Journal of Economic Psychology 3 (1983) 333-345 North-Holland

333

SWITCHING COMMUTERS FROM CAR TO PUBLIC TRANSIT: A MICRO MODELLING APPROACH *

Ian FENWICK, Roger HEELER and Patricia SIMMIE

York University, Ontario, Canada

Received September 26, 1982; accepted March 28, 1983

This paper examines the perceptions and values of commuters in Toronto concerning public transit and the private car. Interviews were conducted at an office location readily accessible by car, bus or subway. Respondents were characterized by their predominant commuting mode and provided ratings of the perceived benefits of the modes available. Respondents then completed a conjoint analysis task designed to estimate their trade-offs, or the extent to which they were prepared to sacrifice on one aspect of a commuting journey in order to gain on another. The conjoint analysis used a fractional factorial design allowing main effects and two way interactions to be estimated.

The conjoint analysis methodology is used to develop benefit segments and evaluate methods of marketing public transit.

1. Introduction

Increasing the use of public transit for commuter journeys is a major objective of energy-conscious policy makers. Switching commuters to public transit offers lower per capita energy consumption, and reduces traffic congestion and other problems inherent in dealing with large numbers of cars. The marginal benefits are particularly impressive. The cost and fuel use of one more passenger on a bus or a subway are essentially zero. An extra car on the streets imposes a non-zero cost on its user and on society in general. Even on a full cost basis there are substantial benefits from increased transit use. As transit use increases, it becomes economic for a transit system to improve the range and frequency of its service. This in turn improves the quality of the

* Funding for this study was provided by Consumer and Corporate Affairs Canada. Mailing address: I. Fenwick, Faculty of Administrative Studies, York University, 4700 Keele

Street, Downsview, Ont. M3J lP3, Canada.

334 I. Fenwick et al. / From COT to public transit

product to users and further increases usage. The application of consumer analysis to transit planning is particu-

larly difficult because of the rigidities built into individual’s travel patterns, and the considerable variation in the level of service afforded different individuals. For example, the cost to individuals of commut-

ing by car may vary considerably. If a household already possesses sufficient cars for its members’ commuting needs, the marginal com- muting cost may be low. On the other hand, the acquisition of a car for commuting represents a major investment. This short term rigidity reduces the relevance of many experimental studies of transit usage. A one-month price or service experiment may be too short to affect automobile acquisition and disposal decisions.

Variation in the individual level of service provided by transit increases the variance to be explained in survey studies of consumers’ transit use. In consumer behavior one can always expect considerable variation in purchase patterns as a result of such factors as the occasion of use, the benefits sought by the individual, the individual’s percep- tions of the product available, the personal characteristics of the individual and his/her social circumstances. But for most consumer goods the physical characteristics of the product are approximately the same for each individual and each purchase occasion. For example, each can of a given brand of baked beans will contain nearly the same number of beans in a sauce of similar taste and a package of identical format. By contrast for transit the physical characteristics of the prod- uct may vary considerably over individuals. The same bus may provide the convenience of a car for one person, whose home and place of work coincide with bus stops on a frequently travelled route, but a very low level of service for another whose home and work are a mile away from bus stops and whose hours of work are at times when the frequency of service is poor. The resulting high level of variance to be explained introduces considerable noise to aggregate survey studies and makes analysis at the individual consumer level especially desirable.

As experiments are suspect, and aggregate surveys subject to large variance, a method of measuring consumers’ values and perceptions at the individual level is particularly valuable. Such a method is provided by conjoint (or trade-off) analysis and micro-modelling. It should be noted that in the transit context the method provides medium to long-term results. Analysis is based on individuals’ values and percep- tions. In the short term these values and perceptions may be overridden h,, +hP r;,,;&+x, font,.,.0 ..,,.,‘fi..,l.. ---L---J

I. Fenwick et al. / From car to public transit 335

2. The research method

The analysis methods used here model rational decision making as typified by the learning hierarchy. In the learning hierarchy consumers are assumed to go through a logical sequence of steps prior to a decision. These steps are usually described as awareness, interest, evaluation, trial, and adoption. The critical evaluation step is decided by a balancing of the consumer’s needs with the market offerings as perceived by the consumer. This mode of decision making is believed to predominate for high involvement decisions. Choice of travel to work mode would normally be a high involvement decision for an individual. Other decision systems (e.g. dissonance, low involvement) apply in other situations (for a full discussion of types of consumer decision- making, see Ray 1982: ch. 7).

Individuals have many reasons to travel. Different criteria may be used for different types of journeys. Transit researchers must be careful to establish a particular domain of enquiry. For the present study the focus was on individual week day commuting, a very specific and real situation.

In order to use conjoint analysis, it is necessary that we define a precise, and concise, set of criteria on which people actually base their commuting decisions. Fortunately, the very extensive literature on transit research does suggest that travellers use a fairly well-defined and limited set of criteria in evaluating transport modes.

3. Transit research

Previous research on consumers’ choice of transport mode can be grouped into three general categories: ex-post experimentation, demand models, and consumer segmentation studies.

Properly controlled experiments are rare in the transit field. However several transit systems have tried new programs, the results of which can be interpreted as ex-post experiments. For example, the Denver Regional Transportation District experimented with free transit rides outside of the rush hour (Crosby 1979). The program appeared to increase ridership by young, lower income people with limited access to a car. A study of the potential for car-pooling found that cost was almost irrelevant for switching from solo driving to car pools (Vancouver

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Car Pool 1977). The conclusion was that positive incentives probably had very little impact on encouraging car pooling, but negative incen- tives such as increased parking costs and bus lanes might be effective.

Demand models, using econometric techniques to predict aggregate demand for transit, and consumer segmentation studies which investi- gate the transit demands of specific groups (e.g. the elderly) are more common. Key findings from this body of research are summarized in table 1. Overall, these studies point to the existence of four principal factors in the choice of travel to work: cost of the trip, service provided (in particular convenience, reliability and comfort), journey time (both travel time and waiting time) and auto auailubility. In addition, some studies show privacy and safety to be important factors. It is on these decision factors that the current study is built.

4. The study

Field research was conducted in a major North American city with an established transit system. The population of interest was individuals who commute to work. A single research site was used. Consequently, for our sample all trips to work have a common destination eliminating one source of variation in analyzing attitudes and preferences. This

advantage is purchased at the cost of reduced generalizability. The office complex used was chosen as being large enough to provide

the desired sample size, containing a variety of occupational groups, and being known from previous research to have a mix of commuters using transit and car. Part of the overall research design required a comparison of car and transit commuters, so equal size samples of both groups were required to maximize the sensitivity of the comparison within the overall sample size.

Self-completion questionnaires were distributed to respondents as they arrived for work. Respondents completed and returned the questionnaires through the day as their personal work schedules die- tated. A $1 lottery ticket was used as an incentive to complete the questionnaire. A total of 400 questionnaires were distributed, of which 279 (70%) were returned. Of these, 60 (21%) were completed by transit users (i.e. individuals who had used public transit to commute to work on at least 3 of the last 5 days). An equivalent size sample of non-tran- sit users was randomly selected from the remaining responses, subject

338 I. Fenwick .SI al. / From car 10 public wansit

to the respondent living within the area covered by the city’s transit system. Of the transit users 25% used a car for a segment of their journey; in the micro-modelling analysis these are referred to as dual users.

The questionnaire contained four sections. The first section mea- sured transit use. The second section contained forty attitude items. Twenty of these items concerned opinions of the public transit services. A typical item was “I can get to work on time using the transit system”. Respondents were asked to respond to this stimulus on a five point “ strongly agree” to “strongly disagree” scale. The remaining twenty items asked the same questions with “transit system” replaced by “car”.

The third section collected the data required for conjoint analysis. Six attributes, each at two levels, were used. These were journey cost ($10 or 25 per week); comfort (plenty of room to sit or too crowded for comfort); journey time (same as current commuting trip or 15 minutes shorter); wait for service (2 minute wait or 10 minute wait); walking distance involved (door to door or 3 block walk); and reliability of service (always on schedule or have to allow leeway for delays). The levels on the attributes were chosen to provide a range of response while remaining credible.

Travel scenarios were constructed by combining attributes. Each scenario contained one level of every attribute. For example, one scenario was:

A journey to work which: _ costs $25 per week - requires a 3 block walk at one end - will involve a 2 minute wait - will take as long as your current trip - will offer plenty of space for you to sit - and will ensure that you arrive right on schedule

The two levels of each of six attributes yielded sixty-four possible scenarios, far more than respondents can reliably handle. An orthogo- nal experimental design was created involving a subset of sixteen scenarios. This subset was created using the principles of experimental design, to allow estimation of all main effects and most two-way interactions (see Green 1974).

I. Fenwick er al. / From car to public transit 339

Respondents were asked to rank order the sixteen scenarios. In making their rank orderings respondents must implicitly trade off the features of each hypothetical journey. For example, they must decide whether a higher cost outweighs convenience and reliability. Subse- quent analysis of the rank orders (described below) enables the relative values placed on these features to be estimated. The strength of the technique is in forcing trade-offs, rather than simply asking whether convenience or reliability is the more important. The exact instructions given to respondents were as follows:

Instructions for ranking travel scenarios

Now we would like you to look at a series of descriptions of a “trip to work”. The trips described on the set of cards are not meant to be either public transit or automobile, but rather a trip which might be taken by either mode.

Each card in the set contains six statements describing characteristics of a trip to work. What we would like you to do is sort these cards into a pile where the card on the top of the pile most closely describes your preferred trip to work, and all the other cards are in descending order of preference for the trip to work.

Many people find that it is easier to sort the cards if they first divide them into two piles - one describing “good” trips, and the other describing “bad” trips. Then each of these two piles can be divided as well - perhaps into “best”, “medium” and “average” for the “good” pile, and “average” and “awful” for the “bad” pile. If you continue to divide the piles, you will soon have all the cards sorted in order. Although this task may seem complicated, there are only 16 cards to sort, so most people find it really only takes a few minutes. We appreciate your efforts.

Now - we would like you to do 2 things with the sorted cards:

1. Number them in order of preference in the box in the top right hand comer - 1 for the most preferred, 2 for the next most preferred, etc., to 16 for the least preferred.

2. Starting with the “trip” which you rated as number 1, assign it 100 points. Assume that all the other trips can have between 0 and 100 points, depending on how good or bad they are, write the number of points on the line below the box where you put the rank on the cards. The only restriction is that the trip ranked “2” must be given fewer points than the trip ranked “l”, and similarly for all lower ranks - the points assigned must be lower for lower ranked trips.

The third section of the questionnaire asked respondents how likely car and transit were to take the attribute values listed in the scenarios. The fourth, and final, section of the questionnaire collected respondent demographics.

340 I. Fenwick ef al. / From car IO public transit

5. Results

The total sample achieved nearly equal representation of men and women and a spread of age groups. 90% of respondents lived in households owning a car; 80% lived within ! of a mile of a transit stop,

and 75% lived more than 2 miles from their place of work. Compared to car commuters, transit users were more likely to be

female, younger, less educated, lower income, and less likely to have professional/managerial occupations. Transit users lived closer to the office and closer to transit stops. Transit users were less likely to live in households which owned a car, and much less likely to have a car for their personal use. To an extent, the demographics of transit usage are demographics of low car availability. However, the car may be de- liberately foregone because of the transit system, rather than transit used because of the non-availability of the car. Tabulations showed that transit drew significant patronage from all demographic groups, except those where household income exceeded $40,000 a year. Car users were found to be quite familiar with the transit system, which is consistent with the high involvement choice model. Only 18% of car users had never used public transportation for travel to work.

The attitude data consisted of twenty items, each asked once in the context of car use and once in the context of transit use. For example, respondents rated both “Transit is there when I want it” and “The car is there when I want it”. Ratings can be analyzed in two ways: items concerning the two modes can be compared, or responses of transit and car users can be compared.

As expected, transit users consistently rated transit more favorably than car users rated transit. Similarly, car users consistently rated car more favorably than transit users rated car. This is a normal attitude rating phenomenon, reflecting differential perceptions, halo effects, or a combination of both. For car versus transit ratings, both car and transit users generally gave the car more favorable ratings than transit. The only exceptions were four items concerned with cost and safety, for which transit obtained the higher ratings; and the item “I would always choose . . . on a snowy day”, where transit users preferred transit and non-transit users preferred car.

Similar differences were obtained for the perceptions data shown in table 2. For the quality of service attributes used, car was perceived as superior to transit and transit users had a more favorable impression of

I. Fenwick et al. / From car to public transit 341

Table 2

Perception of transportation modes a: “ How likely is a trip to work (on public transit) (by car) “.

Perception of

transit

Car Transit

users users

Perception of

car

Car Transit

users users

- To cost % 10 per week 2.5 2.8 2.1 2.9

- To cost $25 per week 3.5 3.1

- To have plenty of space for you to sit 3.5 2.9 1.0 1.3

- To be 15 minutes faster than you current trip 4.8 3.9 2.1 1.7

- To require a 10 minute wait 2.0 2.5 4.4 4.2

- To need a 3 b&k walk at one end 2.4 3.1 4.7 4.6

- To always get you to work right on schedule 3.5 2.6 1.5 1.9

a Shows mean scores on a 5 point scale where “very likely” = 1 and “not at all likely” = 5.

transit than did car users. Thus there were differences in the way the two modes of commuting were perceived, as well as individual dif- ferences in these perceptions.

5.1. Conjoint analysis

The conjoint analysis used a monotonic regression procedure to fit a model relating individual’s rankings of travel scenarios to the character- istics of each scenario. The model fitted was:

where: Ri, is the ranking individual i gives to travel scenario m; X l-6 are dummy variables indicating the level of each of the 6 trip

attributes (journey cost, comfort, journey time, etc.) which scenario m contains;

4-6 are the regression coefficients to be estimated. Monotonic regression uses an iterative procedure. A regression is

performed using the ordinary least squares method; estimated, or fitted, values for the rankings R are obtained; these are then adjusted to be monotonic with respondent’s actual rankings; the ordinary least squares regression is then repeated using these new adjusted rankings. The process ends when fit ceases to improve. This procedure effectively

342 I. Fenwick et al. / From car to public transit

treats the dependent variable as ordinally scaled in contrast to the conventional regression approach which demands interval scaled data.

The regression coefficients show the contribution of each journey attribute, at each level, to the overall evaluation of the trip. These coefficients are known as ‘utilities’ or ‘part-worths’. From the model above it can be seen that the overall evaluation of a trip is the sum of the part-worths of all the attributes which that trip contains.

The conjoint analysis model can be fitted on an individual basis (estimating separate utilities for each respondent), or by pooling data over any subset of respondents (estimating utilities for entire subsets or segments of the market). In this case we first estimated models for three subsets: transit users, car users, and ‘dual’ users (those whose commut- ing journey involved car and transit). An individual level analysis is discussed below as an input to micro-modelling.

Table 3 shows the utility values obtained for the three user cate- gories. For all three groups, price is the most important trip characteris- tic, and is of special concern to dual users. Proximity is the second most important feature; journey time and punctuality are less important. Dual users give considerably lower utility to comfort and slightly lower

Table 3 Conjoint utilities by user classification.

Attribute: level Car users

Public transit

users

Users of both car and public transit

Price: $10 Proximity: door to

door

Journey time: 15 mins.

shorter than current trip

Reliability of service: always on schedule

Waiting time: 2 min. wait

Comfort: plenty of room to sit

0.40 B 0.39 0.52

0.23 0.20 0.22

0.04 0.06 0.05

0.03 0.09 0.06

0.13 0.12 0.09

0.17 0.14 0.06

’ In every case the value of the less-preferred level of the attribute is set to zero, so the utility for car users of a $10 as opposed to a $25 trip price is 0.40; for door-to-door service as opposed to a 3 block walk utility is 0.23; etc. Within each user group utilities are normalized to add to 1.0.

I. Fenwick et al. / From car to public transit 343

utility to waiting time than do the other groups: dual users seem willing to put up with a lot to avoid price increases.

6. Micro-modelling

A major advantage of the type of data collected here is the ability to develop models of consumer choice on an individual, or micro, basis. The conjoint analysis model can be estimated for each individual, producing a separate set of utilities for every respondent. We also know how likely each respondent feels the travel modes are to possess each of the trip attributes. By combining this data we can estimate each respondent’s overall evaluation of the existing travel modes. All we need to do is to plug each respondent’s estimated utilities and reported mode perceptions into the preference equation above (eq. 1). The result is an estimate of each respondent’s overall utility for each transpor- tation mode. We can now perform conceptual experiments by varying mode perceptions and assessing the effect on respondents’ overall mode evaluations. We can identify which respondents are most affected, and which are likely to switch modes.

First, each respondent’s overall evaluation of car and transit were estimated as outlined above. If the preference model is correct, and if respondents’ perceptions and utilities have been correctly determined, we should find that most respondents actually use the mode of trans- port for which they have they higher evaluation.

In fact 66% of all respondents were correctly classified, i.e. they used the mode of transportation for which their estimated utility was higher. This is considerably above the classification rate to be expected by chance alone (50%). Mis-classified respondents were particularly likely either to have no car available (although their preference score for car was high), or to require a car during their working day (although their preference when commuting was for transit).

As an example of using micro-modelling to investigate potential policy changes, we next manipulated the service and punctuality per- ceptions of transit. This service improvement simulation was developed to deal with a particular problem at the research site. The office complex was situated on an extensive block of land such that offices were at least two blocks from the nearest transit stop. If the transit authority allowed buses to loop through the site itself accessibility could be substantially improved.

344 I. Fenwick et al. / From car to public transit

To model the potential effects of this service change, respondent perceptions of proximity to transit were uniformly improved. Overall evaluations of transit were re-calculated and 27% of car-users were now found to have transit evaluations that exceeded their car evaluations. This represents a potential to switch modes. Clearly, simply changing bus routing will not cause these individuals to use transit. It is necessary for the new level of convenience to be perceived, and to be perceived as permanent. However, it does appear that this simple change has a high potential to attract new traffic.

Changing punctuality perceptions of transit was investigated in a similar way. The perceptions of transit punctuality were increased to equal (on average) those of the car. Overall mode evaluations were re-calculated. Not a single car user was found that might switch to transit: car evaluations remained above transit evaluations. It is not worthwhile to improve perceived punctuality of transit.

7. Conclusions

Estimating consumer response to transit promotion, or changes in transit services is fraught with difficulties. Experimental and/or econo- metric studies are hampered by the short term rigidities in peoples’ travel patterns and the many changes in the environment occurring over longer periods. Surveys with analysis at the aggregate level are difficult to use because in addition to the normal individual variance, the product itself is in fact delivered in a highly variable way to different consumers. Transit research requires individual level procedures.

In micro-modelling, the utility analysis perspective has much to offer. The method was illustrated in a field setting, with applications to transit planning. The results demonstrated that in this instance users and non-users of transit placed similar values on travel to work attributes. But the two groups differed substantially on their percep- tions and attitudes towards transit and car. Overall, the only factors for which transit was preferred over car were cost and safety. The latter item is perhaps insufficiently stressed in transit promotions.

Micro-modelling allows the. potential effect of changes in transit policy to be assessed quickly and in detail. We can analyze the reactions of specific market sub-groups and determine the best means of appeal- ing to particular segments. This gives a flexibility unavailable from any other analysis method.

I. Fenwick et al. / From car to public transit 345

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