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Since several decades, French urbanisation evolves in a way that can hardly be seen favourable to public transportation. Innovation is needed in this field, if the individual automobile is to be faced on its own territories. This need for innovation concerns the miscellaneous stages of the public transport production, including the preliminary analysis ones. Indeed, many work remains to be done to understand mobility behaviour, what is more if we are to propose versatile and viable alternatives to current trends.
Geomarketing based surveys, combining revealed and stated preferences, are a powerful way of acquiring such an accurate information. From a qualitative point of view, this kind of information allows to calibrate public transport services adapted as much as possible to the targeted customers. However, the major problem of the spatial generalisation of this individual information remains often unsolved.
In this context, a strategy is proposed, based on the combination of miscellaneous methods. The spread of the individual automobile within the whole society explains much of this trend, which has major spatial impacts. This diffused and often spontaneous urbanisation, based on individual strategies, leads to geomarkwting dilemmas, including mobility related ones.
As Torrens and Alberti, state: Indeed, the rate of daily journeys to work decreases, while the rate of leisure trips augments.
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This development has to be connected with the changing way of life, and will probably be amplified by the decrease of working time. A direct consequence of this evolution is that polarised flows towards towns and town centres are not as much representative as before of the spatial pattern of daily flows, as fringe flows take more and more importance. It cannot remain on its traditional target, that is daily journeys to work during rush hours, within densely populated areas of towns.
Nevertheless, the will to compete with the individual automobile, what is more on its own territories, implies to take up many challenges.
In our opinion, versatile solutions can greatly contribute to pick up the gauntlet. This quest for versatility in public transport services is closely related to a major marketing shift, based on the henceforth well known pair of market segmentation and product differentiation.
Each group, or “segment”, can be targeted by a different geomarketkng mix because the segments are created to minimise inherent differences between respondents within each segment and maximise differences between each segment.
Once a “niche marketing” is identified, any personalised solution can be consequently imaged and tested, leading to a better use of the service proposed. In particular, the main problem of the spatial generalisation of this individual information remains often unsolved. The aim of this article is to present an example of a practical strategy set up in a context of operational research, that led to the creation of a personalised public transport service. First, the survey strategy retained will be presented.
Then, a procedure dedicated to the production of market maps, through spatial generalisation, will be exposed. Nevertheless, since the sixties, the point of view favoured changed much lobros, ].
Veomarketing preferences surveys, which come from investigations led in the early seventies in the market research field [Green and al. Anyway, the early adaptations of Davidson  and Louviere and his colleagues  to this field had a weak impact at that time. The dominant paradigm of discrete choice modelling, mostly based on revealed preferences data, was then firmly anchored. In other words, it is not possible to conduct “controlled” experiments or laboratory tests to ascertain how travellers would behave under geomarketiny situations.
Consequently, demand models are constructed on the basis off observations of traveller behaviour under existing goemarketing supply conditions ” [Kanafani,pp. Nevertheless, three main criticisms can be addressed to this seeming “objective” approach. First, mobility behaviours highlighted this way are by definition limited to the conditions observed during the survey, and can hardly be extrapolated to conditions beyond the observations. In other words, revealed preferences tell us little about the modification of mobility behaviours possibly induced by the hypothetical introduction of a new alternative.
Finally, the reputed reliability of data obtained this way, based on the apparent direct apprehension of an objective reality, can be misleading. We can hardly be satisfied with the simple description of a behaviour by its author, given the extreme variability of individual perceptions at work, especially in the spatial and temporal dimensions. Indeed, people are asked to give their opinion about a conjectural linros realistic transport scenario.
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This kind of survey is thus well adjusted when we need to appreciate the market for a new transport alternative, for which we have no previous experience. The basic idea is incontestably simple, as John Paterson summed it up in a humorous allocution: Nevertheless, beyond this fact, two more remarks can be formulated. First, stated preferences surveys do not reflect actual travel behaviours. More, the idea is to conceive strategies based not on the way people would behave in a given context, but rather on how they say they would act.
Second, during the survey, respondents have to be presented with much more information than they would have in making their travel decisions. This drawback mainly implies that models developed using stated preference data do not reflect the uncertainty travellers experience in reality.
Indeed, many bias can occur during stated preference data collection, amongst which we can cite, following [Bradley and Kroes,pp. There is a strong commercial component here, that cannot be avoided. More, it seems that the product concept has to be “sold” to the respondent from the very first contact.
The alternative proposed should then be firmly engaged, from a political ggeomarketing commercial point of view.
The survey can therefore be seen as a commercial lever, as well as a reliable way of getting precious data. This process, we called “caused preferences surveys”, can finally be justified by the following arguments: However, obtaining such a complete chain of information is far from being obvious and implies adapted survey strategy.
Railway stations have then become major flow generators, covering schedules seldom served by public transport services early in the morning and late in geomagketing evening. Therefore, a strong co-operation of the train company is needed, as well as a questionnaire adapted to this time and context. First, the knowledge of mobility behaviours of a given subset of individuals is essential, but not sufficient, especially when studying the introduction of a new service.
A visual information is thought to help accelerating this involvement process. Third, the service proposed might be as realistic as possible. More, it has to be adapted to the actual experiences of the respondent. Fourth, revealed and caused preferences may be combined. In a first step, revealed preferences were collected to the maximum number of people, using a self-filled questionnaire.
The figure 1 sums up this procedure. First, it was seen as an aid to calibrate the upcoming service, mainly regarding parameters such as waiting time, travel time, arrival time and price. To achieve this goal, geomarjeting scenarii were proposed, in a strategy exploiting both “ranking-based” et “rating-based” responses.
Three scenarii were proposed, combining three attributes related to the comfort of the trip, its timing and its price Figure 2. This adaptive procedure helped us providing a virtual service as se as geomarketung. Indeed, investigators were allowed to stay only one hour aboard, and a maximum of five days of inquiry was accorded by the SNCF, for a limited number of lobros. Therefore, an “as-much-as-possible” strategy was adopted, leading to convenience samples.
The revealed preferences questionnaire was distributed systematically to each traveller, while the caused preferences one was addressed to the maximum number of people during the available time. Four hundred and seventy six self-filled questionnaires were filled and one hundred and twelve interviews were led this way, providing a rich data base.
Furthermore, as individuals gave us their personal address when filling the self-filled questionnaire, a very accurate spatial information was constituted.
It was indeed our belief that we needed very accurate but versatile insights from this geomarketiny amount of data. Then, an interactive GIS prototype has been implemented geomarkteing the statistical programming environment Xlisp-Stat [Tierney, geomarmeting, so as to achieve this goal. By dynamically linking several figures, we are indeed able to see how the phenomenon they represent interact, and to lead multidimensional visual exploration of the underlying data set.
Geomarketing: Methods and Strategies in Spatial Marketing – Google Libros
But this is the case only for a hundred people. And for nearly four hundred of them, individual characteristics and revealed preferences are only known.
Then, the question is: Best results are obtained through the procedure shown by figure 6. Their rough position on the scale of use is sufficient, and two groups can indeed be formed, leading to a binary variable.
Then, individual characteristics and revealed preferences are transformed into quantitative variables, using the Disqual procedure [Saporta,]. Geomarkeging multiple correspondence analysis, factorial co-ordinates are calculated, leading to a reduction of the initial dataset.
Finally, the problem can be seen as a discrimination one: This relation is underlined using a Lowess [Cleveland, ], enhanced by geomarekting bootstrap confidence band lbiros and Tibshirani, ]. They propose then to replace the linear regression fits by far more flexible non-parametric fits, in the spirit of the generalised additive model [Buja, Hastie and Tibshirani, ].
These smooth functions are not defined a priori by the user, as would be the case for example with quadratic discriminant analysis, but during the calibration process, using iterative algorithms. Flexible discriminant analysis was then used here, using the “MDA” code provided by the authors for the R package. Cubic spline functions were used during the calibration process, allowing us to handle the complex relationships showed before.
A benchmarking procedure was then adopted, so as to compare the performance of these various models. As figure 8 show, the computation burden involved by the two non-parametric solutions is worth while, as the classification performance of the procedure is clearly increased.
What we get at the end of this process is an individual probability of belonging to the “good” group that is group with score above 5for nearly five hundred people and not only one hundred of them. From this point, the problem of spatial generalisation can then be handled. We need more than an estimation of the size of the market for our service: Unfortunately, the information we have is quite poor.
The number of people that could possibly take the service while going to the TGV station, for a given period and a given census unit, can indeed be estimated by the population of this unit multiplied by an average probability of adopting the service: This can be achieved using a weighted mean for example: Of course, this basic model can be desegregated according to the available census data, using indicators such as age, sex or profession.
Nevertheless, such an improvement would imply a more consistent sample. For the moment, the solutions implemented, based on very simple statistical principles, lead to very poor results. Indeed, it sometimes seems quite unreal to propose collective solutions to individual behaviours, due to their complexity and diversity. Nevertheless, the viability of such an individual public service implies an accurate knowledge of travel behaviours in a given area, for a given target population, and their connection with the underlying spatial patterns.
Geomarketing based surveys, combining revealed and modified stated preferences, called “caused preferences”, are a powerful way of acquiring such an accurate information. In this context, a strategy was proposed, based on the combination of miscellaneous methods.