Effects of Land Use Characteristics on Residence and Employment Location and Travel Behavior of Urban Adult Workers

Transcription

1 Effects of Land Use Characteristics on Residence and Employment Location and Travel Behavior of Urban Adult Workers João de Abreu e Silva, Thomas F. Golob, and Konstadinos G. Goulias The relationships between socioeconomic and demographic characteristics, land use characteristics around the residence and work locations, and a variety of travel behavior indicators are examined by using a structural equations model. This simultaneous equations system allows one to model the effects of land use characteristics on travel behavior while controlling for self-selection bias: certain types of persons choose to live and work in areas that suit their lifestyles and resources. In the model, travel behavior choices are multidimensional; total time away from home, trips and trip distances by three types of modes, car ownership, and possession of a transit pass are included. Land use is captured in geographic information system based measures of land use and transport supply variables centered on both home and work locations. These measures are reduced to eight land use factors. The analysis provides strong evidence in favor of using land use and urban form designs and planning both around residential neighborhoods and workplace areas. Results provide quantitative evidence of the extent to which workers living in denser, central, compact, and mixed zones make more intense use of transit and nonmotorized modes and tend to have lower car ownership levels. Workers in areas well served by freeways tend to make more intense use of their cars, although this does not inhibit use of transit. The results show that land use measures differ in their ability to explain different travel demands even when controlling for socioeconomic and demographic effects. The study of the interaction between land use and travel behavior is becoming increasingly important as governments consider using land use as a trigger to change travel behavior. This is particularly important when one considers travel behavior as the derived demand from the need to participate in activities and the necessity to reach locations at which different activities may take place, and thus it is possible to envision changing the relative location of activities to change travel (1). During the 1980s, policy makers became increasingly aware of the need to find policy tools with which to curb congestion and the use of single-occupant vehicles. The proposition to use land use patterns as a policy tool to change mobility was fueled by the Newman and Kenworthy study (2). One of their main propositions that urban density is important in explaining consumption of energy J. de Abreu e Silva, Center of Urban and Regional Studies, IST, Technical University of Lisbon, Avenida Rovisco Pais, Lisbon, Portugal. T. F. Golob, Institute of Transportation Studies, University of California, Irvine, 522 Social Science Tower, Irvine, CA K. G. Goulias, Department of Geography, University of California at Santa Barbara, 3611 Ellison Hall, Santa Barbara, CA Transportation Research Record: Journal of the Transportation Research Board, No. 1977, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp in transport in large cities was first subjected to scrutiny with mixed results. Several conceptual models were developed to explain the relationships between land use and travel behavior. Van Wee proposed one (1), theoretically based on utility, in which travel behavior influences and is influenced by land use patterns (location of activities) and travel impedances (spatial separation), and it is influenced by the needs and desires of people. These three groups of variables are mutually influenced over time. Current situations in land use and travel reflect some kind of dynamic equilibrium, because new changes in the system occur before the long-term equilibrium becomes a fact (1). Stead et al. proposed a three-way relationship among mobility patterns, land use, and socioeconomic characteristics, in which the former two are mutually influenced and influence travel behavior (3). Naess argued that the urban structure creates conditions for future actions or behavior that span a long period (4). It appears clear that urban structure is not the only condition for changing travel behavior, but it is a contributory or partial cause of travel behavior among many. For example, residents in a central area are closer to more opportunities, which means that some trips may be made by using nonmotorized modes or transit (4). But, because their time spent on travel is less than that of residents in outlying areas, it is possible that they make more trips more often. However, this increase could hardly be expected to eliminate the difference in kilometers traveled by the residents in the outer fringes (4). In Europe, the Sesame research project, studying 57 urban agglomerations in France, Germany, Great Britain, the Netherlands, Switzerland, and Spain, pointed to the existence of several important relationships at an aggregate level among land use patterns, travel behavior, and transit supply (5). (The project reports are available at These relationships include the following: An increasing density is strongly correlated with an increase in the supply of public transport both in vehicle kilometers and the presence of rail-based systems. An increasing concentration of jobs and inhabitants means a higher density of public transport stops. Density was correlated (at a 95% confidence level) with the share of public transport (positively) and car trips only drivers (negatively). The job concentration in the center was negatively correlated with car use and positively with bicycle use. Other European studies also concluded that land use patterns influence travel behavior. Stead et al., reviewing three studies, one at 121

2 122 Transportation Research Record 1977 the individual level and the others at the ward and settlement levels, found evidence that both land use and socioeconomic variables explain variation in travel patterns (3). But socioeconomic variables often explain more of the variation in mobility patterns than land use variables. Other reviewed studies (3) showed that land use characteristics might explain up to 40% of the variation in car ownership. Dargay and Hanly, by using data from the English National Travel Survey ( and ), found that the total distance traveled by car at a person level was significantly influenced by only the fourth quartile of density for the data from the first period (6). For the second period, the total traveled distances by car and all modes decrease consistently as density increases. The urban area size was significant for predicting distance traveled in both surveys (6). In a subsequent paper (7), by using the same data set and a multinomial logit model, Dargay and Hanly found that car share is lower in denser areas (fourth quartile of density) and transit shares are higher. In other areas, there is little difference. They also found that households in denser areas are less likely to own a car, and if they do, they are less likely to own more than one car (7). Naess, studying 29 residential areas in Copenhagen, Denmark, concluded that residential location influences travel behavior, even when the different socioeconomic characteristics and attitudes among the interviewed groups are considered (4). In the United States, the debate around the relation between land use patterns and travel behavior is conflicting and unclear. Handy, reviewing several studies, concluded that the relation between land use and transportation is not yet fully understood (8). She also pointed out diffused uncertainty about the degree of intensity of these relations and the endogeneity of location and travel. For example, people living in denser areas do so because they prefer to use the car less. Bagley and Mokhtarian, by using a structural equation modeling (SEM) approach, found that when attitudinal and lifestyle variables are introduced, the land use variables cease to have an important role in explaining mobility patterns (9). This supports the argument that the relationship between land use and mobility patterns is mainly due to correlations between the former and attitudinal variables. Their study, however, included just a few travel behavior indicators (distances traveled by mode and commuting distance) and a small sample, and it neglected completely the possible impact of employment location land use characteristics. In a similar way, Schwanen and Mokhtarian, analyzing the influence of mismatched residents in urban and suburban neighborhoods, concluded that urbanites living in suburban areas might use the car more often than they would like (10). On the other hand, suburbanites living in urban areas, although they may use transit, still consider the car as a viable option. The goal for this paper is to disentangle a select group of correlations among travel behavior indicators, land use depicted by a rich variety of measures, and social and demographic characteristics, while controlling for self-selection biases in residence and employment locations, by using a database from a European capital, Lisbon, Portugal. Comparisons with other studies mentioned here can be made only in terms of the findings because the context and data used are different. GEOGRAPHICAL OVERVIEW AND SURVEY DESCRIPTION The Lisbon metropolitan area (LMA) is located in the Atlantic Façade of the Iberian Peninsula. It contains 17 municipalities, including the capital of Portugal, Lisbon. The Tagus River estuary, crossing through the region, is a considerable barrier splitting this region in two. In 1991, the LMA had 2.5 million inhabitants, representing 26% of Portugal s population. This area is also the richest region of Portugal and the most dynamic economically. Between 1991 and 2001, the regional population increased by about 140,000 inhabitants, and simultaneously the central core area Lisbon and the inner suburbs lost population. During the last decade, several changes occurred in LMA. Two of the most important were substantial increases in income and transport infrastructure provision. A mobility survey was implemented between 1993 and 1994 (unpublished data), which used both telephone and personal interviews. In every interview the socioeconomic characteristics of every household member were collected. A person within the household was randomly chosen and asked to collect her or his mobility patterns. The universe comprised all the residents in the LMA who were more than 10 years old. A total of 30,680 interviews were made, characterizing 101,337 persons (unpublished data). A sample from the survey database was selected to build the model presented here, including surveys made during a weekday and containing only families in which the interviewed person was an adult worker (age 18 or older and employed). CONCEPTUAL MODEL AND DATA The developed model intends to address the relation between land use and socioeconomic characteristics, residential location, and travel behavior. It was built by using the models proposed by van Wee (1) and Stead et al. (3), as well as a variety of critiques of the reviewed empirical analyses in the previous section. Unlike other studies, this model also considers travel indicators that span both long-term and short(er)-term decisions. Figure 1 illustrates the conceptual model and the links among the variables tested as hypotheses. Land use characteristics at residence and employment locations are influenced by the socioeconomic characteristics of the respondents and were assumed to be endogenous. Thus the study can implicitly account for the preferences and needs of specific persons (and their households) to live and work at specific locations with specific land use patterns. Land use and socioeconomic characteristics also influence commuting distance. This model considers feedback effects in which travel behavior also influences commuting distance and land use characteristics (e.g., shapes the location of activities). Travel behavior in this figure includes car ownership, transit pass ownership, amount of time out of home, modes selected, travel distance, and number of trips. Socioeconomic variables describe both the interviewed person gender, age, and working schedule variables and his or her household household total income (using professional occupation variables), number of persons, families with only adults and teenagers (older than 10 and younger than 18), average age of the family, average age of the adults, and families with only one or with two members. These variables were used in the SEM model as exogenous variables. Several land use variables were defined at the civil parish level zoning, and a reduction technique was used to assemble this information into a few key factors. The land use variables attempt to capture the multidimensional characteristics of urban space by incorporating variables focusing on urban form, intensity, and diversity

3 de Abreu e Silva, Golob, and Goulias 123 FIGURE 1 of occupation as well as levels of transport infrastructure provision and accessibility. These variables were urban density; distance from the center of each zone to the central business district (CBD) of Lisbon; mix of jobs, students, and inhabitants; an entropy index considering four types of urban land uses (residential, public facilities, industry, and offices); a compactness index; the number of kilometers of trunk roads per person; the percentage of people within a radius of 400 m of all the bus stops; the percentage occupied by urban land uses; the percentage of people within a radius of 400 m of all the train, metro, and ferry stations; and the percentage of people within a radius of 1,000 m of each freeway junction. The entropy index used here measures the degree of diversity in terms of land uses. It was first used in this context by Cervero (11). entropy = P P j ln j ln J j Socioeconomic Characteristics Residence and Workplace Land Use Characteristics Long-Term Decisions Short-Term Decisions Commuting Distance ( ) ( ) Travel Behavior Car Ownership Pass Ownership Number of Trips Km Traveled Trip Scheduling Conceptual model. where P j is the proportion of urban soil in the jth category for each parish. This index varies between 1 and 0, 1 meaning a perfect balance of the land use categories considered (11). Accessibility variables were also built as the ratios between nonmotorized accessibility and car accessibility, and public transport accessibility and car accessibility. This accessibility index is a perceived one with all the data necessary to its construction taken from the survey. All these variables were built describing either the characteristics of residence (labeled home) or the employment zones (labeled work) of each individual. They were combined to build eight factors describing the land use characteristics at the residence and employment areas. With the exception of one factor, there is a clear distinction between the factors describing the land use characteristics of residence and workplace zones. The factors and the defining variables together with their loadings are presented in Table 1. The first factor presents high loadings in several variables describing the land use characteristics and the public transport supply at residence areas, typical of the premodernist city. This is called residence in traditional urban areas. At the employment zones, these same variables were used in the second and third factors. The second factor has high loadings in density, mix, and the accessibility indexes and heavy public transport supply in the employment zones. This is called working in traditional urban areas. The third factor presents some complementarities with the factor working in traditional urban areas, the compactness index, and the distance from the CBD have high loadings. It is called working in compact and central urban areas. This differs from the second land use factor by not having important loadings on variables that are usually associated with traditional urban areas, namely, density, mix, and higher levels of accessibility on nonmotorized modes. The fourth factor mainly describes the supply of trunk roads and major streets in both residence and employment areas, both with strong negative loadings. This is called road supply and is the only factor for which there is no clear distinction between the variables describing the land use characteristics of residence and workplace zones. The last four factors were named after the single variable that had a loading higher than 0.5 in absolute values. They were named, respectively, freeway supply in the residence area, residence in a specialized area, working in a specialized area, and freeway supply in the work area. The commuting distance variable was transformed by using a logarithm (all distances used were direct distances). The travel behavior variables are Number of cars and motorcycles in the household; Transit pass ownership; Number of nonmotorized, transit, or car trips in a weekday; Number of kilometers traveled by each mode; and Time spent between the starting hour of the first trip and ending hour of the last trip, corresponding to the height of the time space prism in activity-based approaches (12). These variables and the land use factors entered the model as endogenous variables. All are integrated by considering a hierarchy of decisions, from long-term decisions (commuting distance) to medium-term (car and pass ownership) and short-term ones (kilometers traveled by each mode, number of trips, and daily time budget). The sample used contains 7,849 individuals from the most urbanized municipalities of LMA. Of these, 53% are men, reflecting the greater presence of men in the labor force. The average age of the interviewed is The average age of household members is 34.2 (38.9 for the total sample), and their average household size is 3.44 people (3.34 for the total sample), with an average of two working members (1.6 for the total sample) per household. The average number of trips in a working day per mode is 1.14 for cars, 0.98 for transit, and 0.39 for nonmotorized modes (respectively, 0.72, 0.66, and 0.49, for the total sample). The average commuting

4 124 Transportation Research Record 1977 TABLE 1 First Four Land Use Factors and Their Defining Factor Loadings Land Use Factor Most Important Land Use Variables Loadings Residence in traditional urban areas Density home Distance CBD home % urban land home % people 400 m from heavy transit home % people 400 m from bus home Access transit/access car home Access nonmot./access car home Mix home Compactness home Working in traditional urban areas Access transit/access car work Access nonmot./access car work % people 400 m from H. transit work Density work Mix work Working in compact and central urban areas Distance CBD work % urban land work % people 400 m from bus work Compactness work Road supply Km road/person work Km road/person home Freeway supply in the residence area % people 1,000 m from freeway node home Residence in a specialized area Entropy index home Working in a specialized area Entropy index work Freeway supply in the work area % people 1,000 m from freeway node work distance is 4.9 km. The average number of cars per household is 1.12 (1.04 for the total sample), and 47% (34% for the total sample) of the interviewed have a transit pass. SEM APPROACH SEM can handle indirect and multiple relationships and also allows the study of reverse relationships. For instance, the same variable could be a predictor or an outcome in different modeled relationships (8). SEM is a specific type of simultaneous equation system in which the variables are divided into two sets of variables: endogenous and exogenous (13). A structural equation system with observed variables only (no measurement submodels) can be expressed as y = α+ By+ Γx+ ζ where y = vector of p endogenous variables, x = vector of q exogenous variables, α=vector of p regression intercepts, ζ=vector of p disturbances with variance covariance matrix Ψ, Β=( p by p) matrix containing the coefficients for the equations relating the endogenous variables, and Γ=( p by q) matrix containing the regression coefficients for the equations relating endogenous and exogenous variables. It can be shown that the model-replicated combined variance covariance matrix of the observed (p) endogenous and (q) exogenous variables, arranged so that the endogenous variables are first, is given by the partitioned (p + q by p + q) matrix. 1 1 ( ) ( + ) ( ) 1 I B ΓΦΓ Ψ ( θ) = I B ( I B) ΓΦ 1 ΦΓ ( I B) Φ Estimation of SEM models is performed by using the covariance analysis method method of moments (13). To be estimated are the elements of the Β, Γ, and Ψ matrices that are specified as free parameters. The objective function is to minimize the differences between the sample variance covariance matrix, S, and the model-replicated matrix Σ(θ). The methods used for model estimation are normal theory maximum likelihood (ML), generalized least squares (GLS), and weighted least squares (WLS) (14). WLS can be used in forms as asymptotically distribution-free weighted least squares (ADF-WLS) (14), allowing the treatment of limited and discrete endogenous variables. WLS minimizes the following fit function (15): F( θ)= ( ) 1 s W s ( ) where s =vector of the elements in the lower half, including the diagonal of the covariance matrix S, =vector of corresponding elements of Σ(θ), reproduced from the model parameters θ, and W 1 = positive definite weight matrix of order u by u, where u = (P + q)(p + q + 1)/2. These weights are estimates of the fourth-order moments (the variances of the covariances). The direct effects in the SEM model are given by the parameters of the Β and Γ matrices and can be interpreted

5 de Abreu e Silva, Golob, and Goulias 125 in the same way as regression coefficients (16). It can be shown that for an identified SEM, the total effects of the exogenous variables on the endogenous variables (the coefficients of the so-called reducedform equations) are given by (I Β) 1 Γ, and the total effects of the endogenous variables on one another are given by (I Β) 1 I. The indirect effects are given by the differences between the total and the direct effects. Examination of all the different effects allows one to identify policy actions better suited as direct impacts on behavior and policy actions more effective as triggers for other desirable changes on travel behavior. This type of examination is also ideal for identifying policies that are self-defeating because of contrary direct and indirect effects. It should be noted that interpreting a model by using the direct effects alone could provide misleading conclusions. SEM applications in travel behavior analysis are available elsewhere (13, 14). The SEM model here uses 29 variables (18 endogenous and 11 exogenous). MODEL RESULTS The model fit parameters indicate a good fit. The ratio between the degrees of freedom and chi-squared is Standard Bayesian criteria the Akaike information criterion (AIC) and the consistent AIC indicate that the model is superior to either the independence or the saturated model. The results are presented in the following way. First, both Beta and Gamma matrices are presented, showing the direct effects between all the variables. Then, the total effects of the land use factors on the mobility variables are discussed. Finally, the indirect effects of the exogenous variables on the mobility variables are shown. This structure allows the direct relations between all the variables to be highlighted (Beta and Gamma contain the same information that could be read from a causal diagram). The Gamma matrix shows how socioeconomic characteristics affect directly the land use patterns and travel behavior. The total effects of land use patterns on mobility variables are the key results showing the intensity and type of influence that land use patterns have on travel behavior. The indirect effects of the exogenous variables on the mobility variables allow examining of the effects channeled via the land use factors on the mobility variables (Table 2). The direct effects among endogenous variables show that variables describing long-term decisions influence short-term decisions. The exception to this consequentiality is transit pass ownership, which is influenced by the number of nonmotorized trips, commuting distance, and two land use factors, which are influenced by household car ownership. This could be because of two possibilities: an indication that a transit pass is a shorter-term decision as originally considered (e.g., a month-to-month decision) and the possible effect of lifestyle and preferences captured by the other travel behavior variables. Moreover, there is a simultaneous relation between car ownership and commuting distance. On one hand, people working far from home will have more cars, and people living and working in more suburban environments will have more cars. At the same time, however, people who would like to have more cars will be living and working in the suburbs, thus having a commuting distance shorter than the ones that live in the suburbs but work in the center (Table 3). The direct effects of socioeconomic variables are in accordance with the commonly accepted effects of socioeconomic variables on mobility. Older people tend to have lower car ownership levels and to walk and use transit more often. Women tend to use transit more often, and so they have a higher probability of owning a pass. Men tend to have longer commuting distances than women and also to spend more time outside the home. The number of employees in the household positively affects car ownership. The kilometers traveled by the three modes were omitted from Table 3 because there are no direct effects from the socioeconomic exogenous variables. In looking at the direct effects of exogenous variables, it can be seen that age affects directly only working in compact and central urban area. This may be because the service sector, more concentrated in central areas, replaced manufacturing as the main employment sector, meaning that younger workers will work in more central zones. Residence in traditional urban areas and freeway supply in the residence area are indirectly affected by age via the number of cars in the household (Table 2), the first negatively and the second positively. Thus older people will tend to live in traditional urban areas. Gender affects negatively four land use factors, meaning that working women are more prone to live or work in zones with those characteristics. Households with a higher income tend not to reside in zones with lower road supply and with a higher level of land use mix. Having a fixed working schedule affects all except two land use factors. People with a free working schedule are more prone to work in traditional and central zones and also in zones better served by freeways. Working in a specialized area is negatively affected by working schedule. Liberal professionals or entrepreneurs usually do not have a fixed working schedule, so they are more prone to work in areas with a higher density (intensity) of offices. The number of workers in the household affects negatively this land use factor. Larger households tend to work or live in zones better served by roads and freeways. Families with one or two members tend to live or work in more traditional zones but also in zones better served by freeways (Table 4). The land use patterns of the zones of residence have negative effects on the commuting distance. On the contrary, the land use patterns of the zones of employment have positive effects on the commuting distance. These contrary effects can be justified by the following example. Working in traditional urban areas and working in compact and central urban area have positive effects on commuting distance, largely because a great number of people who work in Lisbon live in other municipalities, thus increasing the distance between home and work. They also live in suburban zones (lower scores on the more traditional land uses at residence), so the result is an increase in the commuting distance. Residence in traditional urban areas and residence in a specialized area have negative effects on pass ownership, whereas freeway supply in the residence area has no effect. Regarding household car ownership, only the last two land use factors have positive total effects. The effect freeway supply in the residence area on car ownership is due to indirect effects, which act through the commuting distance (which itself has a positive effect on household car ownership). In contrast, car ownership has a positive direct effect on freeway supply in the residence area and a negative direct effect on residence in traditional urban areas (Table 2). Families who prefer to and can have more cars will tend to locate in zones well served by freeways. The first four factors have a negative effect on the number of car trips. Higher densities and central and traditional urban designs inhibit the use of cars. The strong effect that working in traditional urban areas and working in compact and central urban area have on the number of trips and on the kilometers traveled by transit is also explained by the large number of people who work in Lisbon and live

6 TABLE 2 Direct Effects Between Endogenous Variables: Beta Matrix Endogenous Variables Time Working Spent in Between Residence Working Compact Freeway Freeway First No. of in in and Supply Residence Working Supply and No. of No. of Trips: No. of Log Traditional Traditional Central in the in a in a in the Last Trips: Trips: Non- Cars in Transit Commuting Urban Urban Urban Road Residence Specialized Specialized Work Endogenous Variable Trips Car Transit motorized Household Pass Distance Areas Areas Area Supply Area Area Area Area Time spent between first and last trips (4.08) (4.64) Km traveled: car 0.81 (207.38) Km traveled: transit (31.11) (3.89) Km traveled: non motorized (26.26) (2.66) (3.16) No. of trips: car (8.39) ( 17.49) ( 23.96) (7.51) ( 26.84) (2.65) (5.70) (2.96) No. of trips: transit ( 10.83) ( 3.83) (17.01) ( 5.43) (12.55) (7.38) (3.99) 2.27 (2.23) No. of trips: non motorized ( 11.54) ( 24.32) ( 2.41) ( 4.92) ( 7.51) 5.96 ( 3.06) No. of car in household ( 28.78) (11.54) (2.82) ( 2.70) (3.42) Transit pass ( 18.43) (9.95) (16.65) (31.57) (15.50) (4.38) Log commuting distance ( 3.49) ( 38.20) (18.65) ( 12.65) ( 8.15) (17.02) (9.86) Residence in 0.07 traditional ( 5.57) urban areas Freeway supply in 0.03 the residence area (3.15) NOTE: Values in parentheses are t-statistics.

7 TABLE 3 Direct Effects of Exogenous Variables: Gamma Matrix Exogenous Variables Gender: Fam. with Work No. of Household Average Single-Person Two-Person Adult Average Endogenous Variable Age Male Teens Income Schedule Workers Size Age Household Household Age Time spent between first and last trips (7.25) ( 3.81) (2.89) (7.46) (3.40) No. of trips by car ( 3.95) (4.72) ( 3.27) No. of trips by transit (3.22) ( 9.84) ( 4.75) No. of trips, nonmotorized (6.45) (12.34) No. of cars in household ( 10.28) (4.10) (6.16) (5.98) (4.47) (13.12) ( 6.46) ( 6.04) ( 9.19) Transit pass ( 22.06) (2.14) ( 35.63) ( 7.63) ( 3.51) (10.51) (11.26) ( 2.38) ( 8.28) Log commuting distance (22.67) ( 3.41) (4.29) (2.25) ( 2.74) Residence in traditional urban areas (22.48) (4.87) Working in traditional urban areas (2.69) (21.60) Working in compact and central urban area ( 5.50) ( 4.36) (11.98) (14.54) (9.07) Road supply ( 3.54) (6.80) ( 7.53) ( 10.28) Freeway supply in the residence area ( 4.23) (9.24) (12.68) (6.49) Residence in a specialized area (8.45) (7.51) (4.59) Working in a specialized area ( 4.07) ( 3.91) ( 4.60) Freeway supply in the work area ( 3.87) (7.44) (8.63) (7.99) NOTE: Values in parentheses are t-statistics.

8 TABLE 4 Total Effects of Land Use Land Use Factors Residence in Working in Traditional Traditional Working in Compact Freeway Supply in Residence in Working in Freeway Supply in Mobility Variable Urban Areas Urban Areas and Central Urban Area Road Supply Residence Area Specialized Area Specialized Area Work Area Time spent between first and last trips ( 5.22) (8.06) (4.50) ( 3.10) ( 3.85) ( 4.39) (5.90) (4.78) Km traveled by car ( 3.33) ( 15.94) ( 6.61) ( 3.43) (2.56) ( 1.42) ( 1.23) (4.74) Km traveled by transit (1.07) (11.55) (15.93) (2.60) ( 0.39) (0.64) (6.88) (4.45) Km traveled, nonmotorized (8.15) ( 8.04) ( 4.00) (11.76) ( 4.59) ( 2.08) ( 8.27) (1.33) No. of trips by car ( 3.33) ( 15.92) ( 6.62) ( 3.43) (2.56) ( 1.42) ( 1.22) (4.76) No. of trips by transit (10.19) (15.85) (19.53) (11.42) (4.17) (2.89) (6.78) (6.26) No. of trips, nonmotorized (13.51) ( 12.42) ( 4.00) (13.27) ( 4.18) ( 1.45) ( 9.48) ( 8.91) No. of cars in household ( 6.03) ( 7.88) ( 8.50) ( 6.96) ( 7.18) ( 6.92) (4.06) (5.10) Transit pass ( 15.68) (20.97) (32.47) (11.30) (0.15) ( 2.72) (10.34) (8.61) Log commuting distance ( 39.74) (19.48) (2.22) ( 12.76) ( 8.12) ( 10.64) (16.79) (9.86) NOTE: Values in parentheses are t-statistics.

9 de Abreu e Silva, Golob, and Goulias 129 outside. Only the first and fourth factors have positive effects on the number of nonmotorized trips. The last four factors have positive effects on the number of trips by transit, but the effects due to working in a specialized area are stronger. Regarding the number of trips by car, it is possible to see that land use mix, at both residence and work areas, has a negative effect on the number of trips made by car. The total effects of land use variables on the time spent between the first and last trips are of the same direction as those found for commuting distance, this stresses that longer commute distances mean a longer time outside the home. These effects are in great part channeled via the commuting distance. Only working in compact and central urban area has no direct effects on commuting distance (Table 2), and through commuting distance a great part of all the effects on the other mobility variables are channeled, namely, on pass and car ownership. Table 5 shows indirect effects from the exogenous variables on the mobility variables. Only eight of the indirect effects are higher than 0.1. A part of them are channeled through land use factors. The indirect effects of age on the kilometers traveled in nonmotorized modes are due to effects on the factor working in compact and central urban area channeled via the number of nonmotorized trips. The effect of age in this land use factor is negative, but because working in compact and central urban area affects negatively the number of nonmotorized trips, it increases the indirect effects of age on the kilometers traveled in nonmotorized modes. The indirect effects of gender in three mobility variables the kilometers traveled by car and transit and the number of car trips are partially channeled through the last one of these variables. The four land use factors that are influenced by gender are working in compact and central urban area, road supply, freeway supply in the residence area, and freeway supply in the work area. All of them influence either directly ( working in compact and central urban area and road supply ) or indirectly the number of car trips. The indirect effects of income on the kilometers and trips traveled by car and the household car ownership are channeled through road supply and working in a specialized area. Both factors are negatively affected by income, and they influence the commuting distance, passes, and car ownership. Both increase the effect of income on car ownership levels. Road supply also decreases the effects of income on the kilometers and trips traveled by car (it affects positively the number of car trips). On the contrary, working in a specialized area increases the effect of income in these two variables by positively influencing the number of transit trips. The indirect effects of the average age of the household on the number of transit trips are partly due to working in traditional urban areas, positively influencing it. Working in traditional urban areas influences pass ownership and transit and nonmotorized trips, thus counterbalancing the direct effect of the average age of the household on the number of transit trips, which is negative. characteristics showing contrary impacts on travel behavior. To do so, eight land use factors were included to allow a more comprehensive analysis of the diversity of urban characteristics. The model shows that living in more traditionally urbanized zones favors a more intense use of transit and nonmotorized modes and lower car ownership. On the contrary, living in areas well served by highways contributes to having more cars and using them more often. Working in traditional or central areas contributes to higher levels of transit use and to lower levels of car ownership, although not necessarily leading to a more intense use of nonmotorized modes. Working in areas well served by freeways contributes to a more intense car use, although this does not necessarily contribute to lower use of transit. The overall model shows that land use patterns affect travel behavior in a significant way. This effect, however, is not simple; it is different for residence and workplace locations, and sometimes is not even direct. A great part of the influence of land use on travel behavior is mediated by the commuting distance that tends to be shorter for the residents of more traditionally urbanized zones. The commuting distance directly affects both the number of cars in the household and pass ownership. These two effects and the effect on the number of nonmotorized trips are the most important effects of commuting distance on the mobility variables. Residence location measured as the commuting distance influences travel behavior mainly by conditioning the transport modes available for each household. In contrast, land use variables are influenced by the socioeconomic and demographic characteristics of individuals and households (which, as is known from other studies, also influence attitudes and predispositions). Thus there are some effects of self-selection or some limitations by socioeconomic constraints in living or working in zones with certain land use characteristics. Nevertheless, the model is able to capture direct and indirect impacts of longterm variables on the shorter-term activity and travel behavior indicators. But besides the socioeconomic variables, some more commonly accepted travel behavior variables also influence land use variables in the present case, household car ownership. If one considers the travel behavior variables as, among other things, the visible outcomes of lifestyle preferences, these results are partially in accordance with the findings of Bagley and Mokhtarian (9), who found that attitudinal variables and lifestyles were more important than land use variables when one neglects land use around the workplace or any other spatial anchors around which daily activities are organized. The analysis presented here provides strong evidence in favor of using land use and urban form designs and planning to achieve a more sustainable travel behavior not only around residential neighborhoods as most U.S. studies of travel behavior appear to indicate but also around workplaces. CONCLUSIONS This paper used SEM to analyze the complex relationships between land use and travel behavior, controlling for a variety of other factors, including social and demographic characteristics. That SEM allows the simultaneous analysis and estimation of several layers of endogenous and simultaneous relations between variables is probably one of its most important features. This property was exploited here to unravel the differential effects of residence and workplace ACKNOWLEDGMENTS Part of this work was carried out when the first author was a visiting scholar at the University of California at Santa Barbara and the University of California at Irvine, sponsored by a FLAD/MCOTA grant from the Portuguese American Foundation for Development. The authors thank the Metro of Lisbon, EP, and TIS.pt for the survey used in the study and Patricia Mokhtarian and three anonymous referees for their insightful comments.

10 TABLE 5 Indirect Effects of Exogenous Variables on Endogenous Mobility Variables Exogenous Variables Gender: Fam. with Work No. of Household Single-Person Two-Person Adult Average Endogenous Mobility Variable Age Male Teens Income Schedule Workers Size Average Age Household Household Age Time spent between first and last trips ( 2.12) (2.00) ( 1.93) ( 1.90) ( 5.23) ( 4.14) (4.18) (4.35) ( 2.33) ( 3.34) ( 2.21) Km traveled by car ( 5.64) (29.74) ( 4.72) (30.99) (9.09) (2.83) ( 8.23) ( 4.64) ( 0.11) (0.43) (4.48) Km traveled by transit (1.40) ( 18.35) (0.70) ( 19.19) ( 6.39) ( 5.06) (3.90) (0.70) (9.43) (0.89) ( 5.56) Km traveled, nonmotorized (7.34) ( 14.80) (0.95) ( 9.94) ( 4.00) ( 4.03) (9.90) ( 7.30) (4.17) (4.98) (4.76) No. of trips by car ( 5.64) (30.61) ( 1.29) (31.32) (2.73) (5.77) ( 8.24) ( 4.64) ( 0.11) (0.43) (4.48) No. of trips by transit ( 8.56) ( 13.84) (1.72) ( 23.41) ( 6.37) ( 4.78) (3.87) (9.50) (11.19) (1.61) ( 5.94) No. of trips, nonmotorized (6.58) ( 19.18) (1.59) ( 9.62) ( 5.23) ( 3.72) ( 3.94) ( 7.22) (4.46) (4.54) (4.57) No. of cars in household (9.44) (19.88) ( 3.66) (21.51) (5.81) (2.41) ( 7.35) ( 11.79) ( 5.11) (0.37) (8.40) Transit pass ( 11.33) (9.14) ( 0.62) (3.32) (9.05) (0.06) ( 9.85) (12.33) (6.33) (4.50) ( 2.07) Log commuting distance (2.18) ( 1.63) ( 4.15) ( 2.44) ( 4.48) ( 3.20) (1.56) (1.15) ( 7.12) ( 0.44) (2.17) NOTE: Values in parentheses are t-statistics.

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