An increasing number of transit agencies around the world now use the smart card as a system of perception. Several countries already use the smart card for many purposes, as a transit ticket and/or as a credit card protection mechanism. The smart card is now reliable enough to replace old perception systems. It is both beneficial for the user and for the owner at a revenue control level. The smart card allows, in particular, data processing, data analysis and data exploitation, considerably increasing the sum of information owned by the transit agency. The main goal of the project is to study the potential of commercial partnership that could enhance the commercialization of the smart card, by using the origin-destination survey data to demonstrate the potential of the smart card.;The second goal of this project is to present an exhaustive state of the art covering the potential uses of collected data from smart card technology, as well as different marketing measures taken around the world to promote the use of the smart card. This state of the art is also covering the technology around the smart card and the question about to the privacy concerns. This section will also present the progress made within the field of research on the exploitation of origin-destination survey's data during the last years. This state of the art is presented in chapter 2.;The third chapter applies the methodology which concretely is the meeting step through the project's execution. Firstly, the tools and data that were used will be described. Then, every step will be explained to demonstrate the proof of concept of the work. Thus, the customer analysis will identify the main destination spot for all customers' group. The results indicate that transit users move principally near subway station or downtown. We observed that people travelling for work are moving mainly downtown and those travelling for shopping or for a hobby are moving towards commercial areas. The descriptive characteristics of customers' group are similar, so that the proposed group doesn't have a proper or a strong identity. Characteristics will have a greater influence on the visited place than the kind of users. Nevertheless, it is possible to identify some tendencies. These will be presented in chapter 4. With the help of a commercial database, and by the analysis of the presence of commerce in the hot spot, it is possible to identify some commerce that presented a good potential for commercial partnership. A more precise analysis will target some specific businesses to study their representations on the Montreal territory. In fact, we observed that coffee houses present a good potential for commercial partnership because their number and repartition on the territory can accommodate many customers. The Tim Horton's restaurant chain is especially interesting. In fact, we count more than 188 addresses on the Montreal territory, of which 71 are situated within hot spots. Other coffee houses, like Second Cup or Al Van Houtte also have a good potential for commercial partnership having respectively 41 and 39 addresses in Montreal.;Finally, research proposals have been submitted to assess the methodological contributions of this thesis regarding mass transit planning. By offering information on the most visited places, new areas for analysis have been opened for mass transit planners. Marketing scenarios can now be simulated to assess every option. Moreover, it can be of interest to assess economic gains from different fare policy structures. (Abstract shortened by UMI.);Two steps are necessary to reach this goal. First, we need to characterize the transit customer and second, we need to characterize the most attractive place for users. The methodology is achieved by the development of two Excel spreadsheets, expressly designed. The first one is to evaluate if the socio-demographic characteristics are decisive for different users' group. Thus, customers groups are proposed to evaluate if these characteristics influence users' needs and travel habits. Also, it is possible to detect hot spots for a certain concentration of destination by customer type on a map of Montreal. The hot spots are generated by Crimestat, a spatial statistics package, and visualized by a geographic information system (GIS) like fGIS. The second spreadsheet evaluates the customer's exposition to different kind of commerce. This spreadsheet has been created to evaluate the repartition of different commerce in customer's group hot spots or to identify the characteristics of the customers who travel near a certain kind of commerce.