due to internet deliveries to households. For the midsize cities, B2C FTG ranges

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 41   from 7,000 (Cincinnati, Ohio) to 41,000 (Philadelphia, Pennsylvania). It should be noted that, because the cities selected are the economic centers of their MSAs, their freight activity is larger than comparable cities of their size that are not the economic centers of their MSAs. In some cities, the total employment is even larger than the resident populations (i.e., Cincinnati and Boca Raton). Table 8 also shows the total FTG for FIS. The bulk of FTG comes from FIS. Across these cities (excluding Washington, DC), FIS comprise 39% of the establishments and 41% of employment. However, in the case of FTG, it is a different story. For all cities analyzed, except for Washington, DC, and Boca Raton, more than 90% of all freight activity is generated at the FIS. The results for B2B FTG range from 27,000 (Jackson, Mississippi) to 873,000 (New York, New York) trips per day. N ew Y or k, N Y L os A ng el es , C A Ph ila de lp hi a, P A A us tin , T X W as hi ng to n, D C A lb uq ue rq ue , N M C in ci nn at i, O H Ja ck so n, M S B oc a R at on , F L Population (2016) Establishments Employment B2B FTG/day Establishments Employment B2B FTG/day Establishments Employment B2B FTG/day B2C Deliveries B2C Trips 8,560.07 3,918.87 1,559.94 916.91 672.39 556.86 298.01 172.04 91.70 245.01 123.00 27.95 34.79 22.86 15.40 21.17 6.33 11.07 3,786.19 1,705.47 555.59 651.92 511.97 269.75 492.53 124.05 140.45 873.38 443.37 105.35 121.12 56.69 64.43 95.82 27.10 33.83 103.19 46.27 12.35 11.74 5.64 6.48 8.94 2.72 3.36 1,366.60 756.38 201.86 268.54 127.03 132.52 220.14 60.23 51.26 802.47 405.01 97.55 109.59 48.09 59.96 89.71 25.30 29.98 141.82 76.73 15.60 23.05 17.22 8.93 12.23 3.61 7.71 2,419.59 949.09 353.72 383.39 384.93 137.23 272.39 63.82 89.19 70.91 38.36 7.80 11. 52 8.61 4.46 6.11 1.80 3.85 1,284.01 587.83 202.79 110.03 80.69 66.82 35.76 18.92 10.09 256.80 117.57 40.56 22.01 16.14 13.36 7.15 3.78 2.02 1,130.18 560.94 145.91 143.12 72.83 77.79 102.97 30.89 35.85Total Trips (B2B+B2C) C om m er - ci al FI S SI S H H s Note: HHs means households. Table 7. Results for selected cities (in thousands). N A IC S Description N ew Y or k, N Y Lo s A ng el es , C A Ph ila de lp hi a, P A A us tin , TX W as hi ng to n, D C A lb uq ue rq ue , N M C in ci nn at i, O H Ja ck so n, M S Bo ca R at on , F L 44-45 Retail Trade 27.4% 22.6% 28.5% 32.0% 26.2% 31.7% 33.1% 37.7% 35.2% B2C Internet Deliveries to Households 24.2% 22.5% 29.4% 16.7% 25.1% 18.2% 7.4% 13.0% 6.3% 72 Accommodation and Food Svcs. 12.2% 11.4% 15.0% 17.6% 30.6% 13.7% 14.5% 10.1% 14.1% 42 Wholesale Trade 16.9% 21.2% 8.5% 12.9% 8.2% 12.9% 16.9% 15.5% 24.2% 48-49 Transp. and Warehousing 9.8% 8.8% 9.0% 6.5% 4.4% 8.1% 8.3% 8.5% 9.0% 31-33 Manufacturing 4.0% 9.4% 6.1% 6.9% 1.3% 7.5% 13.6% 7.1% 4.5% 23 Construction 5.5% 4.1% 3.6% 7.3% 4.2% 7.8% 6.2% 8.0% 6.7% FTG B2B (*) 802.5 405.0 97.6 109.6 48.1 60.0 89.7 30.0 B2C Deliveries (*) 1,284.0 587.8 202.8 110.0 80.7 66.8 35.8 10.1 B2C Freight Trip Generation (*) 256.8 117.6 40.6 22.0 16.1 13.4 7.2 2.0 Total Trips (B2B+B2C) (*) 1,059.3 522.6 138.1 131.6 64.2 73.3 96.9 25.3 18.9 3.8 29.1 32.0 FTG in Freight-Intensive Sectors Note: (*) denotes units in the thousands. Table 8. FTG breakdown for FIS in cities.

42 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools 5.3.2 Breakdown of Freight Activity in Cities by Industry Sector The breakdown among industry sectors in the FIS is shown in Table 8. The patterns shown among all cities are revealing. Without exception, primarily consumer-oriented establishments— such as retail establishments, restaurants, hotels, food stores, and big-box retailers—generate the bulk of freight traffic in FIS. These consumer-oriented sectors generate more than 50% of all freight traffic in the cities analyzed. Transportation and Warehousing represents less than 16% of the total. This finding is important when considering the location of warehouses and distribution centers in the city. Table 8 also shows that a significant proportion of freight traffic is generated by households. In large cities (i.e., New York, Los Angeles, Philadelphia, and Washington distribu- tion center), B2C traffic represents more than 22% of all freight traffic. In smaller cities, B2C represents between 6% (Boca Raton, Florida) to 18% (Albuquerque, New Mexico) of all daily traffic (B2B FTG + B2C FTG). It is also important to gain insight into the contribution to FTG produced by establishments of various sizes. Figure 16 shows that the bulk of the trips is generated by establishments with less than 10 employees (35% to 60% of all FTG). In New York, 45% of the trips are generated by establishments with less than four employees, and 13% in those establishments with 5 to 10 employees. In Los Angeles, it is nearly 52% for the establishments with less than 10 employees, and in Philadelphia, it is 48%. On the lower end is Jackson, with 35% of all FTG generated by the smaller establishments. These results have major implications to land use, as these smaller establishments typically do not have much space to accommodate large amounts of inventory, and compete for curb space to unload and load merchandise. 5.3.3 Indicators of Freight Activity in American Cities This section discusses a set of unit indicators that could be used to estimate freight activity, approximately, using total employment, number of establishments, or population. The team computed the unit indicators shown in Table 9 for selected cities. Table 10 lists the indicators for most freight-intensive industry sectors in the selected cities. Generally, establishments in FIS generate between 7 and 10 freight trips every day; or about 0.4 to 0.6 trips for each FIS employee in a city. In most cities, FIS produce between 9 and 17 trips per 100 people each day. This indicator is significantly larger in smaller cities with larger than typical FIS economies, such as Cincinnati (0.325) and Boca Raton (0.35). As shown, B2B FTG varies widely between industry sectors. In most cities, the largest share of freight trips is generated by the Retail Trade sector, followed by either Wholesale Trade or Accommodation and Food Services. The Transportation and Warehousing sector generates far 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 1 10 100 1000 Pe rc en t o f F TG Average employment per establishment Washington, DC Los Angeles, CA Jackson, MS Cincinnati, OH New York, NY Austin, TX Philadelphia, PA Philadelphia, PA Boca Raton, FL Figure 16. Percentage of total FTG by average employment.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 43   N ew Y or k, N Y L os A ng el es , C A Ph ila de lp hi a, P A A us tin , T X W as hi ng to n, D C A lb uq ue rq ue , N M C in ci nn at i, O H Ja ck so n, M S B oc a R at on , F L Population (2016) Total Area (mi2) Population density Establishments Employment B2B FTG FTG/(est-day) FTG/(empl-day) B2C Deliveries B2C FTG (5 deliv/trip) B2C Deliv/(person-day) B2C FTG/(person-day) FTG (Total) FTG/(person-day) 8,560,072 302.670 10,919.692 103,192 1,366,604 802,472 7.776 0.587 1,284,011 256,802 0.150 0.030 1,059,274 0.124 3,228.257 3,918,872 46,274 756,385 405,009 587,831 117,566 522,575 468.700 8.752 0.535 0.150 0.030 0.133 1,559,938 12,351 201,863 97,552 202,792 40,558 138,110 134.092 4,491.661 7.898 0.483 0.130 0.026 0.089 916,906 11,738 268,536 109,592 110,029 22,006 131,598 297.885 1,188.442 9.337 0.408 0.120 0.024 0.144 672,391 5,642 127,032 48,085 80,687 16,137 64,222 61.050 4,252.462 8.523 0.379 0.120 0.024 0.096 556,859 6,478 132,519 59,965 66,823 13,365 73,329 187.716 1,145.370 9.257 0.452 0.120 0.024 0.132 298,011 8,937 220,142 89,705 35,761 7,152 96,857 77.920 1,476.678 10.037 0.407 0.120 0.024 0.325 172,039 2,716 60,234 25,297 18,924 3,785 29,082 111.039 598.210 9.314 0.420 0.110 0.022 0.169 91,702 3,364 51,262 29,981 10,087 2,017 31,998 29.329 1,207.224 8.912 0.585 0.110 0.022 0.349 B2B FTG in Freight-Intensive Sectors Internet Deliveries to Households (B2C) Total Trips (B2B+B2C) Note: est = establishment; empl = employment; and Deliv = deliveries. Table 9. FTG indicators in selected cities. N ew Y or k, N Y L os A ng el es , C A Ph ila de lp hi a, PA A us tin , T X W as hi ng to n, D C A lb uq ue rq ue , N M C in ci nn at i, O H Ja ck so n, M S B oc a R at on , F L Population (2016) Total Area (mi2) Population density (pop/mi2) FIS Establishments FIS Employment B2B FTG Total per day B2B FTG/establishment B2B FTG/employment B2B FTG B2B FTG/establishment B2B FTG/employment B2B FTG B2B FTG/establishment B2B FTG/employment B2B FTG B2B FTG/establishment B2B FTG/employment B2B FTG B2B FTG/establishment B2B FTG/employment B2B FTG 1,559,938 12,351 201,863 39,308 11,705 20,718 12,440 4,997 8,385 B2B FTG/establishment B2B FTG/employment 8,560,072 103,192 1,366,604 290,260 178,696 128,916 104,297 58,275 42,027 302.670 10,919.692 8.019 0.741 10.835 0.954 5.316 0.305 18.173 0.892 3.859 0.344 7.764 0.538 3,918,872 46,274 756,385 117,893 111,022 59,448 46,117 21,396 49,133 468.700 3,228.257 9.337 0.660 10.826 0.960 6.282 0.276 18.192 0.727 3.646 0.373 8.891 0.389 134.092 4,491.661 8.613 0.672 11.342 0.635 5.178 0.308 18.402 0.580 3.663 0.368 11.743 0.372 916,906 11,738 268,536 42,170 17,033 23,173 8,550 9,629 9,037 297.885 1,188.442 11.174 0.571 11.690 0.559 7.375 0.252 18.077 0.597 4.594 0.291 11.353 0.362 672,391 5,642 127,032 16,828 5,247 19,674 2,829 2,690 818 61.050 4,252.462 9.349 0.666 11.022 0.994 7.452 0.249 17.355 0.613 5.990 0.234 7.176 0.552 556,859 6,478 132,519 23,272 9,466 10,048 5,932 5,717 5,531 187.716 1,145.370 11.566 0.575 11.377 0.736 7.046 0.256 17.550 0.792 4.334 0.297 10.038 0.419 298,011 8,937 220,142 32,083 16,385 14,009 8,085 6,017 13,125 77.920 1,476.678 11.245 0.577 11.864 0.562 6.908 0.257 17.888 0.512 4.593 0.282 14.376 0.300 172,039 2,716 60,234 10,968 4,500 2,933 2,486 2,340 2,071 111.039 598.210 10.446 0.593 11.279 0.539 4.769 0.203 14.538 0.382 7.645 0.389 11.834 0.322 91,702 3,364 51,262 11,256 7,748 4,508 2,890 2,137 1,442 29.329 1,207.224 10.500 0.654 11.021 0.996 7.225 0.258 17.842 2.084 3.303 0.426 9.241 0.607 NAICS 44-45 - Retail Trade NAICS 72 - Accommodation and Food Services NAICS 48-49 - Modal Transportation and Warehousing NAICS 42 - Wholesale Trade NAICS 23 - Construction NAICS 31-33 - Manufacturing Table 10. Freight and service activity indicators by sector in selected cities.

44 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools more freight trips per establishment than any other sector, with this sector generating 17 to 18 daily trips per establishment in all but the two smallest cities. Wholesale Trade is the next largest sector in terms of per-establishment FTG, with 10 to 12 daily trips per establishment in all cities. The Retail Trade sector generates 8 to 12 daily trips per establishment, with this number being the smallest in NYC and Philadelphia. This is likely because the retail environment in these cities is dominated by smaller stores that do not have storage areas and require frequent trips. Generally, the sector with the fewest trips per establishment is the Construction sector, with three to six daily trips per establishment. In terms of freight trips generated per each employee in a sector, the trends are much murkier, with each city having a different pattern. Accommodation and Food Services tends to have the fewest freight trips 0.2 to 0.3 per employee, and the Construction and Manu- facturing sectors have slightly higher freight trips per employee, 0.3 to 0.6 trips per employee. Wholesale Trade, Retail Trade, and Transportation and Warehousing have the highest FTG per employee, generally having 0.5 to 1.0 trips per employee. B2C freight trips are a rapidly growing portion of freight trips worldwide, mainly due to the rise of online shopping. In the nine cities that were selected, there were 10 to 15 deliveries for every 100 people in the city due to B2C shipping, or about 0.02 to 0.03 freight trips per person-day. As online shopping continues to grow and branch into different types of goods, this number can be expected to grow significantly in the near future. These indicators are not consistent across all freight industry sectors in a city’s economy. 5.3.4 Freight Trip Generation Patterns in MSAs This section presents the results concerning FTG patterns at the level of MSAs. In general terms, the results are consistent with the results already discussed in the previous section though there are differences that arise from the differences in the economies at the urban and metropolitan areas. At the urban level, particularly in cities that are at the center of a small metro area, there is likely to be a high concentration of consumer-oriented businesses in SIS. In these cases, the consideration of suburban economies tends to diminish the relative weight of the consumer- oriented establishments. However, in large metro areas, there could be numerous concentra- tions of consumer-oriented establishments beyond the main urban core located near secondary economic poles. As a result, the percent of FTG may not change much (as in the cases of New York and Los Angeles). In cases like Washington, DC, a city with a pronounced service orienta- tion, the total contribution to FTG from consumer-oriented businesses at the MSA level could indeed increase. Table 11 shows the total FTG for selected MSAs in the United States. The small MSAs (under 2 million residents) generate between 29,000 (Kingsport MSA) and 122,000 (New Orleans MSA) trips daily from their respective 6,000 to 25,000 establishments. The large MSAs (over 10 million residents) generate as much as 2 million (New York MSA) freight trips per day. Across these MSAs, FISs generate 90% or more of the total B2B FTG. The results for FTG are consistent with what would be expected: smaller MSAs generate fewer total daily trips, and trips to FIS businesses represents the bulk of B2B FTG. B2C trips are also consistent with size of MSAs, as between 15% (Albany MSA) and 25% (Washington, DC, MSA) of total daily trips are B2C trips. It is interesting to note that the number of FIS B2B trips are significantly larger than B2C trips, and that the number of B2C trips surpasses the number of SIS B2B trips in all MSAs. 5.3.5 Breakdown of Freight Activity in MSAs by Industry Sector The breakdown of freight activity for FIS is shown in Table 12. Similar to the city results, when aggregated, the consumer-end establishments tend to generate more freight trips than heavy industry. In fact, the Retail Trade sector contributes to the most freight traffic in each MSA.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 45   N ew Y or k M SA Lo s A ng ele s M SA C hi ca go M SA H ou st on M SA W as hi ng to n, D C , M SA Se at tle M SA Po rt la nd M SA N ew O rle an s M SA A lb an y M SA To led o M SA K in gs po rt M SA Population (2016) Establishments Employment B2B FTG Establishments Employment B2B FTG Establishments Employment 2,848 B2B FTG B2C Deliveries B2C Trips 20,275 550 8,032 2,066 238 3,362 1,910 312 4,670 156 3,041 608 2,674 13,328 358 5,501 1,448 146 2,653 1,342 212 106 1,999 400 1,848 9,546 246 4,264 1,119 109 2,048 1,050 137 2,216 69 1,432 286 1,405 6,798 2,634 1,397 1,237 134 616 60 579 74 37 816 163 779 6,151 2,605 1,675 144 462 48 930 414 95 48 738 148 610 3,803 1,609 99 385 42 759 357 57 849 29 456 91 477 2,423 1,055 50 271 13 534 252 38 521 19 291 58 329 1,271 25 484 122 10 257 114 15 227 7 153 31 152 883 22 376 107 10 172 101 12 204 6 97 19 126 605 14 276 73 7 147 69 7 129 4 67 13 86 306 6 94 29 3 58 28 3 37 1 34 7 36 H ou se - ho ld s Total Trips (B2B+B2C) SI S O ve ra ll B 2B FI S Table 11. FTG for selected MSAs (in thousands). N A IC S Description N ew Y or k M SA Lo s A ng el es M SA C hi ca go M SA H ou st on M SA W as hi ng to n, D C , M SA Se at tle M SA Po rt la nd M SA N ew O rl ea ns M SA A lb an y M SA To le do M SA K in gs po rt M SA 44-45 Retail Trade B2C Internet Deliveries to Households 42 Wholesale Trade 72 Accommodation and Food Svcs. 48-49 Transp. and Warehousing 31-33 Manufacturing 23 Construction FTG B2B (FIS) (*) B2C Deliveries (*) B2C Trips (*) Total Trips (B2B+B2C) (*) FTG in Freight-Intensive Sectors Note: (*) denotes units in the thousands. 26.8% 26.1% 28.2% 31.9% 30.0% 33.8% 20.4% 18.7% 21.1% 16.2% 16.2% 19.4% 13.9% 14.0% 11.5% 8.4% 9.9% 8.2% 11.3% 10.7% 14.7% 15.8% 15.5% 13.3% 10.1% 10.7% 12.2% 4.0% 5.8% 5.0% 8.5% 11.7% 6.3% 5.9% 11.3% 9.4% 9.0% 8.1% 6.0% 17.8% 11.2% 10.8% 356.7 252.0 114.4 100.7 68.9 27.9 456.3 290.8 152.5 97.1 66.5 33.6 91.3 58.2 30.5 19.4 13.3 6.7 27.0% 24.2% 15.7% 9.8% 10.8% 6.1% 6.4% 1,909.6 3,041.3 608.3 2,673.8 22.7% 23.0% 19.5% 10.6% 9.1% 10.5% 4.7% 1,342.3 1,999.2 399.8 1,848.0 23.0% 21.4% 12.6% 9.3% 17.4% 10.6% 5.7% 1,050.5 2,431.9 286.4 1,405.4 25.5% 22.0% 14.5% 10.8% 10.1% 10.2% 6.9% 578.6 815.8 163.2 778.9 31.1% 26.3% 7.8% 14.3% 7.9% 3.8% 8.8% 414.4 738.1 147.6 609.7 476.5 329.1 152.4 126.2 85.8 36.1 Table 12. FTG breakdown for FIS in MSAs. In comparison with analogous results obtained for selected cities (Table 10), the top industry sector is Retail Trade in both analyses; however, the second position for MSAs is Wholesale Trade, whereas for cities, it is Food and Accommodation Services. This is a reflection of the fact that cities concentrate customer-oriented services, and MSAs have a dilution in the share of trips generated by these sectors. Another reflection of this effect is the higher shares of FTG for the Manufacturing and Construction sectors in MSAs in comparison with cities. The table also shows the proportion of B2C FTG with respect to total trips, which is quite significant across all MSAs. On average, 20% of all trips are due to internet deliveries to households. 5.3.6 Indicators of Freight Activity in American MSAs Table 13 lists the unit indicators of FTG for selected MSAs.

46 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools In most MSAs, FIS establishments generate between 6 to 12 daily freight trips per 100 people. The typical establishment generates between 8 and 11 freight trips on any given day, which translates into 0.4 to 0.6 freight trips per employee. Table 14 lists the freight and service activity indicators by sector in MSAs. These freight trips are not distributed evenly, but are often concen- trated in specific areas with a large amount of freight activity, such as commercial areas and shopping centers. Portland’s case is different from the rest of the cities; its FIS establishments generate 20 daily freight trips. This is consistent with the amount of freight activity in this region with the Port of Portland and major industrial hubs. On the lower end of the spectrum is Wash- ington, DC, the most pronounced service economy in the group, meaning that the number of FIS establishments is small relative to the population. It is useful to analyze the results for New York, Los Angeles, and Washington, DC. For these three areas, per capita FTG at the FIS is slightly higher than that for the principal city. This may be the result of high land values and zoning restrictions in the primary cities, which have pushed FIS establishments into the suburbs. Yet, in all three cases, the city itself has a higher number of freight trips per establishment and per employee than the MSA does as a whole. This may be caused by these FIS establishments in the principal city servicing more customers than those in the suburbs. 5.3.7 Geographical Distribution of Freight Trips Similar to the spatial distribution of the establishment density, FTG density tends to decrease with distance from the economic pole regardless of MSA. This pattern reflects the desire of businesses to locate as close to the market as profitable, taking into account the price of the land and the potential revenues from customers. As a result, the businesses that tend to locate closer to the economic pole are those that could command large revenues while requiring relatively small amounts of land. These conditions are frequently met by restaurants, retail stores, and N ew Y or k M SA Lo s A ng ele s M SA C hi ca go M SA H ou st on M SA W as hi ng to n, D C , M SA Se at tle M SA Po rt la nd M SA N ew O rle an s M SA A lb an y M SA To led o M SA K in gs po rt M SA Population 2016 (*) 6,151 3,803 2,423 1,271 605 306 Total Area (mi2) 6,701 5,963 6,659 2,170 2,254 4,491 Population Density 918 638 364 586 268 68 Establishments (*) 48 42 13 10 7 3 Employment (*) 930 759 534 257 147 58 FTG (*) 414 357 252 114 69 28 FTG/est-day 8.576 8.417 19.976 11.143 10.507 9.901 FTG/empl-day 0.446 0.470 0.472 0.446 0.469 0.485 B2C Deliveries (*) 738.1 456.3 290.8 152.5 66.5 33.6 B2C Trips (*) 147.6 91.3 58.2 30.5 13.3 6.7 B2C Deliv/person-day 0.120 0.120 0.120 0.120 0.110 0.110 B2C FTG/person-day 0.024 0.024 0.024 0.024 0.022 0.022 FTG (Total) (*) 562.0 448.0 310.2 144.9 82.2 34.7 FTG/person-day 238 20,275 6,950 2,917 3,362 1,910 8.012 0.568 3,041.3 608.3 0.150 0.030 2,517.9 0.124 13,328 4,836 2,756 146 2,653 1,342 9.200 0.506 1,999.2 399.8 0.150 0.030 1,742.2 0.131 9,546 6,628 1,440 109 2,048 1,050 9.662 0.513 1,431.9 286.4 0.150 0.030 1,336.9 0.140 6,798 8,967 758 60 1,397 579 9.657 0.414 815.8 163.2 0.120 0.024 741.8 0.109 0.091 0.118 0.128 0.114 883 3,112 284 10 172 101 9.813 0.585 97.1 19.4 0.110 0.022 120.2 0.136 0.136 0.113 B2B FTG in Freight-Intensive Sectors Internet Deliveries to Households Total Trips (B2B+B2C) Notes: (*) denotes units in the thousands. est = establishment; empl = employment; and Deliv = deliveries. Table 13. FTG indicators in selected MSAs.

Urban and Metropolitan Areas: Economies, Supply Chains, and Freight Activity 47   the like, which could be resupplied frequently, bypassing the need for storage areas. As the distance from the economic pole increases, the value of land decreases and the amount of land used by business increases, making it possible to receive larger shipment sizes. The com- bined effect of these factors is a reduction in the density of establishments, which lowers the density of FTG, and increases shipment sizes, which lowers the FTG. The results of the analyses conducted confirm these observations. Figure 17 shows the results for Los Angeles, Washington, DC, and Albany. In addition to showing that FTG density decreases with distance to the economic pole (as expected), the figure shows major differences in the peak FTG density. Across MSAs, the FTG density follows a similar trend as for establishment density (Figure 14), where larger MSAs have higher peak densities. The peak FTG densities in Los Angeles and Washington, DC, are signifi- cantly higher than in farther away areas in the same regions. This observation makes sense; for example, in Los Angeles, the urban core centers around zip code 90017, with the tallest buildings in the city. Farther away, land is developed with less density and there is greater varia- tion in land use as residential areas become more dominant. It is also important to consider the magnitude of these densities in comparison with other areas. For example, even 15 miles from the urban core of Los Angeles, the FTG density is higher than it is at the urban core of Albany. As shown, Los Angeles has a particularly high FTG density at its urban core. This area rep- resents most of downtown Los Angeles, which, unlike most cities, has more FIS establishments N ew Y or k M SA Lo s A ng ele s M SA C hi ca go M SA H ou st on M SA W as hi ng to n, D C , M SA Se at tle M SA Po rt la nd M SA N ew O rle an s M SA A lb an y M SA To led o M SA K in gs po rt M SA Population 2016 (*) 20,275 13,328 9,546 6,150 3,802 2,423 1,271 882 604 Total Area (mi2) 6,950 4,836 6,628 6,701 5,963 6,659 2,170 3,112 2,254 Population Density 2,917 2,756 1,440 918 638 364 586 283 268 FIS Establishments (*) 238 146 109 48 42 13 10 10 7 FIS Employment (*) 3,361 2,652 2,047 929 759 533 256 172 146 B2B FTG per day (*) 680 396 308 175 120 81 41 38 25 B2B FTG/establishment 8.862 10.226 10.837 10.991 10.763 9.926 11.673 12.246 B2B FTG/employment 0.662 0.603 0.572 0.591 0.596 0.614 0.651 0.661 B2B FTG per day (*) 396 340 168 107 44 62 43 17 10 8 B2B FTG/establishment 11.041 10.968 11.428 11.457 11.595 11.483 10.532 10.532 B2B FTG/employment 0.795 0.839 0.612 0.704 0.649 0.680 0.617 0.595 B2B FTG per day (*) 247 184 125 80 80 51 33 21 19 13 B2B FTG/establishment 5.013 6.210 6.049 5.527 5.375 6.628 7.799 8.170 B2B FTG/employment 0.315 0.277 0.281 0.282 0.286 0.260 0.475 0.398 B2B FTG per day (*) 271 158 232 75 44 45 33 18 5 5 B2B FTG/establishment 18.210 18.165 18.513 17.915 17.778 17.866 10.532 10.532 B2B FTG/employment 0.925 0.886 1.255 0.747 0.852 0.693 0.397 0.431 B2B FTG per day (*) 162 81 76 51 49 40 25 9 21 9 B2B FTG/establishment 3.474 3.972 3.489 3.736 3.564 4.283 8.737 9.207 B2B FTG/employment 0.397 0.332 0.401 6,798 8,967 758 60 1,396 189 10.372 0.582 11.336 0.638 6.681 0.266 18.212 0.686 5.586 0.257 11.071 0.572 11.198 0.700 6.480 0.271 18.048 0.818 4.205 0.311 0.349 0.373 0.310 1.028 0.670 305 4,491 68 3 57 12 10.469 0.663 3 10.532 0.820 5 8.261 0.384 2 10.532 0.506 4 9.320 0.622 NAICS 44-45 - Retail Trade NAICS 42 - Wholesale Trade NAICS 72 - Accommodation and Food Services NAICS 48-49 - Modal Transportation and Warehousing NAICS 23 - Construction Notes: (*) denotes units in the thousands. Table 14. Freight and service activity indicators by sector in MSAs.

48 Planning Freight-Efficient Land Uses: Methodology, Strategies, and Tools than service-intensive establishments. In this area, Wholesale Trade and Retail Trade make up a large portion of the total FTG. Another interesting finding is that the rates of decline in FTG density are different between MSAs, which reflects the patterns of land use. As shown, in Washington, DC, FTG density declines quite rapidly, reaching a minimum value at 18 miles from the economic pole. Albany, New York, a smaller metropolitan area, reaches a minimum value at a similar distance, but the rate of decline is much more gradual. 0 2,000 4,000 6,000 8,000 10,000 12,000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40FT G D en sit y (F TG /m i2 ) Distance from the Main Economic Pole (miles) Los Angeles MSA SIS FIS 0 1,000 2,000 3,000 4,000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40FT G D en sit y (F TG /m i2 ) FT G D en sit y (F TG /m i2 ) Distance from the Main Economic Pole (miles) Washington, DC, MSA SIS FIS 0 200 400 600 800 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 Distance from the Main Economic Pole (miles) Albany MSA SIS FIS Figure 17. Spatial distribution of FTG density from economic pole.