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2. DATABASE DEVELOPMENT
Pages 18-34

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From page 18... ... CROSS-SECTIONAL DATABASE Based on some preliminary analyses undertaken in Chapter 2 of CRA's TCRP report Building Transit Ridership An Exploration of Transit's Market Share, and the Public Policies that Influence It, we chose as the basis for the crosssectional dataset the 33 metropolitan areas with population of at least one million in 1980. This set of cities was selected for the following reasons: � Relevance to the study. Read the entire page →
From page 19... ... We therefore explored the most likely potential sources of relevant data that could help to build up the picture of how commuting mode shares and average vehicle occupancy vary across areas, and have been changing over the last 15 years. We examined each of these sources to appraise the nature and content of the data available, as well as the strengths and weaknesses of that source relative to possible analysis for the objectives of this project. Read the entire page →
From page 20... ... vey includes � Trip length the national level and for specific MSAs some � Trip timing journey-to work data FHWA Highway Annual time � Road mileage � Long time series of most data items Statistics series of key . Total vehicle � Few data items of relevance statistics re registrations � Little geographical detail below the state highway � Licensed drivers level provision and � VMT � No vehicle occupancy statistics use � Fuel consumption � Uncertainties about cross-state compar . Read the entire page →
From page 21... ... Transit trips are relatively rare in the NPTS databases, and for a variety of reasons successive NPTS surveys have been judged to underrepresent transit tripmaking.~ Since the survey focuses specifically on household travel patterns, its major strength is that it covers trips made for all purposes at a more detailed level than is available from (say) the Census journey-to-work data. Read the entire page →
From page 22... ... it cannot provide figures 1 1 =7 1 1 "7 representing flows of those living in the suburbs and working in central cities. FHWA's Highway Statistics A compilation of highway-related data is published annually by the Federal Highway Administration, primarily concerned with highway finance and physical infrastructure. Read the entire page →
From page 23... ... Data on total motor vehicle registrations, by state, are publicly available from FHWA, but the Polk data provide independent estimates of new registrations for each year and of the number of passenger vehicles in operation, by model year. Read the entire page →
From page 24... ... commute � Downtown parking costs � State gas tax � Average gasoline price � Freeway miles per square mile � Total freeway mileage � Total road mileage � HOV lane mileage Transit level of � Peak fare service � Peak vehicles � Vehicle miles � Vehicle hours � Vehicles/vehicle miles � Vehicles/vehicle hours � Vehicle miles/worker � Vehicle hours/worker � Peak vehicles per worker � Capital spending � Rail dummy � Heavy rail dummy Land Use � Land area � Population density � Population density � % zoned as residential � % single family detached houses � % housing built prewar � % single unit structure � % housing renter occupied � % high density apartments � % 5+ units in building � % housing built prewar � % housing built post-1970 � Housing density � Median year housing built � Median housing value Other � Annual precipitation � Average January temperature � % non-peak departure time to work � Coefficient of variation of departure . time 24 Read the entire page →
From page 25... ... %1 vehicle households � % 2 vehicle households � % 1 + vehicle households � % 2+ vehicle households Private vehicle � TTI congestion index level of service (1982) � Average gasoline price � Freeway miles per square mile � Total freeway mileage � Total road mileage � HOV lane mileage Transit level of � Peak fare service . Read the entire page →
From page 26... ... . It was data like these that we hoped to use as "dependent variables" in time series analysis, to explore how changes over time in the relative costs and service characteristics of different competing modes influences trip volumes and mode shares. Read the entire page →
From page 27... ... ~ ~ ~ ~ ~ ~ ~ ~ I Metro Area ~ ~ ~ ~ ~ ~ ~ ~ - : Data~ailabili:ty -I ~ ~ ~ New York Annual peak period cordon counts 1971 -1994; decennial occupancy survey Washington, DC Peak period cordon counts with occupancy for 1968, 1974-1981, 1983, 1985, 1987, 1990, and 1993 : Philadelphia Cordon counts in 1960, 1980, 1985, 199O, 1995 Chicago MPO did counts in 1981 -1985 only San Francisco Bay Bridge only; city did one cordon count in 1984 : Seattle One cordon count in 1977 Minneapolis One cordon count in 1984 : Houston Only one cordon count in mid-80s : Boston Very infrequent; highway counts only San Diego Average weekday traffic only; occupancy survey every couple of years : Portland Average daily traffic only; no occupancy data : Phoenix Average weekday traffic only; no occupancy data Kansas City Average daily traffic only; limited occupancy data : Los Angeles No cordon count data Atlanta No cordon count data . Read the entire page →
From page 28... ... Bridge/tunnel tolls � Quality adjusted new car prices � MTA Transit fares With only one city allowing a possible time series analysis, the task of collecting data for explanatory variables was made somewhat simpler. Nevertheless, the nature of time-series data makes it difficult to assemble a complete dataset with many variables. Read the entire page →
From page 29... ... Geographic cIefinition issues As we have described, the data for the dependent variables in the cross-sectional models developed in this study are taken from the Census of Population and Housing. These data represent mode shares and occupancies for each of thirty three metropolitan areas, computed from the Census data. Read the entire page →
From page 30... ... Louis Baltimore Pittsburgh Tampa Kansas City Sacramento Portland Columbus New Orleans Dallas Atlanta Denver Cincinnati Indianapolis Providence Source: Federal Highway Administration. Clearly this would seem to suggest an inherent incompatibility in comparisons of 1980 and 1990 mode shares and occupancies derived from these data. Read the entire page →
From page 31... ... Other mocie share calculation issues Although the Census Journey-to-Work data have the strong advantages of national scope and very large sample sizes - large enough to provide usable statistics for quite small geographic areas - the restricted amount of space on the Census form imposes some important limitations on how such essentially complex and diverse behavior as commuting can be characterized in a uniform manner. Most importantly, the questionnaire only allows respondents to record the "primary" mode of their work trip, forcing all commute trips into a "single mode" classification and thereby reducing the precision of mode share calculations. Read the entire page →
From page 32... ... While the Census data provide the level of detail needed, they are only collected every ten years, and the earlier discussion highlights the problems with obtaining a consistent time series of mode share or occupancy measures for individual metropolitan areas. While the relationships we are examining are sufficiently complex that no amount of data will allow us to quantify them exactly, adequate time series data would be a very important component in adding to our understanding of them. Read the entire page →
From page 33... ... Typically, this problem is addressed through two-stage regression techniques employing instrumental variables variables that are not a function of the dependent variable, but are a good predictor of the problem endogenous variable. In this type of analysis, though, finding suitable instrumental vanables can be highly problematic (for example, what is a good predictor of frequency that is unrelated to demanded. Read the entire page →
From page 34... ... Contacts with local and state agencies associated with fourteen of the largest cities revealed that New York City was the only city with adequate time series data from which to compute annual mode shares. Hu, P.S., and Young, J., Nationwide Personal Transportation Survey: 1990 NPTS Databook (two volumes) Read the entire page →

From page 18...

... CROSS-SECTIONAL DATABASE Based on some preliminary analyses undertaken in Chapter 2 of CRA's TCRP report Building Transit Ridership An Exploration of Transit's Market Share, and the Public Policies that Influence It, we chose as the basis for the crosssectional dataset the 33 metropolitan areas with population of at least one million in 1980. This set of cities was selected for the following reasons: � Relevance to the study.

2. DATABASE DEVELOPMENT Pages 18-34

2. DATABASE DEVELOPMENT
Pages 18-34