Data Sources

The present analysis includes an extensive data collection effort. The objectives of this data collection effort are to obtain the information required as inputs for the transporta-tion and economic models, and to verify the findings and interpretatransporta-tion of model results.

Data collection involves gathering information on a number of factors hypothesized to affect economic development. Those factors can be classified into two categories:

highway project-specific (project type, size, and associated construction costs), and lo-cation-specific factors, as illustrated in Table 4.1. The highway-project specific data, in-cluding project location, was obtained from the Indiana’s Statewide Long-Range Trans-portation Plan; information only on the expansion projects was collected (as discussed in the next section). Information on the accessibility and land use indices was compiled with the assistance of INDOT Transportation Planning Personnel and Bernardin, Lochmueller & Associates Inc. Data on the long-term economic benefits of the selected highway projects were generated with the use of the dynamic economic simulation model REMI (discussed in detail in Chapter 6). This information will be useful in a later part of the analysis to estimate long-term project-level economic multipliers.

Table 4.1 Data Highway Project-Specific Data Location/Accessibility/

Land Use indices Statewide Long-Term Economic Benefits

The data collation efforts are discussed next.

4.2.1. Indiana’s 25-Year Long-Range Plan Highway Expansion Projects

The Indiana Department of Transportation is in the process of updating its 25-year Long-Range Plan. This plan provides a vision for the future development of the state trans-portation system with an emphasis on the state’s highway network. A listing of state highway jurisdiction projects was developed for each of the state’s twelve MPO’s and each of the six INDOT districts. Projects were developed as one of the three categories:

“committed projects” (listed in the first three fiscal years with federal funding be obli-gated); “proposed capacity expansion projects” (not programmed with traditional and federal funding and require special funding to advance towards implementation); “place-holder type of projects” (proposed for implementation but delayed outside the study’s timeframe). Expansion projects include the following types: added travel lanes, new road construction, median construction, new interchange construction, new bridge con-struction and freeway upgrade. Non-expansion projects include: road rehabilitation (3R), road reconstruction (4R), and Transportation Systems Management (TSM) improve-ments. To determine the effectiveness of the plan in achieving economic development goals, the economic impacts of the projects included in the proposed 2003–2027 update of the Long-Range Plan (limited in scope to “capacity expansion” projects—

approximately 400 projects), were analyzed. This study was prepared by CSI and BLA (2004).

A representative sample of each highway improvement category (added travel lanes, new road construction, median construction, new interchange construction/modification) was selected. In total, 117 individual highway improvement projects were considered for analysis. The selected sample was further classified according to the area type, to ur-ban and rural projects, as shown in Table 4.2.

80

Table 4.2 Classification of Projects by Area and Highway Improvement Type

URBAN RURAL

Added Travel Lanes 27 31

Median Construction 20 4

New Road Construction 8 18

Interchange Construction 7 2

Total Number of Projects 62 55

Additional information provided for the LR Plan projects include: project ID, route, func-tional class, project length, start lanes, end lanes (after the improvement), project costs (in 2003 dollars⁵), district, MPO, and county. The summary statistics for the state high-way jurisdiction projects included in the LR Plan are provided in Section 4.3.

4.2.2. Highway Performing Monitoring System (HPMS) Database

Necessary inputs for the travel demand and user benefit analysis such as information on highway geometrics, traffic operation data, and design parameters were obtained from the 2005 Highway Performance Monitoring System (HPMS) of Indiana (FHWA, 2000).

In general, the HPMS is a national level highway information system that includes data on the extent, condition, performance, use, and operating characteristics of the Nation's highways. Likewise, the HPMS of Indiana is an inventory system that contains adminis-trative and extent of system information on all public roads in Indiana.

The 2004 Statewide Reference Post Book (INDOT, 2005) was used to locate the pro-jects on the state highway network and then, relate the data elements provided in HPMS to the location of these projects. Inventory information collected include: geometrics (e.g., lane, median and shoulder widths), traffic performance data and operation attrib-utes (e.g., average annual daily traffic (AADT) for base year and a 20-year forecast

5 The FHWA Highway Construction Price Index [online: http://www.fhwa.dot.gov/programadmin/

pricetrends.htm] was used to convert the 2003 dollar-values to 1996-dollar values, to be consis-tent with the units of the outputs (i.e., statewide long-term economic benefits of the selected highway projects) generated with the use of the regional economic model, REMI.

AADT, facility capacity, percent single-unit trucks and percent combination trucks).

Summary tables of the data reported in HPMS are provided in Appendix B (Tables B.1 and B.2).

4.2.3. Indiana’s Economic Profile and Transportation Statistics

Information on Indiana’s industries and business establishments by number of employ-ees was obtained by the United States Bureau of Economic Analysis (US BEA). The service industries are primarily located on urbanized areas, while the heavy manufactur-ing, construction and wholesale industries are located in non-metropolitan areas. Sepa-rate tables were compiled for industries located in metropolitan and non-metropolitan areas in Indiana, as shown in Table B.3 (Appendix B).

Industrial specialization (or clustering) in Indiana was measured by conducting a location quotient analysis at the county level. A location quotient (LQ) is taken as a rough indica-tor of a region’s competitiveness in that industry, measured in terms of employment. A LQ of one means an industry makes up the same share of a regional economy as it does of the national economy. The higher the LQ, the greater the competitive advan-tage a region appears to have (Glickman, 1977). LQ were calculated using the Location Quotient calculator—a tool produced by the United States Bureau of Labor Statistics (US BLS, 2005). The location quotients for selected industries in Indiana are provided at the county-level in Table B.4 (Appendix B).

Finally, the US Bureau of Transportation Statistics (US BTS) and the INDOT 1995 Sur-vey served as the main sources of information main information sources for automobile travel in Indiana, including the percent of business-related automobile trips by area type (urban, rural), and automobile occupancy by trip purpose (business and non-business related trips) and by area type (urban, rural). The share of business-related automobile trips in rural areas in Indiana ranges from 3 to 5 percent, while the corresponding share in urban areas ranges from 4 to 6 percent. The average automobile occupancy for busi-ness-related trips in rural areas is 1.2, while the average automobile occupancy for simi-lar trips in urban areas is 1.1.

82

4.2.4. Regional Accessibility in Indiana

The concept of personal accessibility relates to the ease with which residents of a par-ticular region can travel to population and employment centers, and other types of attrac-tions such as health facilities, educational instituattrac-tions, airports, and cultural events. Ac-cessibility is typically measured by calculating an index. Such index provides a quantita-tive measure of the attracquantita-tiveness of destinations of a particular kind, and how quickly travelers can get there. Mathematical calculations are typical used to generate these indices which take into account both travel time and the size of the attraction to which people travel. Generally, a region which is well-connected internally and externally to common travel destinations has a high degree of accessibility. In contrast, a region that has a less-well developed highway network will generally have a low degree of accessi-bility.

Information on accessibility in Indiana was compiled based on the methodology estab-lished as part of the Indiana Statewide Travel Demand Model. Given below are some key concepts involved in using the model to calculate accessibility indices to airports, employment and universities, as described in CSI and BLA (2003):

Traffic Analysis Zone (TAZ): The entire state is divided into traffic analysis zones (TAZs).

Each TAZ represents a portion of a county. Generally, the area within each TAZ is characterized by a relatively consistent type of land use. For example, urban and rural areas generally would not be included within the same TAZ.

Attractive Force (AF): For the purposes of each accessibility index, each TAZ was as-signed an “Attractive Force” (AF). This AF measures the amount of a particular attrac-tion contained in each zone. For each of the accessibility measures, the AF was defined as follows:

• Access to Employment: For each TAZ, AF was defined as the employment in that zone.

• Access to Airports: If a TAZ contains an airport with scheduled commercial pas-senger service, AF is the annual number of air paspas-senger enplanements. Oth-erwise, AF is equal to 0.

• Access to Universities: If a TAZ contains a college or university with an enroll-ment of at least 2,500 students, AF is the number of students enrolled. Other-wise, AF is equal to 0.

Network Travel Time (t): For each TAZ, the model calculates the average congested travel time (for 24 hours) between that TAZ and each of the other TAZs in the entire model. The travel time between the TAZs is adjusted by an impedance exponent to re-flect the fact that people do not respond to variations in travel time in a purely linear fashion. This impedance exponent is used to reflect people’s actual behavior, in that drivers’ willingness to travel to destinations drops.

Accessibility Index (AI): This index is determined by calculating the ratio of the attractive force to travel time between that TAZ and each other TAZ, and then calculating the sum of those ratios. If the value of this index is high for a TAZ, that TAZ has high accessibil-ity to that attraction. If the value of this index is low for a TAZ, that TAZ has low accessi-bility to that attraction. The mathematical formula is expressed as follows:

∑

=

j x

ij j

t

AI AF (4.1)

where, AI is the accessibility index; AF_j is the attractive force at zone j; t_ij is the network travel time between i and j, and x is the impedance exponent.

For the purposes of this statewide study, the accessibility indices to airports, employ-ment and universities are assigned at the county-level and take values from 1 (low de-gree of accessibility) to 5 (high dede-gree of accessibility). The accessibility indices to air-ports, employment and universities for Indiana counties are provided in Appendix B (Figures B.1, B.2 and B.3, respectively).

84