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Offre de thèse "Shared mobility systems: Investigating the impact of pricing strategies on the acces

Forum 'Emplois' - Sujet créé le 11/05/2023 par unknown (919 vues)


Le 11/05/2023 par unknown :

Shared mobility systems:Investigating the impact of pricing strategies on the accessibility,the resilience and the footprint of transportation systems

Director: Prof. Ludovic Leclercq (ludovic.lerclercq@univ-eiffel.fr)

Co-Supervisor: Fayçal Touzout (faycal.touzout@univ-eiffel.fr)

Context:

According to the World Bank, domestic and international transport already contribute 20% of global GHGemissions [1]. And as the need for mobility grow, GHG emissions from transport could increase by as muchas 60% by 2050. In addition to the necessity of shifting to cleaner transport solutions, ensuring that everyonehas access to an affordable and efficient mobility and strengthening transport systems in order to enhancetheir resilience are three of the major challenges that transport is facing. Shared mobility have a greatpotential to help overcoming such challenges.

Shared mobility is defined as “the short-term access to shared vehicles according to the user’s needs andconvenience, instead of requiring vehicle ownership” according to Shaheen et al. (2015) [3]. Since theintroduction of the bike sharing system V ?elib’ [2] in Paris, 2007, the concept of shared transportation hasexponentially increased around the world. Thus, it is necessary to understand how to efficiently integratethese systems to the urban transportation systems, in an environmental, social and economic perspective.Machado et al. (2018) [4] present an overview of the shared mobility services, the concepts and characteristicsof each one of them and their users and the challenges that emerge for policy-makers, local authorities andtransport industry when integrating them to urban transportation systems.

One of the major challenges that a local authority faces when adding a shared mobility system (SMS)to its transportation system is ensuring that the SMS comes as a complementary service to the alreadyprovided services and not as a competitor. One of the levers that local authorities have, to enforce thiscomplementarity, is to impose on the SMS provider a certain service quality through taxes when the latterfails to meet the requirements. In this thesis, we will consider three metrics of service quality:•Improving the accessibility of transportation systems in peri-urban areas.•Reducing the footprint of the mobility services.•Ensuring the resilience of the whole transportation system when it faces disruptions.

Regarding the SMS provider, achieving such service quality goes through an optimal inventory managementof its fleet. Two solutions are investigated in the literature of SMS: vehicle relocation or pricing strategies[5]. In this thesis, we will focus on how can pricing strategies help in this direction, even though vehiclerelocation can be considered as a complementary measure.

Objectives:

The objective of this thesis is to present a panel of pricing strategies for SMS providers in order to manage theinventory of their fleets and investigate the impact of such strategies on ensuring the aforementioned threeservice quality metrics, namely, accessibility in peri-urban areas, reduction of the footprint of the mobilityservices and resilience of the transportation system.

One of the most important challenges is to inventory which strategies are the most relevant/promising. Tothat purpose, it is primordial to take into consideration the three dimensions of pricing mechanisms describedin [6]:•Pricing basisis how the user pays his trip. It can beusage-based pricingwhere the price is dependenton the duration of usage (the most commonly used); afixed priceper trip; or apackage pricing(monthly or yearly subscriptions. . . )•Spatio-temporal pricingstipulates that the pricing should depend on the time of use, the originand the destination of the user.•State dependencydistinguishes betweendynamicanddifferentiatedpricing. Dynamic pricing takesinto consideration the state of the SMS as well as the whole transportation system in real-timeand defines the prices accordingly. Differentiated pricing is done in a static,a priorimanner,where the prices are defined according to a historical study of the demand.

Regarding thepricing basis, most of the works in the literature are focused on a usage-based pricing. Thisis a shortcoming in the literature as more and more SMS providers propose different pricing options, bycombining usage-based or fixed pricing with package pricing. The idea in this thesis is to propose pricingstrategies for both cases, without neglecting or advantaging one instead of the other.

As for spatio-temporal pricing, destination-based pricing is rarely explicitly considered, since the destinationof the user is usually unknown in advance (Although, one can argue that it is implicitly considered whenparking options are, spatially, limited, as the price will depend on the destination when the prices are usage-based). Furthermore, according to Lippoldt et al. (2018) [7], such mechanism are more complicated, andcannot be easily communicated. In this thesis we will investigate the impact of introducing this aspect intopricing, by offering incentives to users who are open to provide a zone of their destination, and comparingit to a system where providing a destination is mandatory, with penalties in case of a violation. Althoughthe second strategy is restrictive, it would be interesting to observe the trade-off between the comfort of theuser (how acceptable it is to provide a destination) and a more interesting price.

When it comes to state dependency, differentiated and dynamic pricing are both tackled in the literature.Each system comes with its own advantages and challenges. The biggest advantage of differentiated pricingis that it is easily communicated to users as the price is already fixed for each time and origin of departure.However, this necessitates a perfect understanding of how the demand and the traffic evolve during the day,which makes it very vulnerable to uncertainties. Thus, it makes of a very complex structure problem, sincedifferent aspects and sources of uncertainties are to be taken into consideration. However, although theproblem is complex and can be computationally challenging to solve, as it is an NP-Hard problem [6, 8],exact approaches can be considered, since the problem needs to be solved only once. On the other hand,in the dynamic pricing problem, a re-active approach is taken. At each moment, the state of the networkis observed, and pricings are computed accordingly. For this reason, the problem is less complicated toformulate. However, it is not less computationally challenging to solve. Thus, it is necessary to propose fastapproaches that can be used in anonlinesetting. Moreover, the biggest disadvantage of the dynamic pricingis the lack of transparency in the pricing communicated to the users.

In this thesis, we will propose a combination of the two strategies. Dynamic pricing will be implemented as asecond layer over a differentiated pricing strategy. Differentiated prices will be determined and communicatedto the users as an upper-bound (i.e. the maximum that the user will pay for a certain trip) and dynamicpricings will adjust the prices regarding the state of the network, only if it is more advantageous for the usercompared to the differentiated pricing. A comparison of the combination will be conducted with systems that are solely depending on differentiated and dynamic pricings. Robust and stochastic optimisation approacheswill be focused on to formulate the differentiated pricing problem to take into account the uncertainties ofthe traffic and the demand. Whereas decomposition approaches such as column generation and Benders’decomposition will be investigated for the solving of both problems. However, as these exact approaches willmost likely not be scalable for real-life applications, especially for the dynamic pricing problem, heuristicapproaches will be considered, combining operations research and machine-learning algorithms.

Finally, once the problems are formulated and solved, it is necessary to appropriately asses and validatetheir results. To that purpose, ”MnMs”, an open source multi-modal transport simulation-based platform,developed at the LICIT laboratory will be used. MnMs provides case studies based on real-life demandand transportation networks of the city of Lyon, which will give insight on the practical usefulness of thepricing strategies. Moreover, as the platform is open source, the work developed over the thesis will enrichits features, which can be beneficial for the transportation research community.

Candidate:

The candidate should be perfectly familiar with basic operations research approaches. Comfortable withstochastic and robust optimisation, and have an interest in choice-based optimisation. Moreover, the candi-date should have an aptitude for programming (preferably in both Python and either Java or C++).The chosen candidate will have to audition for the scholarship in front of a jury of Gustave Eiffel Universitythe 15th or 16th of June.

References:

[1] World Bank (2022). Transport Overview. http://www.worldbank.org/en/topic/transport/overview (ac-cessed on 24 November 2022).

[2] Vélib’ (2022). https://www.velib-metropole.fr (accessed on 24 November 2022).

[3] Shaheen, S., Chan, N., Bansal, A., & Cohen, A. (2015). Shared mobility: A sustainability & technologiesworkshop: definitions, industry developments, and early understanding.

[4] Machado, C. A. S., Hue, N. P. M. de S., Berssaneti, F. T., & Quintanilha, J. A. (2018). An overview ofshared mobility. Sustainability (Switzerland), 10(12), 1–21.

[5] Laporte, G., Meunier, F., & Wolfler Calvo, R. (2018). Shared mobility systems: an updated survey.Annals of Operations Research, 271(1), 105-126.

[6] Soppert, M., Steinhardt, C., M ?uller, C., & G ?onsch, J. (2022). Differentiated Pricing of Shared MobilitySystems Considering Network Effects. Transportation Science, 56(5), 1279–1303.

[7] Lippoldt, K., Niels, T., & Bogenberger, K. (2018, November). Effectiveness of different incentive modelsin free-floating carsharing systems: A case study in Milan. In 2018 21st International Conference onIntelligent Transportation Systems (ITSC) (pp. 1179-1185). IEEE.

[8] Waserhole, A., & Jost, V. (2016). Pricing in vehicle sharing systems: Optimization in queuing networkswith product forms. EURO Journal on Transportation and Logistics, 5(3), 293-320.







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