Researching and Modeling Consumer Choice Behavior in Urban Transportation

Consumer behavior theory and modeling approaches can help improve managerial understanding of how consumers choose between alternative transportation modes for urban travel. This paper develops a microanalytic model of modal choice in flowchart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need, by first specifying the characteristics of the trip itself, and then specifying the "ideal" modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this "ideal" solution and the consumer is posited to select that mode which provides the best match. The model explicitly recognized the impact of psychological variables on modal choice as well as the consumer's need for information.


Christopher H. Lovelock (1975) ,"Researching and Modeling Consumer Choice Behavior in Urban Transportation", in NA - Advances in Consumer Research Volume 02, eds. Mary Jane Schlinger, Ann Abor, MI : Association for Consumer Research, Pages: 851-862.

Advances in Consumer Research Volume 2, 1975      Pages 851-862


Christopher H. Lovelock, Harvard University, Graduate School of Business Administration

Consumer behavior theory and modeling approaches can help improve managerial understanding of how consumers choose between alternative transportation modes for urban travel. This paper develops a microanalytic model of modal choice in flowchart form, clarifying the stages in the modal choice decision process for any given trip. Individual consumers are seen as trying to satisfy a particular travel need, by first specifying the characteristics of the trip itself, and then specifying the "ideal" modal attributes required for this trip. Next, the perceived characteristics of a limited number of modes are evaluated against this "ideal" solution and the consumer is posited to select that mode which provides the best match. The model explicitly recognized the impact of psychological variables on modal choice as well as the consumer's need for information.


Recent years have brought a growing recognition of the problems raised by excessive use of automobiles as a mode of travel in urban areas. At the present time, the United States is entering what has been described by some as a "renaissance era" in public transport, spurred by concerns over pollution and congestion, urban renewal plans, opposition to new freeways and, most recently, by the energy crisis. New and improved bus and rail services are being speedily developed in numerous American cities, both large and small, assisted by major investments of public funds and accompanied by a public takeover of many privately awned operations.

The overall result has been a growing involvement by the public sector in the field of urban mass transit, with the objective of reviving dying systems and of developing new ones. But it is not enough to invest money in public transportation. The real challenge is a marketing one: how do we persuade people to use it? Transit managers simply cannot afford to lose sight of the fact that unless public transit is well patronized, many of the benefits claimed for it will never be achieved.

A brief description of the tasks facing transit planners and managers will suffice to indicate why an understanding of consumer behavior is so important in this field of activity. At the outset, it is essential that transit services be designed (or redesigned) to appeal to those who account for the bulk of car travel in urban areas, while also meeting the needs of so-called "captive" riders who either have no car or cannot drive. This task requires obtaining information on what characteristics consumers would ideally like to find in a transportation mode for various types of journeys, and then developing new or improved transit services which come as close as possible to meeting this ideal. Subsequently, the resulting services must be marketed in such a way that consumers become aware of them, perceive them as competitive with the automobile--if they have one--and are actually persuaded to use transit. Where car owners are concerned, transit usage may require significant changes in established behavior patterns and even in lifestyles.

In order to bring about such a behavioral change, transit marketers must learn more about the criteria which people employ in making decisions relating to travel modes, as well as the relative importance of these criteria. Management will also need to know how individuals set about making decisions concerning modal choice, what information they use in making such decisions, and how they obtain it. Finally, transit managers must find the most effective means of communicating with different segments of the market for new transit services together with the most appropriate appeals to employ in seeking to change the behavior of consumers in these various segments.


Over the years, a considerable volume of consumer-oriented research has been conducted on the topic of urban transportation. However, on closer analysis, it transpires that much of this has only limited usefulness for developing transit marketing strategies. Past transportation research may be categorized into three groups:

1. Origin and Destination Studies

2. Mode Choice Models

3. Research into Modal Attributes

Origin-Destination studies are part of the standard stock in trade of the transportation planner. As the name suggests, their primary purpose is to identify the extent of travel taking place between any two or more geographic points. In their simplest form, travelers are surveyed at selected cordon points and asked where they have come from and where they are going. A note is also made of the time of the interview and the mode in which the respondent is traveling. Recently, such "O & D" studies have become a little more sophisticated, with the questions or interviewer's notations including information on the consumer's frequency of travel on specific journeys and some salient demographic characteristics. In this way, a consumer profile can be built up which may be very useful for market segmentation purposes. Repeated applications of the same survey instrument at periodic intervals can show changes in travel behavior over time, possibly reflecting changes in transportation system characteristics (e.g. increased fares, development of new routes, etc.). However, O & D studies ignore attitudinal factors completely.

From the data collected by such studies, transportation researchers have developed a substantial number of quantitative models designed to forecast "modal split"--that is, the proportion of trips accounted for by each of the modes available in a given travel corridor. A huge number of Mode Choice Models have been developed and are described in review papers such as Bock (1968) and Hartgen (1970). Typically, such models contain a strictly limited number of independent variables. Many, for instance, were designed simply to demonstrate the impact on transit's share of the travel market of changes in transit fares. These models resulted in such managerially unhelpful findings as a conclusion that the only way to increase transit's market share in a major midwestern city was to have a negative fare for each passenger of 35 cents. The other widely used variable, often employed in conjunction with price, was travel time. The best of the economic models of medal choice were probably the utility models grounded in Lancaster's (1966) general theory of consumption. These models assumed that travel, as a derived demand, was a negatively valued utility and posited that consumers would select that mode of transportation which represented the least disutility. The disutility of travel by a given mode was defined as the sum of the disutilities associated with each modal attribute specifically included in the model. One significant problem with this utility model is that it assumes disutility to be a linear function of an attribute's magnitude, thus ignoring threshold levels of acceptability and diminishing (or increasing) marginal utility.

The third category, Research into Modal Attributes, clearly has a useful role to play in both transportation system design and model construction. The objectives of this type of research are, first, to determine haw consumers rank different attributes of transportation modes and second, to discover how well specific modes are perceived as performing on these desired characteristics. These studies gather data through the use of large-scale consumer questionnaires, and typically employ Likert-scaled items or paired comparisons to arrive at rankings of attributes. Several major research studies (Paine et al., 1967; McMillan and Assael, 1968, 1969; and Golob et al., 1972), employing varying methodologies and sample populations, have yielded reasonably consistent findings concerning the characteristics required by consumers in a transportation mode and the relative importance which they attach to these characteristics. In order of importance, the principal characteristics which consumers seek in a mode of transportation appear to be safety, reliability, time savings, cost, convenience and comfort. However, additional findings showing consumers to rate car travel higher than transit travel on virtually every attribute are open to question, in light of the fact that substantial numbers of people in urban areas with a choice of modes do elect to use public transportation, as well as on the grounds of possible bias in questionnaire phrasing and inclusion of non-representative consumers in the sample population (Lovelock, 1972).


From the foregoing discussion, it becomes clear that existing transportation research and modeling techniques overlook many considerations and concepts which are widely -used by marketers. For a start, modal choice models ignore the potential impact of several key elements in the marketing mix which may influence consumer behavior. Although the issues of product characteristics and price levels are addressed, the approaches employed suffer from significant shortcomings, not least in the use of a purely economic treatment of pricing strategy. Completely missing is any recognition of the role which promotion can play in influencing modal choice.

A number of transportation researchers have pointed to the limited explanatory power of models based upon economic analysis and have emphasized the need to develop a better understanding of the ways in which consumers arrive at modal choice decisions (Sommers, 1970; Le Boulanger, 1971; Horton, 1972). In particular, growing interest is being shown in learning more about how behavioral variables such as attitudes relate to modal choice (Hartgen and Tanner, 1971; Allen and Isserman, 1972).

Yet another shortcoming of previous modeling approaches is that most modal choice research has tended to be macroanalytic in nature (i.e. focusing on the behavior of large groups of travelers) and relatively little is known about the individual consumer's decision process or how his decisions might be influenced by transportation managers. In the balance of this paper I propose to outline a marketing-oriented approach to modeling modal choice, derived from research into brand choice for consumer convenience products.


Marketing researchers have, of course, devoted considerable attention to development of models of consumer behavior. The greatest emphasis in modeling consumer choice has been given to convenience products, notably branded food items. I believe that certain of the approaches employed in modeling the brand choice decision process can usefully be adapted to decisions involving frequent selection of other types of consumer products or services, including urban transportation alternatives. A particular advantage of consumer behavior modeling, from the perspective of transportation research, is that such models often explicitly incorporate social-psychological variables, enabling one to examine the role of attitudes, perceptions, etc., in the modal choice decision process.

As noted earlier, most economic models of modal choice have been macroanalytic in nature. This focus on the behavior of large groups of consumers severely detracts from the model's ability to explain behavior and thereby provide insights into ways of changing existing behavior patterns. The focus here, therefore, will be on a microanalytic approach, emphasizing the decision process of individual consumers in selecting a mode for a particular trip.

Microanalytic simulation is a relatively new approach to modeling consumer behavior, embodying both economic and behavioral concepts. As noted by Eskin and Venkatesan (1974), these models describe the behavior of individual decision makers rather than group or market level decision processes. They also involve a specification of causal structure which seeks to explain who various decisions are made, rather than just claiming ability to forecast outcomes given knowledge of environmental conditions. A key characteristic of such models is that they simulate the actual decision-making process. The procedure used for generating forecasts and searching for optima is to run the model, as opposed to the more conventional process of solving models by analytical methods. This simulation approach is generally necessitated by the complex and interactive nature of the model's structure.

Consumer decisions are defined in terms of observable behavior, such as choice of a product or brand. Typically, these decisions are determined by some form of attitude construct, based upon consumer preference for a particular product. This preference is derived from the consumer's perception of the benefits to be derived from consumption or use of the product in a specific situation, and presumably reflects the match between his/her specific needs and the perceived attributes of each of the products or brands under evaluation.

The process of model development can be broken into two stages: description of the consumer's decision process and then quantification of the inputs. In its descriptive format, the model can be expressed as a flow chart of the decision process; this shows the various steps involved in making a decision, identifies the alternatives available and specifies the interactive nature of the model's structure. When quantified and operationalized, microanalytic simulation motels typically exist in the form of a computer code, capable of producing numerical outputs such as forecasts of the number of persons purchasing specified brands under given conditions. Usually, these models are stochastic in nature, since uncertainty components play an important role in constructing the model. The characteristics ant behavior of consumers, as well as the environmental conditions facing them, are generated within the model through probabilistic processes, based upon data derived from market research and managerial judgement.

Eskin and Venkatesan went on to demonstrate how a simple microanalytic simulation was successfully developed to illustrate the consumer decision flow involved in selecting a particular brand of cake mix from among competing alternatives, and subsequently operationalized to enable management to evaluate the impact of alternative market events and strategies.

This cake-mix model is described in greater detail in Eskin and Lovelock (1971, 1972). In this model, consumers are seen as first defining the occasion at which the resulting cake will be served, thus determining some of the attributes which they will seek in the product. The model limits these attributes to four key dimensions, two of which--price and quality--are treated as interrelated. Each consumer has her own "ideal" product in mind, based on the serving occasion for which it is intended and her personal preferences. Likewise, she also forms a perception of the characteristics of each available alternative, which is open to modification through promotional activity. Alternative brands are then matched against the "ideal" and it is hypothesized that she will purchase that brand which best meets or exceeds her requirements. The model provides for ties between brands and also posits that if no brand matches her ideal, the consumer will either change her requirements or else not purchase at all.


The same basic concepts underlying the cake mix model are used here to develop a behavioral model of modal choice which, I believe, provides fresh insights to the problem of marketing urban transit. The model has its antecedents in the consumer behavior simulation models developed by Amstutz (1967) and Claycamp and Amstutz (1968), as well as drawing on work by Engel, Kollat and Blackwell (1968).

Translating a model designed to study brand choice into one designed to study choice of transportation modes is simpler than might first appear. The "product classes" in this instance are represented by trips for different purposes (e.g. work and non-work trips) for which, as noted by Paine et al. (1967), consumers tend to have differing choice criteria. The "brands," meantime, are represented in this scheme of things by the alternative public and private modes available to the consumer. Choice of a transportation mode shares a very significant characteristic with the selection of branded food products, namely the frequency with which the choice has, theoretically, to be made. This does not mean, of course, that a different decision will be reached each time a journey is made, nor even that the consumer will necessarily think very hard about the decision, which may well have become routinized. But then, consumer loyalty and habitual purchase of an established brand can be a problem, too, for many convenience goods marketers seeking to enlarge their market share in the face of entrenched competition.

The focus here is on the first stage of model development, namely a description in flow chart form of the decision process through which the consumer is posited to go in choosing a mode of transportation for a particular journey. However, I shall also discuss some of the issues related to quantification of inputs. In this model, displayed in Exhibit 1, individual consumers will be seen as trying to satisfy a particular travel need by first evaluating alternative modes of transportation, then matching the perceived characteristics of available modes against an "ideal" solution, and finally selecting that mode which provides the best match (if such exists).



Decide To Make Trip

Transportation is, of course, a derived demand, and so the modal choice decision in the model is preceded by the consumer's decision to make a journey which will satisfy other needs. Each trip has specific characteristics of its own which will have a major impact on the consumer's choice of a mode of transportation. Consequently, the model begins by calling for the consumer to specify the characteristics of the trip, notably the origin, the destination and the purpose of the trip; other important considerations might include the number and ages of others (if any) in his party, the time at which the trip was to be made, the prevailing weather conditions, etc. In its simplest form, this component of the model might consist of three categories, not dissimilar to the "Select Use Category" in the cake mix model:

- Work trip

- Shopping trip

- Social/Recreational trip

Form perceived Need

Having specified these trip characteristics, the consumer next moves to formulate his perceived needs and specify the modal attributes required for this triP. They constitute an "ideal" solution to his transportation needs-the choice criteria against which he or she will subsequently evaluate the various alternatives which may be available. On the basis of previous transportation research, the key attributes to include would seem to be:

- Point-to-point travel time

- Price

- Safety

- Reliability

- Convenience

- Comfort

Additional market research will be needed to develop appropriate indices of what might be either grouped quantitative data (e.g. price in cents per person, travel time in minutes) or verbalized ratings of the other characteristics. One advantage of using grouped data in this way is that it gets amount the problem of purely linear functions: thus, price could be expressed as 10-144, 15-244, 25-294, 30-404, 50-744, etc., reflecting varying demand elasticities at different price levels.

Although a review of transportation research findings suggests that consumers are willing to trade off modal attributes against one another (e.g. they will pay a premium fare for a faster or more comfortable service), my own research suggests that there are certain threshold levels of acceptability on specific attributes which may serve to disqualify the entire mode (Lovelock, 1972). Consequently, it is necessary to include in the coding not merely the consumer's "ideal" value for each attribute but also his "threshold" value. This might consist of a maximum acceptable price or travel time, a minimum level of comfort and safety, etc.

Form Perceptions of Alternative Modes

Now the consumer is ready to begin the search and evaluation process which will lead to a final decision on a choice of mode. The characteristics and performance of each mode can be described in the same terms as perceived need. Thus it can be given a rating in terms of travel time on a particular journey, a price level, a comfort rating and so forth What is important is that these are not objective ratings as determined by an outside expert, but the subjective perceptions of an individual consumer. As such, they may be far removed from reality, particularly if the consumer is poorly informed or has little experience of using the mode in question.

It is entirely possible that certain modes may be entirely absent from the evaluation process. The model makes explicit provision for this through what is termed the MODAL POOL. In this pool will be found all the alternatives which are perceived as offering a potential solution to the transportation problem raised by the trip in question (note the dotted line feeding information on the type of trip to the MODAL POOL). Each trip decision requires a new search of the pool, since the perceived alternatives are themselves a function of the trip characteristics. However, the pool does not necessarily include all feasible alternatives--only those which the consumer selects for evaluation. The selection process is a function of two factors, PERSONAL CHARACTERISTICS and STORED INFORMATION, as modified by VALUES AND ATTITUDES.

PERSONAL CHARACTERISTICS refer to demographic, physiological and personality factors: for instance, car ownership, stage in life cycle, state of health and underlying motivations may all have an important impact on the modes which people evaluate for a particular trip. STORED INFORMATION, representing past experience and awareness of the availability of alternative modes and routes, also constitutes an important determinant of the modes which are to be brought forward for further evaluation. Acting as a filter to the modal pool are VALUES AND ATTITUDES. Individuals may be perfectly well aware that there is a bus service available, but their generalized attitudes towards buses may be such that they will not even consider this mode as an alternative.

At this point, then, the consumer brings forward one or more selected modes from the MODAL POOL for further evaluation and forms perceptions of the characteristics of each.

Additional Information

The immediate question now is whether the consumer has sufficient information to make a decision. It may be that (s)he is thinking about taking a train; but doesn't know where the station is, what the schedules are or how much it costs. Perhaps (s)he is also considering driving and is unsure how long to allow for the trip or whether parking is available at the other end. If the consumer already has enough information and cannot be bothered to search for more (s)he can proceed straight to a decision. Alternatively, the consumer is posited to specify the additional information required.

This information search may or may not prove successful. Unfortunately, an unsuccessful outcome to the information search is particularly likely to result in the case of public transportation, since many transit companies have very poor information services or are understaffed, so that a phone call is often met by a continuous busy signal. If consumers elect not to search for additional information even when they need it, or if they search unsuccessfully, then they will proceed towards making a final decision without the benefit of full information with the accompanying risk that it may not be the best decision. However, obtaining the needed information permits consumers to update their knowledge and also record what has been learned under STORED INFORMATION for future reference.

The Matching Process

The consumer is now ready to compare the perceived characteristics of each of the alternative modes against the previously defined choice criteria for this trip. It is hypothesized that he or she will select that mote whose perceived attributes (i.e. cost, convenience, comfort, etc.) meet all threshold requirements ant perform best in matching (or exceeding) the attributes of the ideal, but allowing for trade-offs between different characteristics. However, there is no guarantee that a satisfactory match will occur, even when only one mode is under consideration for the trip. If a match results, one would expect the consumer to select a mode, make the trip and then update his knowledge on the basis of the experience. However, if no match results, two alternatives remain open--either not to make the trip at all or to change these perceived needs in the home of subsequently achieving a match.

A change in needs can have one of three outcomes. The first is represented by a decision to modify the ideal, so that the consumer is prepared (say) to accept a slower or more expensive trip, use a less comfortable vehicle, etc. Second, would-be travellers can modify the nature of the trip itself, so that it is effectively no longer the same trip--they can leave later, visit a different destination, not take children along with them, decide to forego one of the purposes of the trip, etc. The model provides a feedback loop between FORM PERCEIVED NEED and DECIDE TO MAKE TRIP which, it will be noted, re-establishes contact with the MODAL POOL. This action makes possible a third solution, namely another search of the pool and introduction of new modes into the modal decision process.


The value of this model is twofold. The flow chart alone serves to highlight the information we need to know about individuals and their behavior if we are to be able to understand and influence their modal choice decisions. It is hoped that further research will make it possible to quantify the model and use it also as a predictive tool.

A review of the flow chart suggests that we need, first, to know something about each individual, notably demographic characteristics (including, of course, vehicle ownership), physical condition, and personality. This information serves both as a guide to the type and number of trips (s)he is likely to make on any given day and also acts to define the constraints surrounding the modal choice decision process (e.g. a 14-year old cannot legally drive; a person in a non-car owning household is unlikely to have a car available as a possible alternative; a disabled person may not be able to use public transportation, etc.).

We also need to know what information is stored away in the consumer's mind concerning transportation alternatives--which is a function of past experience, exposure to advertising, word of mouth, etc. An understanding of this attitude and value structure can help us understand why one individual's decision process may be sharply different from that of another with broadly similar demographic characteristics. The concept of the MODAL POOL proposed in this model suggests that modal choice is a two-stage process, with the first stage representing a possibly subconscious filtering process by which only a restricted number of possible alternatives are advanced for conscious evaluation. In many instances, it is believed that only one mode, the private automobile, emerges from the pool, with the result that for repetitive journeys the entire modal choice process may be undertaken almost subconsciously.

The consumer's evaluation process focuses our attention on four considerations. The first is the development of an "ideal" modal solution to the trip in question, based upon the characteristics of that trip; this recognizes that a journey between the same origin and destination may not always constitute the same "trip" in the consumer's mind and helps to account for the fact that the same person may not always use the same mode each day for the same journey (Lovelock, 1972). The second point of interest is the need the consumer may have for additional information in order to be able to make a thorough evaluation. If a consumer recognizes such a need but considers that obtaining the information would represent too much of a "hassle," then the choice will be made on the basis of imperfect information and the decision may go, by default, in favor of the mode about which (s)he is already best informed. This has important implications for transit marketers, in that a car owner is likely to be much better informed about driving alternatives than about transit alternatives.

The third element of interest is the matching process itself, where the perceived characteristics of the competing modes are compared against those of the "ideal." Most existing modal choice research has emphasized modal characteristics as the key determinants in the modal choice decision process, but has used a very limited number of attributes. Such research has also tended to employ aggregative models which treat the characteristics of alternative modes as "facts," rather than recognizing differences in perceptions, and also to assume that the choice criteria (i.e. the ideal) remain constant for broadly defined types of trips. Inevitably, such models tend to regard the characteristics of alternative modes as fixed but largely unrelated physical attributes which can only be altered by making physical changes (e.g., introducing new vehicles, speeding up the service, reducing fares, etc.). By contrast, behavioral theory would argue that each individual can have different needs and perceptions and that it may be possible to change perceptions of key characteristics through persuasive communications. Alternatively, a physical change in one attribute (say putting new seats in a vehicle to improve comfort) might serve to influence consumer perceptions of other attributes through a "halo effect."

The fourth point of interest in the model is the updating of information which takes place at two different stages--following a successful information search and again following actual usage of a mode for a particular trip. This focuses attention firmly on the impact which awareness of alternatives and past experience can have on the modal decision process. Another input to updating knowledge (not shown in the model) would be represented by receipt of messages concerning transportation alternatives and their characteristics. In the case of public transportation, these might be expected to result from advertising campaigns or public information programs. Promotional campaigns might also attempt to change attitudes in order that transit would be included in the MODAL POOL as an alternative at least worthy of evaluation.


At this point, the next stage is to attempt to quantify the model. Initially, it will be advisable to develop a very simple model, tailored to a specific modal choice situation in a given location. Microanalytic models can quickly become immensely complex and expensive to run in terms of computer usage. One of the virtues of the cake-mix model described earlier is its simplicityCand yet it does seem to produce meaningful results. In quantifying and operationalizing the model, the following tasks will be necessary:

1. Select a few basic types of trip for a specific route

2. Select key modal attributes and develop appropriate indices for each

3. Identify key segmenting variables and the proportion of consumers in each segment

4. Specify "ideal" and "threshold" values for each attribute by each consumer segment and for each trip type

5. Specify different perceptions of each attribute by each consumer segment for each trip type and for each mode of transportation included in the model

Market research will be necessary to collect new data specifically tailored to the requirements of the motel. With the aid of such data, a "reallife" situation can be simulated ant sensitivity analysis conducted on the inputs to see what impact changes in either the ideal or the perceived attributes would have on modal choice behavior. Such changes could then be replicated under real-world conditions, through physical changes in transit attributes in a restricted location or promotional campaigns by the transit authority designed to alter perceptions regarded as non-representative of reality. Subsequently, the model could be validated by comparing the modal split predicted by the output with the known behavior of the consumer population being simulated By varying the inputs, it should be possible to fine-tune the model and thus improve its accuracy.


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Christopher H. Lovelock, Harvard University, Graduate School of Business Administration


NA - Advances in Consumer Research Volume 02 | 1975

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