disaggregated perceptions and preferences in transportation planning

Transpn Res. Vol 9. pp. 279-295. Prrgamon Press 1975. Printed in Great Blitam

DISAGGREGATED PERCEPTIONS AND PREFERENCES IN TRANSPORTATION PLANNING

GREGORY C. NICOL.AIDIS and RICARLW D~BSON:

Transportation and Urban Analysis Department. General Motors Research Laboratories, Warren, MI 48090, U.S.A.

(Receised 30 May 1974)

Abstract-This paper uses preferences and similarity judgments with respect to system characteristics of an integrated innovative urban transport system to better understand the demand for public transportation. This transport system concept embraces dual mode transit, personal rapid transit. and people mover vehicles. A major goal of this study is to identify if insights could be uncovered by segmenting a sample of respondents into homogeneous perceptual groups. Three psychometric models are applied to a set of judgments from a set of respondents. The results from these models are used to cluster individua!s into homogeneous population segments on the basis of common pattern of preferences. The patterns of preferences for the various groups are then linked to their socio-economic characteristics. The analysis provides some useful insights as to the socio-economic profiles of groups preferring automatic vehicle control. basic transport service and personal luxury service. These results make it possible to better understand the benefits derived by these user groups from different system alternatives.

IAVTRODUCTION

There is an emerging body of literature which attempts to describe and explain urban travel behavior via social science research techniques and theory. The activity of theorists and investigators who contribute to this literature is generally referred to as disaggregate, behavioral modelling. Some principal disciplines which form the guidelines for the development of disaggregate, behavioral models are economics, geography, psychology and sociology. One major objective of the research described herein is to contribute to and extend the base of knowledge emanating from disaggregate, behavioral modelling efforts. In order to facilitate the evaluation of the research reported herein in its proper perspective. a brief review of the literature describing such efforts is provided in the Introduction.

Disaggregate, behavioral models attempt to explain and describe the decisions of individuals with respect to transportation-related alternatives. Within the urban transportation planning context. these alternatives come from the four central processes of trip generation, trip distribution. mode split. and traffic assignment (Stopher and Lisco, 1970). Most of the disaggregate model applications attempt to treat mode choice problems after the early example of Warner (1962). He used primarily a logistic function to estimate the probabilities with which individuals select one alternative from a binary mode choice decision problem. For example, in one problem he tried to predict the probability of individuals selecting an automobile or mass transit for the work trip. His predictor variables included system characteristics. such as relative time and cost. as well as socio-economic and demographic characteristics, such as income and sex.

Since Warner’s pioneering research. a number of

subsequent investigations have been initiated to both improve the methodology for studying and increase the

+Nou, employed with the U.S. Department of Transportation. Federal Highway Administration. Washington. D.C.. U.S.A.

understanding of the determinants of transport mode choices in an urban environment. For example. Lisco (1967) applied multiple probit analysis to describe the work trip mode choices from a 1964 Chicago Area Transportation Study survey in terms of system characteristics and socio-economic respondent characteristics. Logit and probit analysis fit S-shaped curves to the choices of individuals, but the probit procedure is predicated on the cumulative normal distribution. Lave (1969) compared the two methods on Cook County Highway Department data f19S7). which was used previously by Warner. He found the two techniques of model building to yield similar findings by comparing his reSults with those of Warner. In addition, Quarmby (1967) used discriminant analysis to develop a mode choice model for work trips. He used relative measures of time and cost (i.e. differences, ratios. logs) and came to the conclusion that the “differences” formulation was the best. All four investigators, Warner, Lisco, Quarmby and Lave, found travel choices to be sensitive to relative time and cost factors. but Lave showed that sizable manipula- tions of these factors did not result in large diversions of auto users to public transit. Therefore, it is likely that other factors are playing a meaningful role as the determinants of mode choices.

The emphasis placed on the individual and his characteristics as determinants of urban mode choices by the disaggregate, behavioral modelling approach creates an interest in the perceptions and preferences of individuals as determinants of travel behavior. While most of the psychological scaling research in the transportation research literature is not directed specifically towards finding the determinants of mode choices. it is relevant to that issue. For example. Paine. Nash, Hille and Brunner (3969) reported survey results from Balti- more and Philadelphia with respect to 33 public and private transportation system attributes. Using factor analysis. they were able to identify interrelationships among the 33 attributes which were of importance to

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280 G. C. UICOLUDIS and R. DOBSOV

various socio-economic population segments. Golob. Dobson and Sheth (1973) extended the results of Paine et al. by collecting separate importance ratings for public and private transport system attributes. Goiob et u!. found the factor structure of perceived importance for attributes to be different between two systems, and they were able to find particular attributes. such as station location. which were viewed as differentially important for the two systems. Even more recently, Dobson and Kehoe (1974) investigated the relationship between viewpoint towards transport attributes and satisfaction with three proposed transport systems. They found seven distinct viewpoints in their sample of 243 respondents. Furthermore. it was shown (I) that the viewpoints accounted for the variance of satisfaction judgments with the three proposed systems. and (2) that groups with different viewpoints were also distinct with respect to socio-economic characteristics known to be important from previous research (Dobson. Golob and Gustafson, 1974: Golob. Dobson and Sheth. 1973).

For the literature reported above. the psychological scaling research and the disaggregate behavioral modelling are related with respect to their concern for the individual, but they are disjoint with respect to the types of information collected and the manner in which data is processed. Golob and Dobson ( 1974) proposed a general schema for integrating economic utility models. which are in the spirit of disaggregate, behavioral models, and psychometric theory. One advantage of the schema is that it makes it easier to identify how research and theory conducted in both areas jointly contribute to uncovering the determinants of decision making by individuals with respect to transportation-related alternatives. Another advantage of the schema is that it points to the need for research which uses objective system characteristics along with subjective evaluations from individuals to define the determinants of mode choices.

Nicolaidis (1974) reported recently some research which illustrates how the integration can be achieved successfully. Using a model proposed by Carroll and Chang (1970), he defined a comfort index for a variety of transport modes in a college town: each individual had a separate comfort evaluation for each mode. These comfort evaluations along with perceived time and cost data were found to be correlated with the mode choices of individuals in the community. Costantino. Golob and

Stopher (1974) also attempted to estimate mode choices from the subjective evaluations of individuals. In addition, they found that the determinants of mode choice for shopping trips were demonstrably different than the determinants of mode choice for work trips.

The present research takes issue with much of the above research in that it does not advocate total disaggregation. but it rather attempts to demonstrate the value of disaggregating a sample into groups of individuals which have homogeneous patterns of preferences. Two alternative classes of procedures are compared for mapping the preferences of individuals into a multidimensional space, and the individuals are then grouped according to their position in the space or the way they weight the dimensions of the space. The homogeneous

groups, which are formed by this clustering procedure. are cross-classified with alternative socio-economic groupings (I) to identify socioeconomic variables which covary significantly with preference groups and (2) to help link socio-economic respondent characteristics to the well-defined preference patterns for the groups.

THE DATA SOURCE

This report is one in a series designed to study an innovative urban transportation concept, Metro Guide- way (Canty. 1972); the investigation described below is part of the Metro Guideway Attitudinal Demand Study WADS). which was undertaken by the Transportation and Urban .4nalysis Department of General Motors Research Laboratories. The total data collection effort for MADS. which includes pre-tests. mail panel surveys. home-interview and leave-behind questionnaires, is documented by Dobson (1973). An analysis of the mail panel data, which constitutes the basis for the attributes studied herein, is presented by Golob, Dobson and Sheth (1973). Other analyses of selected aspects of the data base include previous or forthcoming reports by Costantino.

Golob and Stopher (1974). Costantino. Dobson and Canty (1974), Dobson and Kehoe (1974) and Murawski and Ventura (1974). These reports collectively address substantive issues, such as perceived social costs and benefits of Metro Guideway. while they compare and contrast alternative methodologies for deriving answers which are germane to the issues.

The sample of respondents who contributed data for the analyses reported here is a group of 506 respondents who participated in the total MADS data collection effort. Those 206 respondents constitute a probability sample selected by a cluster design from the households in the Detroit Urbanized Area. as defined by the 1970 U.S. Census. The total sample consisted of 100 cluster points with approximately five households selected at each cluster point.

This report reveals the preference structure of homogeneous population segments for the attributes of innovative urban transport systems. In order to appreciate the range of systems to which these preference structures

are applicable. brief descriptions of the three Metro Guideway modes are given here. Dual mode transit vehicles are small, bus-like. and demand-responsive: this mode uses regular streets to access to an automated guideway where it goes under remote control. People mover vehicles are larger, bus-like vehicles. They can travel only on an automated guideway. and they operate on a regular schedule. Personal rapid transit vehicles are captive to an automated guideway. and. like a people mover, the potential user must go to a transit station in order to ride it. It provides point-to-point service to all stations on a guideway network for a party of no more than four passengers. Metro Guideway is a transportation systems concept which integrates service provided by all three modes.

The data to be analyzed in this report are mode- independent judgments about attributes for the Metro Guideway system. Respondents were given a verbal description and a graphic display of the systems concept.

Disaggregated perceptions and preferences in WansportaGor. pianning 281

but no information about specific modes were given to the respondents prior to their making the mode-independent preference judgments of the attributes. Figure I shows the response sheet used to collect the data. Also, the 12 specific attributes which are considered in this study are listed there as well. These attributes range from “short travel times” and “low fares” to “control of temperature in the vehicle” and “a comfortable ride in a quiet vehicle.”

MODELWNG EFFORTS AND RESCLTS

1. Internal preference analysis (a) Analytical methods. As mentioned in the

previous section, ail 506 respondents expressed preference judgments for the 12 system attributes. This section describes the internal preference analysis using solely preference information. The analysis attempts to develop a space for the 12 attributes and the person weights or scores for all 506 subjects. This is accomplished through a singular decomposition (Eckart and Young. 1936) of the standardized preference matrix X. The initial preference matrix Y (506 x 12) was row

centered and standardized as discussed by Dobson. Golob and Gustafson (1974). The decomposition is represented

by

X = P.lQ’. (1)

The matrices P and Q contain characteristic vectors of XX’ and X’X. respectively, and the square roots of the r largest characteristic roots of X’X are ordered as consecutive diagonal elements of .I. which has zeros elsewhere. It follows that P. Q and .I are of rank r. which is hopefully and usually much less than the rank of X for an acceptable least-squares approximation of X P represents factor scores which have to be applied to the matrix (nQ’) to recover a close approximation to X. the original standardized preference judgments.

The (.lQ’) matrix represents the coordinates of the 12 attributes in an r-dimensional space (attribute space). The P matrix contains direction numbers between each respondent’s vector and the dimensions of the attribute space. By plotting the attributes and depicting the

Thinking about when I would uee Public Transportation for longer trips and where I might go-This Feature !e This Im- portant to Me:

Having P short time waiting for a vehicle . , . .

Having short travel times

Having low farea . , .

Having P comfortable ride tn a quiet vehicle. . .

Having a driver instead of a completely automatic ryrtem . . . . .

Having my own private *action in the vehicle .

Bslng able to get where I want to go on time . .

Being safe from harm by others and from vehicle rccldcnts . . . . .

Having room for rtrollers or wheel chairs . . .

Being able to get to many pl~en in the Detroit arsa uling the guideway

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Having control of temperr- hire in the vehicle . .

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Fig. 1. A questionnaire format for collecting preference judgments for transportation system attributes.

182 G. C. ~ICOL.IIDIS and R. Dossov

directional vectors of homogeneous preference groups. a visual representation of the preference structure of the sample. can be obtained. This representation can be very helpful for transportation specialists who can assess the preferences of particular groups of respondents. When the homogeneous preference groups are distinct from each other with respect to important socio-economic characteristics. the conclusions derived from an interpretation of the r-dimensional attribute space can be

confirmed by face validity. Face validity is obtained when findings conform to intuitively reasonable outcomes.

A major objective of this section is to identify homogeneous preference groups. The groups were formed by observing the structure in the elements of the matrix P. The rows of P can be interpreted as r-element vectors in a respondent space. This space can be divided into 2’ components which can further be divided into r(r - 1) polyhedrons by bisecting the axes with (r - 1) dimensional hyperplanes. The hyperplanes can be aggre- gated into homogeneous clusters so that the direction cosines for the mean projection of pairs of polyhedrons in a group is large in comparison to the direction cosines with the mean projections for polyhedrons not in the group.

The aggregation of the polyhedrons was accomplished through the use of d hierarchical clustering algorithm (Johnson. 1967). The input for the algorithm is the direction cosine matrix of all direction cosines between any two pairs of mean projections. The algorithm defines hierarchical clusters. from a weak cluster in which every polyhedron is a cluster by itself to a strong one where there is only one cluster. the whole space. Relative density considerations were used to define the one level of hierarchical clusters to be retained. Some applications of this algorithm are mentioned by Fillenbaum and Rapoport (1971) and Green and Rao (1973).

.A procedure similar to the one described above has been presented by Dobson and Kehoe (1973). Their aggregation procedure was based on the two criteria which follow: firstly. the direction cosines for the mean projection of pairs of polyhedrons in a group must be large in comparison to the direction cosines with the mean projections for polyhedrons not in the group: secondly. polyhedrons are clustered so that the merged ones are densely populated with respondent vectors in comparison to the region about them in the respondent space. A direction-cosine matrix was thus formed from which clusters were identified on the basis of the respondent densities. The process is thus subjective and becomes cumbersome and complicated as r increases. Further- more. if each of the existing polyhedrons was to be bisected once more subdividing the space into smaller polyhedrons to uncover subtle structures that could not be uncovered before. the number of polyhedrons becomes so large that it would be difficult to observe any structure in the direction cosine matrix. In order to make

the process more objective. it was decided to use the hierarchical clustering algorithm that was described previously.

The homogeneous perceptual groups. as defined from the clustering algorithm are related to the socio-economic characteristics and activity patterns of the groups to

investigate the correspondence between groups seg mented on the basis of preference judgments and arbitrary segmentation for socio-economic variables. If this correspondence is strong, then greater substantive significance can be attributed to both segmentations.

(b) Results of internal preference analysis. One objective of this section is to form homogeneous preference groups on the basis of preference judgments. Accord- ingly. the preference matrix was decomposed retaining three characteristic vectors. which accounted for 63% of the trace of X’X. A three dimensional solution was used since each of the remaining characteristics vectors produced only minor improvements in the least-squares approximation of X. In order to facilitate interpretation of the preference space, the final solution was rotated by an orthogonal varimax procedure. The statistical procedures employed in the decomposition phase (e.g. standardiza- tion and rotation) were implemented by SINGD. a PL/l program designed to process large matrice with particular ease (Dobson & Hepper, 1974).

By observing the elements of the matrix P. from eqn I, fourteen subjects were eliminated from further analysis because they were positioned at the origin. This result implies that the fourteen subjects were unable or unwilling to differentiate between the twelve attributes. This left a total of 492 respondents to be clustered into homogeneous groups.

The procedure to cluster the respondents has been presented in Section i(a). All respondents were positioned in a three dimensional space which was then divided into eight octants and 48 polyhedrons, in turn. Mean projections were computed for all non-empty polyhedrons and direction cosines were computed between all pairs of mean projections. There were 39 non-empty polyhedrons which resulted in an upper triangular matrix of direction cosines with a total of ((39 X 38)/2) entries. Direction cosines can be thought of as being correlation or proximity measures and represent the input for the hierarchical clustering algorithm.

Johnson’s clustering algorithm is non-metric, and it is capable of defining hierarchies by two methods. The “diameter” method was used to define five clusters. The clusters had respondent frequencies as follows: 55,55,193, 71 and 118. The five clusters or groups were determined at the 34th hierarchical level, the first being the one in which every polyhedron is a cluster in itself and the 38th and last hierarchical level corresponding to one cluster, the whole space. Adjacent levels to the 34th level did not produce clusters with relatively balanced densities. For example the 33rd level resulted in six clusters with respective subject densities of 55.4,5 1: 193,71 and 118, while the 35th level resulted in four clusters with respective densities of 55, 55, 246 and 118. Figure 3, which shows the computer output from this analysis, is presented for illustrative purposes.

The five homogeneous groups were related to the preference space. Figures 3 and 4 represent the geometric representation of the attributes, as defined by the matrix [.\Q’] of eqn 1 (after varimax rotation). In this space. average directional vectors for each of the tive groups are found by averaging on each dimension the direction

Disaggregated perceptions and preferences in transportation pianning 283

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Fig. 2. Hierarchical clustering scheme for the 39 non-empty polyhedrons of the preference model analysis. Five clusters are defined at the 35th hierarchical level.

Fig. 3. Dimension I (vertical axis) and dimension II (horizontal axis) of the internal vector model preference space. The attributes are identified by labeled points, and the respondent groups are

represented by vectors pointing at a circled number.

numbers of all subjects in a particular group. To facilitate the matter of interrelating the homogeneous preference groups and the preference space for all groups, two standardizations were implemented. Firstly, the coordinates of the twelve attributes were standardized to an allowable range from - 1 to + 1 by dividing all coordinates by the largest overall coordinate value. Secondly, the average directional vectors were standardized to unit length by dividing each coordinate of a vector by the square root of its original length.

Fig. 4. Dimension II (vertical axis) and dimension III (horizontal axis) of the internal veclor model preference space. The attributes are identified by labeled points. and the respondent groups are

represented by vectors pointing at a circled number.

Figure 3 shows dimension I vs dimension II. Dimension I contrasts refreshments and personal luxury service to basic transport service. while dimension II contrasts automatic vehicle control to all other attributes. Figure 4 in turn. shows dimension II vs dimension III. Dimension III contrasts privacy vs all other attributes. From the same two figures it can be seen that groups 1 and 2 point to the opposite general direction from automatic vehicles, indicating that respondents in these groups prefer a public transport system with a driver rather than a completely

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284 G. C. ?ilCoL.~lDiS and R. Do8a)zl

automated system. Respondents of group 5 seem to come to the opposite conclusion indicating a preference for automation. Respondents in group ? are not interested in refreshments and newspapers. while subjects of group 3 again point to the opposite direction of automation and do not seem to care about having their own private section. Respondents of group 4 do not care about private sections either.

In order to relate the preferences of the homogeneous groups with their socio-economic characteristics, profiles of the five groups were graphed. Figure 5 depicts the profiles for the total group of 192 respondents as well as the profiles of all five clusters. Subjects in each cluster and in the total group were divided into two or three

groups for each economic variable. Accordingly. these variables were assigned an integer number between 1 and 3. Mean vaiues. ranging from 1 to 2. were computed for those socio-economic variables divided into two groups. Income and age represented a three-way grouping. For these two characteristics. the percentage of individuals belonging to the first group of both variables was computed (i.e. percentage of the respondents in lower income brackets or the percentage of younger respondents). This percentage is believed to be a good indicator for these variables.

It can be observed that all five groups were almost identical with respect to the percentage of married individuals in each goup. as well as with their distribution

Fig. 5. Socio-economic and activity pattern profile for the 5 respondent groups and the total sample, as defined from the internal vector model.

Disaggregated perceptions and preferences in transportation planning 285

of various types of housing in each groups. On the other hand, the groups differ with respect to race, education and age.

Next, the five groups were related to their socio- economic and activity pattern variables through a ,$ statistic computed from a contingency table. The sample was divided into two or three groups on the basis of the same socio-economic grouping used to graph the profiles, which resulted in twelve contingency tables. Table 1 lists the socio-economic and activity pattern variables in the order of magnitude of their corresponding ,$/df value can be interpreted as a descriptive statistic which reveals, to some degree, the dependence between the grouping on perceptual preference judgments versus the grouping on the socio-economic or activity pattern variable being cross-tabulated.

The first three variables, race, education and age, are strongly related to the perceptual grouping of the respondents; the relationships are also statistically signifi-

Table 1. Chi square results from the internal preference analysis

TAXONOMIC VARIABLES ORDERED BY CHI SQUPRE FOR INDEPENDENCE AWNG PERCEPTUAL GROUPS

VARIABLE y*/df PROBABILITY

RACE 4.578 Lx.01 EDUCATION 3.500 b<.Ol AGE 3.190 p-z.01 LICENSE POSSESSION 2.221 .lbp>.O5 INCOME 1.681 .lo>p>.O5 NUMBER OF AUTOS 1.524 p>.10 MARITAL STATUS 1.050 P>.lO NUMBER IN HOUSEHOLD 0.936 o>.lO TYPE OF HOUSING 0.894 p>.lO USE OF TRANSIT 0.680 P>.lO TRIP TYPE 0.622 p>.10 SEX 0.291 p>.10

cant by conventional criteria. Other variables are also related in decreasing order of importance. The variable with the weakest relationships to the perceptual grouping was sex, but other characteristics, such as trip type and use of public transit. also showed a weak correspondence with the perceptual grouping.

Furthermore, the results of the x2 analysis and profile tabulation seem to be compatible with the preference patterns of the five groups. For example. respondents in group I. which had the largest percentage of males, were more educated and owned two or more automobiles, did not prefer automation to conventional driving Group 3. which had a large percentage of whites and a high percentage of individuals above 29 years old owning less than two automobiles did not seem to care about having their own private section and did prefer conventional driving to automation. Finally. members of hip group 5 are comprised of a low percentage of individuals under 29 with high percentages of blacks and females and with the lowest automobile ownership percentage seemmg to prefer automation. From the above discussion a trend is apparent: the more independent a group is from public transit the less automation is desired and vice versa.

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9. External preference analysis (a) Analytical methods. This section describes the exter-

nal preference analysis which uses preference and similarity information to develop a joint space of attributes and person weights or vectors. The merits of collecting and using additional information, such as similarity information will be investigated in the discussion section of this report. Only the subjects that provided similarities information were retained for further analysis.

In a previous research effort (Dobson & Kehoe, 1974), information was generated which allowed the computa- tion of an average attribute configuration from similarity judgments. This attribute space along with the preference statements of the respondents retained for this analysis. form the basic information for the joint attribute and person space. This is accomplished through a generaliza- tion of the basic unfolding model (Carroll, 1972: Coombs, 19523. The model has been implemented by a computer program (Carroll & Chang, 1972). which will subsequently be referred as the PREFMAP. PREFMAP can combine similarity and preference information under four different phases. The phases will be referred to as phases I, II, III and IV: as one goes from phase I to phase IV the underlying assumptions and model complexity are consid- erably reduced. A brief description of the model is provided below to facilitate the understanding of subsequent parts of this report.

Phase IV. which is the simplest model of all, is the vector model. It assumes a set of stimulus points, the twelve attribute in our case, embedded in a multidimensional space. This space has been defined from the analysis of similarity judgments. In this model, different respondents are represented by vectors. The preference order for a given respondent is given by the projection of stimuli onto the respondent’s vector. These vectors can be interpreted in terms of the relative importance of the dimensions to the preference judgments. The cosines of the angles between the vector and the dimensions, directly measure these relative importances.

The model is defined algebraically as follows. It is assumed that all n individuals, share a common r dimensional space for the m stimuli points. Let X = (X,, ): j = 1.2,. . . m; t = I,?. . , r, represent the coordinates of the m stimuli into the r space. and let S = (&); i = 1.2,. , n: j = 1.2.. , m represent the scale values of preferences for the n individual for the m stimuli. Preference scale values are related by a regression equation of the form:

s, = b,X; + c,.

Firstly, the coefficients b,, of the regression equation are estimated: by normalizing the b, to unit length we subsequently obtain the direction cosines for the vector for the ith respondent.

Phase III corresponds to the multidimensional case of the simple unfolding model [(Bennett & Hays, 1960)]. In the model. stimuli are represented in a r dimensional space. as in the vector model. but the n respondents are now represented as a set of “ideal points” in the same space. The farther a given stimulus point is from a

286 G. C. ~ICOLAIDIS and R. DOESON

respondent’s ideal point the less the stimuli is preferred by this individual. Distances are assumed to be metric (i.e. Euclidean).

Let us define once more X=(X,): j = 1.2.. , m; t = I,?, . . . , r, to be the stimulus coordinates in r dimensions and Y = (X,): i = 1.2,. . n. r = I,?, . r. to be the coordinates of the ideal points in the same space. Let S = ($); i = 1,2.. . , n, j = 1,2,. , m, represent the preferences for all II individuals. In the metric version of PREFMAP, which is the version used for the present study since the preference judgments were all metric (i.e. interval scaled), the preferences S are linearly related to the square of the Euclidean distance d,,;

where

S, -i: n,d f + hi

. d;, = 2 (X,, - Y,, )’ ,=I

and o.b constants. Unlike phase III which assumes that all individuals weight equally each dimension of their common space, phase II allows different respondents to weight the dimensions differently. This model is referred to as the weighted unfolding model. The difference from phase III is algebraically in the definition of the distances d,,. In this model

d:= [z w,,(Y,, -X,J’]

In all other respects the model is similar to the one presented in phase III.

Phase I, referred to as the general unfolding model, allows different respondents not only to weight the dimensions of the space differently. but also to choose an idiosyncratic set of “reference axes” within the common space.

As has been mentioned. only the individuals that provided both similarity and preference judgments were retained for the analysis reported in this section. The original number of respondents falling into this category was 243. but seven respondents were disregarded because they were unable to differentiate between the twelve attributes, stating equal preference for all twelve of them. This left a total of 236 individuals whose preferences are to be fit into a common similarity space.

For comparative purposes among the various phases of the PREFMAP model. it was decided to examine solutions from phases II and IV. For phase IV, the 236 respondents were thus clustered into homogeneous groups on the basis of the direction cosines between the respondent vectors and the axes of the attribute space. For phase II, the idiosynchratic weights on the dimensions by each respondent was the basis for forming respondent clusters. By subsequently relating the homogeneous groups as defined from each phase, to the socio-economic and activity pattern variables of the groups, we were able to investigate which phase provided homogeneous groups that were related strongly to an arbitrary segmentation for socio-economic characteristics.

The homogeneous groups for each phase were defined in a way similar to the one presented in Section l(a). Profiles for all homogeneous groups were formed and all groups were cross-tabulated to their socio-economic variables to derive x’ values. Again the procedures and methods are the same as the ones described in Section

l(a). (b) Results of the external preference analysis. As in the

internal preference analysis, a major objective of the external preference analysis is to form homogeneous perceptual groups. This time the basis for grouping includes both preference and similarity information. More specifically, by fitting through PREFMAP the preferences of the 236 subjects into the three-dimensional similarity attribute space. a set of direction cosines was obtained between each respondent’s vector and the axis of the attribute space from phase IV; phase II resulted in a set of idiosyncratic weights for each dimension of the attribute space. Homogeneous groups were formed separately on the basis of the direction cosines and the set of idiosyncratic weights. The analytical results will subse-

quently be referred as phase IV and phase II results.

RESULTS PROM PHASE IV

The three-dimensional attribute space as defined by similarities information (Dobson & Kehoe. 1974) represented the multidimensional space in which preference vectors were fitted for each of the 236 respondents. Directional cosines between each vector and the axis of the space were defined through phase IV of the PREFMAP model. Each respondent was therefore represented by his vector in the similarities space (i.e. a set of three directional cosines).

All respondents were positioned in a three-dimensional space on the basis of their directional cosines. The space was then divided into eight ocrants and 48 polyhedrons, in turn. Only 32 of those polyhedrons were non-empty. Following a procedure similar to the one stated in Section I(b), five clusters were identified finally at the 28th hierarchical level with respective densities of 109.58.7.40 and 22 respondents.

Figures 6 and 7 correspond to the attribute space with the five perceptually homogeneous groups tit in it, each as an average directional vector. The first figure shows dimension 1 vs dimension 2. Dimension I contrasts basic transport service vs refreshments and newspapers for sale, while dimension 2 contrasts personal luxury service vs all other attributes. Figure 7 shows dimension 2 vs dimension 3. Dimension 3 contrasts automatic vehicle control vs room for strollers and own private section. Combining the two figures together it can be observed that group I points towards the direction of “low fares” and to the opposite direction of “own private section” and “temperature control.” Respondents in group 2 tend to prefer “comfort and quiet” while respondents in group 3 have almost exactly the opposite view to the respondents in group I. For group 2, personal luxury service (i.e. own private section and temperature control) seems to be more important than cost considerations. Group 4 prefers automation. “safety”, and “arrive on time” while group 5 is interested in low fares.


I

Fig. 6. Dimension I (vertical axis) and dimension 11 (horizontal axis) of the external vector model preference space (Phase IV). The attributes are identified by labeled points. and the respondent

groups are represented by vectors pointing at a circled number.

Fig. 7. Dimension II (vertical axis) and dimension III thorizonta! axis) of the external vector model preference space (Phase IV). The attributes are identified by labeled points. and the respondent

groups are represented by vectors pointing at a circled number.

In order to observe the socio-economic characteristics of the five homogeneous groups, profiles for the total (236) respondent group as well as the five clusters are depicted as Fig. 8. The five groups are similar in only the average numbers of cars owned by each group. It seems that there is quite a discrimination between the groups on the basis of the remaining socioeconomic and activity pattern variables.

Table 2 lists the socio-economic and activity pattern variables in order of magnitude of their corresponding x2/df values. The variables that are strongly related to the perceptual grouping are race, education and age. the very same ones that appeared in the internal preference analysis results. The variable with the weakest relationship was marital status. but number of cars and sex also showed a weak correspondence with the perceptual grouping.

The results of the x2 analysis and profile tabulation

seem to be compatible with the preference patterns of the

Table 2. Chi squared results from the external preference analysis vector model

TAXONOMIC VARIABLES ORDERED BY CHI SDUARE FOR INDEPENDENCE AMONG PERCEPTUAL GROUPS

RACE 3.087 EDUCATION 2.566 LICENSE POSSESSION ;:i;; AGE TYPE OF HOUSING 1.712 TRIP TYPE 1.364 NUMBER IN HOUSEHOLD 1.174 USE OF TRANSIT 1.167 INCOME 0.743 SEX 0.636 NUMBER OF AUTOS 0.474 MARITAL STATUS 0.328

.05>p>.Dl 0.10

.05>p>.Ol 0.20

.os>p>.o1 0.20

.DSz=p>.Ol 0.10 ps.10 C.10 p>.lD 0.10 p>.TD 0.10 p>.lO 0.20 p>.lO 0.20 p>.10 0.20 p>.lD 0.20 p>.10 0.20

five groups. For example, respondents in group 1, which are represented by a high percentage of females and of whites with relatively many members in the household, are interested in low fares rather than personal luxury. Group 2 respondents, which use public transit more than those of any other group, possess the least licenses, own the fewest automobiles, and are largely youths; these respondents prefer comfort and quiet in a transport mode. Inferences about group 3 are difficult to state due to the smaI1 number of respondents in it. Group 4, represented by a high percentage of singles with a relatively higher income, seem to prefer automation and other attributes. Finally, group 5 with a high percentage of low income individuals preferred low fares.

Note that phase IV is very similar in at least the format in which preferences are fit (vectors) into the preference space with the internal preference analysis presented in Section 1. Therefore, the results from both analyses are compatible and will be related to each other in the discussion section.

RESULTS FROM PHASE II

As has already been mentioned in Section 2(a), phase II corresponds to the weighted unfolding model. Preferences were fitted into the attribute space and all respondents were represented as ideal points in the same attribute space. In addition, each respondent weights the dimensions of the space idiosyncratically. The weights in the three-dimensional attribute space form the basis for the identification of homogeneous perceptual groups. Respon- dents that apply similar weights to the dimensions of the space are thought of as having similar preference characteristics.

All 236 respondents were positioned in a three- dimensional space on the basis of their weights. The space was eventually divided into 48 polyhedrons of which only 29 were non-empty. Through the same procedure described in Section l(b). five clusters were identified on the 25th hierarchical level. with respective densities of 15, 8. 25. 97 and 94.

In order to relate each homogeneous group to the attribute space. average ideal points and average weights on each dimension were computed for the five groups. In turn, the attribute space of Figs. 9 and 10 was adjusted for each of the five clusters by weighting accordingly each

G. C. ~ICOL.AiDIS and R. DWSOY

of the three dimensions of the space. Similar adjustments were made to the average ideal points. Figures I1 and I2 show dimension I vs dimension 2 and dimension 2 vs dimension 3. respectively, of the first group. It seems that the respondents of this group are seeking personal luxury (e.g. “own private section,” “temperature control,” and “comfort and quiet”) rather than “low fares” and “arrive on time.” Figures 13 and 13 represent the group 2’s configurations. Respondents in group :! would rather have safety, comfort and quietness. Group 3’s configurations are presented as Figs. 15 and 16. Respondents in this group prefer basic transport services such as waiting times. access to many destinations. arrive on time. and Fafety. Figures 17 and 18 show the configurations for the

fourth group whose respondents share the same view with group 3’s respondents. Finally, Figs. 19 and 20 represent the configurations for group 5. Safety is preferred with respect to basic transport service which, in turn, is preferred to personal luxury service by this group.

The socio-economic profiles for the five groups have been tabulated and are presented as Fig. 21. The groups are similar only with respect to the percentage of license possessions in each group. .4 ,yz analysis. presented as Table 3, reveals that only trip type and education are strongly related to the perceptual groupings. Marital status and sex are the variables showing the least correspondence.

In relating the socio-economic profiles and y’ results to

Disaggregated perceptions and preferences in transportation planning

Fig. 9. Dimension I (vertical axis) and dimension II (horizontal axis) of the similarity space. The attributes are identified by labeled

points.

Fig. 10. Dimension II (vertical axis) and dimension III (horizontal axis) of the similarity space. The attributes are identified by labeled

points.

the preference structure of the homogeneous groups, it is possible to observe the following. Group 1 is represented by a higher percentage of blacks with a lower income but limited education, and its respondents are interested more in personal luxury service rather than basic transportation service. Group 2 has a small number of respondents; therefore, it is difficult to draw any meaningful inferences. Group 3 has a higher percentage of young people and a higher percentage of whites and married individuals with relatively more cars in their housholds who prefer basic transport service and safety. Group 4, which has the

highest percentage of whites, uses transit extensively and

prefers basic transport service and safety. Finally, group 5 members prefer basic transport service as opposed to personal luxury and are less educated. own less cars than the members of group 4 but make more work trips than the members of any other group.

DISCUSSION

This paper centers around intertwined substantive and methodological objectives. On the assumption that not all individuals have the same pattern of preferences. it

289

/ 1’ / / j / // /-- /

(II ! /

Fig. 11. Dimension I (vertical axis) and dimension 11 (horizontal axis) of the weighted unfolding model (Phase II) for the ftrst group. The attributes and the ideal point for group 1 are identified by

labeled points.

/ ‘\

\ Y. = -0.4291

\ u3 =.-0.8056

\\ ‘\ --i_

‘\ \

\

‘\ \

\ \ / ’ .\ I ’ I ‘\

‘---_

Fig. 12. Dimension II (vertical axis) and dimension III (horizontal axis) of the weighted unfolding model (Phase II) for the first group. The attributes and the ideal point for group 1 are identified by

labeled points,

becomes necessary to determine the different preference patterns of alternative population segments when design- ing transport systems for the whole population. By

applying three psychometric models, it was possible to evaluate the different kinds of information which can be derived from preference judgments vs preference and similarity judgments. In addition, it was possible to

G. C. NIC~LAIDIS and R. DOSSON

/ /

: \ . ,’ \ .IEXPEPki’JRE FDNTROL ‘,

1-k ,-(’

\

\ ,’

\

\ ,’

.

\ \ \ : , \ ?E’SEJIlWTS L k4s?nPE?s .

\ ‘. -__---

\ \

\. ,’

Fig. 13. Dimension I (vertical axis1 and dimension II (horizontal axis) of the’weighted unfolding model (Phase ID for the second group. The attributes and the ideal point for group 1 are identified

by labeled points.

Fig. 14. Dimension I (vertical axis) and dimension II (horizontal axis) of the weighted unfolding model (Phase II) for the second group. The attributes and the ideal point for group ! are identified

by labeled points.

investigate the implications of alternative analytical

assumptions for defining homogeneous population seg-

ments and measuring their preferences. A summary of the

findings with respect to models. perceptual dimension.

preference groups and socio-economic profiles is shown

in Table 4.

One key attribute of innovative urban transport

\ / l

\ \ - .~_ _ _ -- _

Fig. IS. Dimension I ivertical axis) and dimension 11 ihorizontal axis) of the weighted unfolding model (Phase II) for the third group. The attributes and the ideal point for group 3 are identified by

labeled points.

Fig. 16. Dimension II (vertical axis) and dimension III (horizontal axis) of the weighted unfolding model (Phase 11) for the third group. The attrihutes and the ideal point for group 3 are identified by

labeled points.

systems is automatic vehicle control. The vector model

showed considerable sensitivity to this variable. For the

internal vector model. three groups showed a preference

for manual control over automatic vehicle control. .A

socio-economic variable which is common to at least two

of the three groups is the household ownership of two or

more automobiles. Both the internal and external vector


Fig. 17. Dimension 1 (vertical axis) and dimension II lhorizontal axis) of the weighted unfolding model (Phase IT! for the fourth group. The attributes and the ideal point for group 4 are identified

by labeled poin!s.

Fig. 19. Dimension I (vertical axis) and dimension II (horizontal axis) of the weighted unfolding model (Phase II) for the fifth group. The attributes and the ideal point for group .S are identified by

labeled points.

i ,_ ,’ ‘\ ‘-. -- -7 ,’

/

‘\ / DYli w,:;i. 5’:7.;1. /

’ \ I’ /

\ / ,’

.--’ *\ “Y’, EFi:!aTI SC?:“‘. /

‘-._+_--- . / ‘. /

,/’ \ , ,- ,, =.__ : __A Y

‘\ /’

Fig. 18. Dimension 11 (vertical axis) and dimension III (horizontal axis) of the weighted unfolding model (Phase 11) for the fourth group. The attributes and the ideal point for group 4 are identified

by labeled points.

models identified a group of respondents who favored

automatic vehicle control. In contrast to the groups which

did not prefer automatic vehicle control. these groups

were comprised in a large part of females, blacks and

older individuals. Preference for manual control over

automatic vehicle control was most apparent among those

population segments which are comprised of individuals

who are like11 to be passengers in either public transit or

Fig. 20. Dimension II (vertical axis) and dimension III (horizontal axis) of the weighted unfolding model (Phase II) for the fifth group. The attrihutes and the ideal point for group 5 are identified by

labeled points.

the farnil!. automobile showed a preference for automatic

vehicie control.

Both the external vector model and the external

weighted unfolding model found population segments

which were characterized by their sensitivity to low fares.

In the latter regard. the vector model yielded results

which were intuitive]) appealing. For example. goup 5

showed a preference for low fares, and their socio-

292

--

Fig. 11. Socio-economic and actkty pattern profile for the 5 respondent groups and the total sample. as defined from the external weighted unfolding model.

Table 3. Cbi csuare results from the external preference an&is--weighted unfolding model

r TAXONOMIC VARIABLES ORDERED BY CHI SQUARE FOR

INDEPENDENCE AMONG PERCEPTUAL GROUPS

--r---

VARIABLE ; 2 x idf TRIP TYPE 3.c19 EDUCATION 2.204 NUMBER OF AUTOS 1.868 NUMBER IN HOUSEHOLD 1.8Oi RACE

:%"!F HOUSING / it!;! AGE USE OF TRANSIT

j I.246 / 0.774

LICENSE POSSESSION 1 0.486 MARITAL STATUS / 0.664 SEX ) 0.231

r t

PT.10 0.27 ?>.I0 0.20 p>.10 p>.10 !

3.20

p>.iO

economic profile indicates that the group is comprised of a

high percentage of individuals in the low income bracket.

Similarly. group I. which also revealed a preference for

low fares, was made up of young individuals from large

households. On the other hand, group 1 of the weighted

unfolding model. which was comprised largely of

lower-income blacks with a limited education, preferred

personal luxury service over low fares and basic transport

service. The social psychology of the latter outcome is

beyond the scope of the current report. Shinn (1973

reports a comparable finding. and he concludes blacks

may be more sensitive to the status implications of public

transit than whites.

Respondents are generally favorably inclined towards

Disaggregated perceptions and preferences in transportation planning

Table 4. Summary of preference structure and so&economic profile of respondent groups for the 3 preference models

293

INTERPRETATIONS OF DlMENSIDNS

1. REFRESHMENTS & PERSONAL LUXURY SERVICE VS. BASIC TRANSPORT SERVICE

INTERNAL ANALYSIS

2. MANUAL DRIVING RATHER LARGE HOUSEHOLD THAN AUTOMATION, NOT HIGH PERCENTAGE OF MALES INTERESTED IN REFRESH- OWN TWO OR MORE AUTOMOBILES MENTS & NEWSPAPERS USE OF TRANSIT

2. AUTOM4TIC VEH- ICLE VS. OTHER ATTRIBUTES

3. MANUAL DRIVING RATHER THAN AUTO:'ATION NOT INTERESTED IN PriIVACY

HIGH PERCENTAGE OF WHITES HIGH PERCENTAGE UNDER 29 YRS. OLD OWN LESS THAN TWO AUTOMOBILES I

3. PRIVACY VS. OTHER ATTRIBUTES

4. NOT INTERESTED IN PRIVATE SECTION

LITTLE USE OF TRANSIT HIGH PERCENTAGE UNDER $lO,OOO/YR. HIGH PERCENTAGE OF WORK TRIPS

5. AUTOMATION HIGH PERCENTAGE OF FEMALES HIGH PERCENTAGE OF BLACKS LOW PERCENTAGE UNDER 29 YRS. OLD OWN LESS THAN TWO AUTOMOBILES

1. BASIC TRANSPORT SERVICE VS. REFRESHMENTS 8 NEWSPAPERS

1. LOW FARES, NOT INTERESTLD IN PRIVATE SECTION & TEMPERATURE CONTROL

HIGH PERCENTAGE OF FEMALES HIGH PERCENTAGE OF WHITES LARGE HOUSEHOLD HIGH PERCENTAGE UNDER 29 YRS. OLD

EXTERNAL ANALYSIS PHASE IV.

(:z

IXTERNAL LNALYSIS 'HASE II. :WEIGHTED UNFOLDING MODEL)

2. PERSONAL LUXURY VS. OTHER ATTRIBLJTES

2. COMFORT 8 QUIET OWN LESS THAN TWO AUTOMOBILES FEW LICENSE POSSESSIONS USE OF TRANSIT HIGH PERCENTAGE UNDER 29 YRS. OLD

3. AUTOMATION VS. ROOM FOR STROLLERS & OUN PRIVATE SECTION

3. PRIVATE SECTION & TEMPERATURE CONTROL, NOT INTERESTED IN LOW FARES

(SM4LL NUMBER OF RESPONDENTS IN THIS GROUP)

LOW PERCENTAGE OF MARRIED PEOPLE LOW PERCENTAGE UNDER SlO,OOO/YR. LOW PERCENTAGE UNDER 29 YRS. OLD I

4. ALITOMATION ,SAFETY , & ARRIVE ON TIME

5. LOW FARES 1 HIGH PERCENTAGE UNDER $lO.OOO/YR. 1

1. PERSONAL LUXURY VS. LOW FARES b BASIC TRANSPORT SERVICE

HIGH PERCENTAGE OF BLACKS HIGH PERCENTAGE UNDER SlO,OOD/YR. LIMITED EDUCATION

2. SAFETY, COMFORT & I

(SMALL NUMBER OF RESPONDENTS QUIETNESS IN THIS GROUP) I SAME AS

EXTERNAL ANALYSIS PHASE IV

3. BASIC TRANSPORT ADVANCED EDUCATION

SERVICE A SAFETY LOW PERCENTAGE UNDER 29 YRS. OLD HIGH PERCENTAGE OF WHITES HIGH PERCENTAGE OF MARRIED PEOPLE

4. BASIC TRANSPORT SERVICE A SAFETY

HIGH PERCENTAGE OF WHITES USE OF TRANSIT

I

5. SAFETY TO BASIC TRANSPORT SERVICE TO PERSONAL LUXURY

LIMITED EDUCATION HIGH PERCENTAGE OF WORK TRIPS

basic transport attributes, such as ‘arrive on time” and “short waiting time.” For both the internal and external vector models, respondent vectors, with only one exception, pointed in the direction of basic transport attributes along the first dimension. Furthermore. the weighted unfolding model showed that three of five population segments preferred basic transport service attributes and safety over personal luxury service and general amenities. While the ubiquity of the preference for basic transport attributes is impressive, the design implications are minimal. Everyone wants a system which performs well.

A previous investigation by Dobson and Kehoe (1973)

found the working status, sex, income, and age of an individual to be statistically significant socio-economic correlates of his point of view towards transit attributes. While the current study supports generally the previous results, there are some clear differences. Both the internal and external vector models concurrently emphasized race and de-emphasized trip type (i.e. whether the respondent made at least one work trip per week). The weighted unfolding model. however, was very sensitive to the trip variable. None of the preference models grouped individuals in a manner which corresponded to their sex, although Dobson and Kehoe found sex to correlate well with patterns of perceptual similarity judgments. Further-

294 G. C. NICOL.AIDIS and R. DOESON

more. license possession and the number of household automobiles covaried more strongiy with homogeneous population segments formed according to preference judgments than similarity judgments. Socio-economic variables which failed consistently to correlate strongly with population segments predicated upon subjective judgments from individuals include marital status. type of housing and use of transit.

An advantage shared by the internal vector model, the external vector model and the external weighted unfolding model is the multidimensional attribute space upon which all of them are predicated. The internal vector model generates its attribute space from respondent preference judgments, but both of the external models require similarity judgments to generate an attribute space. The external models subsequently reveal underlying preferences with respect to the similarity space according to the pattern of their preference judgments. The attribute space permits the transportation planner to examine the perceptions and/or preferences towards transit attributes from a respondent’s perspective rather than from the perspective the planner imagines the respondent to hold. By applying one or more of the models described in this paper, the planner can test the validity of his preconceived notions about the interrela- tions among attributes.

Both the internal and external models revealed a set of attributes which connoted the concept of personal transport service; these attributes. which clustered together in the attribute space, included “arrive on time.” “short waiting time. ” “many available destinations” and “short travel time.” Two attributes. “cafety” and “low fares,” were proximate often to basic transport service. Another set of attributes which clustered together connoted personal luxury service; these were “own private section, *’ “comfort and quiet,” and “temperature control.” Automation. space for strollers, the availability of refreshments and newspapers at transit stations. and privacy occasionally defined dimensions. and they were distinct from the remaining attributes from the respondents’ perspective. If transport systems were designed to be compatible with the perceptions and preferences of these respondents, the same individuals could become public transit users.

The methodological objective of this paper was not to compare and contrast rigorously alternative psychometric techniques, but it was to reveal the subtle insights provided by psychometric models which are fundamen- tally different. All three models were able to identify the strong preference of respondents for basic transport service. Because of the tendency of the internal vector model to define dimensions in terms of a limited number of attributes, it may have placed undue emphasis on the dislike of respondents for automatic vehicle control. Dobson. Golob and Gustafson (1974) have also found that one or two attributes defined their dimensions from the internal vector model; in that study. 32 attributes of an evolutionary transportation concept were judged by respondents. The external unfolding model tends to obscure the multidimensional nature of preferences for

attributes by expressing preference in terms of a distance function from an “ideal point.”

The external vector model may be a convenient and practical compromise between the above alternatives. It tends to define dimensions in a more meaningful way than the internal vector model, while it preserves the multidimensionality of preferences.for attributes. hddi- tionally. it tended to reveal a considerable number of differences for the homogeneous population segments defined through it. A disadvantage. however, of the external vector model with respect to the internal model is the requirement for the collection of an additional data set. More rigorous analyses are mandatory before firm conclusions could be drawn about the models. Their joint application in the current contexts has provided information beyond that which could be gained from any one of them.

The significance of the investigation reported here can be judged by the new knowledge which it contributes to body of attitudinal research on transportation planning and the number of new analyses and applications which are generated by it. The current investigation is itself a synthesis and/or extension of the earlier investigations of Paine et ul. (1969). Golob, Can&. Gustafson and Vitt (197’3, Dobson et al. (1974) and Dobson and Kehoe (1974). The fundamental psychological tenet around which this report centers is that people have different preferences but no individual. at least no representative individual, is totally distinct. This sharing of common preference patterns allows the understanding of how alternative innovative urban transport designs variously benefit different population segments. The techniques applied herein permit the definition of groups which are truly homogeneous and avoids arbitrary segmentations of a population. In the latter regard, the results of this investigation as well as those of Dobson and Kehoe (1974) show that the variable “use of transit” is relatively unrelated to patterns of preferences and perceptions toward transit attributes. Additional quantitative investigations are required to link securely patterns of perceptions and preferences to the decision making of individuals with respect to transportation-related alternatives.

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