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Amandine Chevalier : « Mobility choices and climate change : assessing the effects of social norms and economic incentives through discrete choice experiments” (avec Charles Raux et Emmanuel Bougna) Catherine Bouteiller : « Principes de simulation tarifaire sur la future ligne 15 du Grand Paris à partir des données de validation de la billettique »

Résumés ci-dessous : Amandine Chevalier « Mobility choices and climate change : assessing the effects of social norms and economic incentives through discrete choice experiments”

The potential of psychological and fiscal incentives in motivating environmentally responsible behavior in a context of long distance leisure travel is explored thanks to a series of controlled experiments on 900 participants. Framing effects like information on CO2 emissions, injunctive and descriptive norms, in combination with fiscal incentives such as a carbon tax, a bonus-malus or a carbon trading scheme are tested. Providing CO2 information on emissions is highly effective and the injunctive norm reinforces this effect in the case of air and train. A quota scheme reinforces the injunctive norm effect in the case of these two modes. More strikingly, the amount of the financial sanction or reward has no effect on the probability of using the various travel modes, unlike the presence of the fiscal framing itself. These results reinforce the case for using psychologically framing effects, in association or not with fiscal instruments, in promoting effective pro-environmental behavior in transport choices.

Catherine Bouteiller « Principes de simulation tarifaire sur la future ligne 15 du Grand Paris à partir des données de validation de la billettique »

In public transport, traditional magnetic cards or tickets have been replaced by smart cards very progressively since the 70’s and is nearly achieved in the main cities. Advantages of using smart cards are well known and commonly accepted by Public transport Authority’s strategy As mentioned by Pelletier and al 2009 “the smart card improves the quality of the data, gives transit a more modern look, and provides new opportunities for innovative and flexible fare structuring” Smart card systems collect day to day variability of user behaviors at a very detailed spatial and temporal resolution (Trépanier et al 2009). A very accurate knowledge of the current behaviors is necessary to deliver the better service at the best time and in a sustainable way. When introducing a new line with real performance in terms of reduction of trips length, users have a real alternative of choice between their usual route and the new one. There are several advantages in taking the new route : gain of time spent during the trip, gains in terms of number of transfer, gain of comfort, gain of security, price advantage. Our research consists in establishing a pricing simulation technique using existing smartcard data historic. This research proposes to find a method to assess what would be the appropriate pricing to optimize trips and social welfare when a new structuring line is built and added to an existing network. It proposes to use fare management systems data collected whenever users of public transport tap in with their smart card. For this research, we have been able to use data from Public Transport pass users in Paris Metropolitan area for one month. These data represent 2.9 millions of users for Adult Pass and Student pass that are completely anonymous. We also used all information available on the new orbital line of Grand Paris that will be launched in 2020. The details of line 15 are already available in open data : stations localizations and connections with the existing metro and RER network, inter station distances, commercial speed of train. Our method consists in using a generalized cost function and a logit model for affecting patronage on all OD trips collected by the fare management data base that will be potentially transferred on the new orbital metro of Grand Paris. We were able to select OD transferable using a Geographical informative system visualization tool . The cost function used, takes into account price, time and value of time for each social segment. As we disaggregated all usages, we were able to use value of time for each social profile : workers, students, seniors. The research focused on peak hour’s trips. As a result we defined a function of fare for the new line that can be optimized taking into account all existing origin and destination trips for all social profiles under capacity constraints. In our results we were able to fix a modal shift target, for example 85% overall shift to the new line and define the total increase of revenue for the whole network (+5%) with a specific grid taking in count the range of distance travelled, the social profile and time of the day. Our results should be refined to take into account all Origin and Destinations recorded, all profile and all and peak and off peak period as well. Big data treatment will be necessary to achieve our work