II. FACTORS AFFECTING TRAVEL DEMAND

Transcription

1 II. FACTORS AFFECTING TRAVEL DEMAND Various economic, social, and land development factors affect growth in travel and changes in travel patterns. Several key factors are used in the Berks County Travel Demand model to forecast future travel behaviors. Population and household growth are derived from planning commission forecasts adopted in 2003 that are consistent with the Berks Vision 2020 Comprehensive Plan. Employment growth is based on countywide projections prepared by the PA State Department of Labor and Industry. The amount and distribution of growth has major impacts on the location, timing, and amount of traffic congestion. All household and employment data are maintained by Traffic Analysis Zone (TAZ) geography. This level of detail includes 673 TAZ s that aggregate to municipal boundaries. Typically, more dense areas have a larger number of small zones while rural areas have fewer but larger zones. There are typically more trip generating and attracting activities in urban areas than there are in rural settings. Map #2 (Page 13) depicts our zonal layout. Projections The BCPC developed population, household, and employment forecasts to fit the 673 zones. Initial countywide population projections were compiled by the Pennsylvania State Data Center in August, Those projections were adjusted upward after the release of the 2000 Census information, and were used in the April, 2003 Berks Vision 2020 Comprehensive Plan. Using Census 2000 distribution of population by block, a Shift-Share Allocation methodology was used to disaggregate countywide future populations back to the TAZ-level. The same Shift-Share Allocation method was used to create employment forecasts. Staff obtained local employer information for the second-quarter, 2000, which includes April 1 (Census Day) for consistency. Employment by zone by 18 major SIC categories was established for a 2000 baseline. Countywide projections by SIC category were provided by the PA State Dept. of Labor and Industry for The 2000 zonal share of countywide employment, by SIC, was computed and applied to future years. Change over time of employment by SIC category was factored into the projections, accounting for Berks County s change in employment from agricultural and manufacturing to a more robust mix that includes service and retail industries. Population Change Berks County has been growing steadily since it was incorporated in In more recent decades, the county has seen increasing growth from the Philadelphia metropolitan area. It is notable that between 1990 and 2000, Berks growth rate was similar to those of our neighboring counties to the east that make-up the Philadelphia region. This can be seen in Table 1. Of the counties directly adjacent to Berks, only Schuylkill to the north has been steadily losing population. 11

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5 Table 1 REGIONAL POPULATION: 1980, 1990, 2000 Change Berks and Nearby Counties Number Percent Berks 312, , ,638 37, % Bucks 479, , ,635 56, % Chester 316, , ,501 57, % Dauphin 232, , ,798 13, % Delaware 555, , ,864 3, % Lancaster 362, , ,658 47, % Lebanon 108, , ,327 6, % Lehigh 272, , ,090 20, % Montgomery 643, , ,097 71, % Northampton 225, , ,066 19, % Philadelphia 1,688,210 1,585,577 1,517,550-68, % Schuylkill 160, , ,336-2, % Pennsylvania 11,863,895 11,881,643 12,281, , % SOURCES: U.S. Census Bureau, 1980 Census, 1990 Census, 2000 Census Berks County is expected to continue past growth trends into the future, though at a lower rate of change. Overall, we can still expect roughly 20 percent growth between 2000 and 2030, as shown in Table 2. The county s Berks Vision 2020 Comprehensive Plan contains policies that promote agricultural preservation and conservation of rural areas, while directing new growth to areas where public infrastructure exists or can become readily available. Table 2 PROJECTED COUNTY GROWTH, % Change, Berks County 373, , , % SOURCES: U.S. Census Bureau, 2000 Census; Forecasts by Berks County Planning Commission 15

6 Population Density Population density is an important consideration in transportation planning. As more people move into and within the county, the density of residential and employment opportunities changes both in magnitude and distribution. The city of Reading and most of the older boroughs contain the highest densities of persons and jobs, while the rural areas contain, by default, more land and, therefore, lower densities. Unfortunately, this last assumption has changed in recent decades as more people and the retail & service industries that follow them are relocating to the rural areas. Transportation provisions are affected in different ways by density of people and jobs. Mass transit services operate more efficiently in very dense, urban areas. Areas that were formerly rural are now developing at suburban densities, but are neither dense enough nor close enough to the transit system to warrant system expansion. Many rural roads, bridges, and intersections are not constructed to standards adequate enough to handle increased demands created by new development. Through the implementation of the Comprehensive Plan, future densities should increase in appropriate areas, thus reducing the need for costly infrastructure improvements in rural parts of the county and promoting increased transit use. Household Growth Household composition and growth is another key variable used in predicting trip generation. Tables 3 and 4 show household growth from 1990 to 2000 for the tenhighest areas on a numeric and percentage basis. From 1990 to 2000, the areas with the largest growth in the number of Table 3 MUNICIPAL NUMERIC HOUSEHOLD GROWTH, TEN-HIGHEST: Change Municipality # H.H. P.P.H. # H.H. P.P.H. Number Percent Berks County 127, , , % Spring township 7, , , % Exeter township 6, , , % Maidencreek township 1, , , % Muhlenberg township 5, , % Amity township 2, , % Lower Heidelberg township , % South Heidelberg township 1, , % Cumru township 5, , % Robeson township 2, , % Tilden township , % SOURCES: U.S. Census Bureau, 1990 Census, 2000 Census 16

7 households were mostly the areas with the largest existing number of households. However, on a percentage basis many formerly rural townships are starting to experience tremendous growth pressures. Table 4 MUNICIPAL PERCENTAGE HOUSEHOLD GROWTH TEN-HIGHEST: Change Municipality # H.H. P.P.H. # H.H. P.P.H. Number Percent Berks County 127, , , % Maidencreek township 1, , , % Lower Heidelberg township , % Tilden township , % Rockland township , % Amity township 2, , % South Heidelberg township 1, , % Leesport borough % Windsor township % Washington township , % Pike township % SOURCES: U.S. Census Bureau, 1990 Census, 2000 Census Beyond 2000, future household growth was derived for each TAZ and municipality from the population and household size projections. Those projections show a continuing trend in positive growth, though at a slower rate county-wide over the next 30 years. Employment and Employers Berks County s workforce and their employers have seen significant changes over the last 20 years that are forecast to continue. Like cities and counties surrounding us, the Reading/Berks job market was dominated by manufacturing. In recent years, however, we ve lost a significant number of those jobs. The closure of some of our largest employers, as well as the loss of many smaller firms throughout the county and the growth of service and professional fields, have significantly changed the makeup of our workforce. Demand on the transportation network varies by employer location and type. Large retail establishments with many part-time employees tend to generate more employment and shopping trips throughout the day and evening, while manufacturing establishments tend to have shift workers traveling at set times of the day with few outside customers. Office workers typically work during the day and travel during the morning and afternoon peak periods with customers also calling during daylight hours. Table 5 shows Berks County s largest employers and their type of business. As shown, our major employers are varied in both type and location throughout the county. 17

8 Table 5 TOP 25 EMPLOYERS IN BERKS COUNTY, 2007 Employer Business Type Number of Full-time Equivalent Employees Reading Hospital & Medical Center Hospital 5,900 East Penn Manufacturing Co., Inc. Batteries - Mfg. 5,160 County of Berks Government - County 2,617 Reading School District School 2,291 Wal-Mart Stores, Inc. Department Store 2,037 Boscov's Department Stores Department Store 2,000 Carpenter Technology Corporation Metals - Specialty - Mfg. 1,976 Commonwealth of Pennsylvania Government - State 1,700 St. Joseph Medical Center Hospital/Health Care 1,549 Sovereign Bank Bank 1,504 Redner's Warehouse Markets Supermarket Chain 1,421 Penske Truck Leasing, L.P. Truck Rentals & Leasing 1,330 Reading Area Community College Schools - Universities & Colleges 1,200 United States Government Government - Federal 1,192 Boyertown Area School District School 1,157 Wilson School District School 1,145 Berks County Intermediate Unit Educational Services 1,137 Kutztown University Schools - Universities & Colleges 1,082 Associated Wholesalers, Inc. Grocery Distribution Retail 1,017 Supermarkets Met-Ed/FirstEnergy Utility - Electric Service 1,000 Ashley Furniture Manufacturing - Furniture 900 Giant Supermarket Chain 875 Transcontinental Direct Advertising and Direct Mail 820 WorleyParsons Industrial Services 728 City of Reading Government - City 721 NOTES: Information based on total number of full-time equivalent employees in Berks County locations. All information provided by Human Resourses Department personnel from each location. SOURCE: Reading Eagle, Largest Employers in 2007, Page C-1, January 13,

9 Attempts to project future employment are difficult, particularly at the local level. Businesses open and close, move to different locations, are purchased by other firms, and are affected by changes in the local and national economic climates. In November 2003, population projections to 2030 were adopted by the MPO for use in the travel demand model. These projections were prepared using the same Shift-Share Allocation methodology used for the population forecasts. Second-quarter 2000 (contains Census Day, April 1, for consistency) employment information was obtained from the state s Department of Labor and Industry for all employers in Berks. The county s roughly 8,400 employers and their respective 181,000 employees were coded by TAZ and employment type. County-wide employment projections by type were issued by the state for Based on Shift-Share, those 2008 state-issued projections were disaggregated to zones. Following that, straight-line trends from 2000 to 2008 were continued to Approximately 235,000 employees are allocated throughout the county in 2030, a projected 29.8 percent increase. County to County Worker Flows As shown in Table 6 (next page), Census 2000 reported that Berks County had 177,831 workers over 16 years old. Of that number, 140,819 (79.2%) stayed in Berks for their jobs while 37,012 (20.8%) commuted outside of the county. By far, the most number of commuters were travelling to Montgomery County to the southeast, followed by Lehigh County, Chester County, Lancaster County, and Lebanon County. Of those coming into Berks from outside, Schuylkill County generated the most commuters, followed by Montgomery County, Lancaster County, Lehigh county, and Lebanon County. Table 6 details the Top 20 commuting destinations for Berks workers, as well as those coming to Berks from surrounding counties. Income Relationships between income level, vehicle ownership, number and types of trips, and transit usage have been shown nationwide. In general terms, higher income levels translate into higher vehicle ownership rates, more trips per household, and less transit usage. Here in Berks County, trends have shown that areas with high housing growth are synonymous with higher income levels and corresponding levels of vehicle ownership. What that translates into is more vehicles on roads formerly (and, in many cases, still) rural. Unfortunately, housing and vehicular growth in new suburbs and rural areas tend to be far-removed from employment centers and transit routes, even further promoting additional cars on rural roads and lower transit use. 19

10 Table 6 COUNTY TO COUNTY WORKER FLOWS (TOP 20 COUNTIES): 2000 Residence Workplace Count Residence Workplace Count Berks Co. PA Berks Co. PA 140,819 Berks Co. PA Berks Co. PA 140,819 Montgomery Co. PA 12,727 Schuylkill Co. PA 5,790 Lehigh Co. PA 6,538 Montgomery Co. PA 4,231 Chester Co. PA 5,596 Lancaster Co. PA 4,074 Lancaster Co. PA 3,870 Lehigh Co. PA 3,266 Lebanon Co. PA 2,053 Lebanon Co. PA 2,799 Philadelphia Co. PA 702 Chester Co. PA 1,916 Bucks Co. PA 675 Northampton Co. PA 605 Dauphin Co. PA 651 Bucks Co. PA 410 Northampton Co. PA 629 Carbon Co. PA 266 Schuylkill Co. PA 619 Philadelphia Co. PA 243 Delaware Co. PA 505 York Co. PA 240 Cumberland Co. PA 238 Delaware Co. PA 187 New Castle Co. DE 157 Dauphin Co. PA 175 York Co. PA 152 Luzerne Co. PA 169 New York Co. NY 76 Northumberland Co. PA 146 Luzerne Co. PA 65 Cumberland Co. PA 84 Somerset Co. NJ 62 Lycoming Co. PA 64 Essex Co. NJ 53 Camden Co. NJ 56 Carbon Co. PA 50 Monroe Co. PA 55 DATA SOURCE: U.S. Census Bureau, 2000 Census of Population, Summary File 3, County-to-County Worker Flow Files; March, 2003 Major Traffic Generators While the major employers shown in Table 5 are certainly major generators of employment trips, they can also generate significant types of other trips. For example, one of our largest single employers The Reading Hospital generates large numbers of patient and visitor trips to its West Reading campus. Department stores have large employment at each location but also create a very large number of retail shopping trips. Additionally, there are other large trip generators that may not necessarily be large employers. Shopping malls and strip centers, office complexes, large retailers, warehouses, and universities generate trips of varying types and amounts. Examples include: Cabela s outdoor retail in Tilden Township; Berkshire Mall in Wyomissing Borough; Spring Ridge housing and office complex in Spring Township; Kutztown University in Kutztown Borough and Maxatawny Township; St. Joseph s Medical Center 20

11 in Bern Township; and various agencies of local, state and federal government located largely in downtown Reading but also throughout the county. Travel to Work Between 2003 and 2006, the number of drivers licensed in Berks County increased from 259,573 to 271,357, a gain of almost 12,000 drivers in a 3-year time span. In addition, the number of registered vehicles also grew from 354,067 to 369,860, a gain of about 15,800 vehicles. In that same period of time, however, the county s non-local roadway network has grown by only sixteen lane miles with the completion of the Warren Street Extension & U.S. 222 South Lancaster Pike projects. Clearly, the growth of the roadway network is not keeping pace with the addition of new drivers and vehicles to that network. According to U.S. Census figures, between 1990 and 2000, there were an additional 13,757 workers commuting to their places of employment. Unfortunately, the number and rate of workers either carpooling or using public transportation in their journey to work declined while, conversely, the number of persons driving alone increased. Combined with population and non-residential growth in suburban and rural areas, the average commute time increased from 18.7 minutes in 1990 to 22.3 minutes in All of these factors lead to one conclusion: More traffic and congestion on the roadways. Table 7 Workers and Means of Transportation To Work: 1990 and 2000 Total Workers Commute Work at Home Car, Truck or Van (Alone) Means of Transportation (Commuters) Carpool Public Transit Walk or Bike , ,949 4, % 11.8% 2.2% 5.6% 0.5% , ,706 5, % 10.3% 1.7% 4.0% 0.6% Other SOURCES: U.S. Census Bureau, 1990 and 2000 Census' Table 7 shows that the number of persons working at home increased in the time period. This trend may continue to grow as technology advances and more employers explore telecommuting. It is the only factor reviewed that, along with carpooling and transit use, can effectively remove vehicles from the roadway network thereby decreasing congestion. Unfortunately, the growth in working at home has not kept pace with the decrease in carpooling and transit use. Trip Generation As discussed in the Major Traffic Generators section, the amount of traffic created is highly dependent on land use. The Institute of Transportation Engineers publishes a 21

12 Trip Generation Manual that details trip generation rates based on type of land use and size. The rates are derived from actual measurements of traffic in driveways of land uses. The Manual shows that convenience markets with gas pumps generate the most trips of any land use, followed by markets without pumps. Other retail uses that tend to demand high visibility from roadways also generate the most traffic. Fast food and sitdown restaurants, banks, and supermarkets all depend on high visibility to garner passby trips. Also note that single family detached dwellings generate more trips than multiunit structures per dwelling unit, and government offices generate the most trips of any office type land use. Trip Distribution While trip generation is dependent on the various types of land uses, their location and densities affect trip distribution. Although some redevelopment has occurred in the City of Reading, most new office and retail growth is occurring in suburban locations. This is not the same pattern that was seen in prior generations. There was a time when the downtown areas were the prime employment centers and trips were made from suburb to city in the morning, and city to suburb in the evening. Recent census data shows that, while trips between the suburbs and city are still predominant, there are also an increasing number of trips between suburban locations. Housing and nonresidential growth has occurred in the inner suburbs: Muhlenberg, Spring, Cumru, Exeter townships and Wyomissing Borough. That growth has brought traffic along with it. With increasing residential and non-residential growth continuing to spread into rural areas, however, the area s rural roads are also handling more trips. 22