Patrick Stefan Adriaan Stokkink

Biographie

I am a PhD student in the Urban Transport Systems Laboratory (LUTS) of the École Polytechnique Fédérale de Lausanne (EPFL). I received the M.Sc. degree in Econometrics and Management Science (specialization in Operations Research and Quantitative Logistics) in 2019 from Erasmus University Rotterdam. My primary research fields and main research interests involve on-demand mobility and transportation systems. I am using a combination of optimization, simulation and control methods to solve operational and strategic challenges in these systems. My current work is mainly focused on large-scale carpooling and crowd-shipping systems.

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https://people.epfl.ch/patrick.stokkink?lang=fr

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Cours enseignés par cette personne (1)

The scope of the lecture is to provide the basic concepts in transport economics and introduce new ones for private and public transport and environmental issues. Demand, supply, welfare analysis and regulation will be illustrated.

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Publications associées (3)

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Ride-sharing with inflexible drivers in the Paris metropolitan area

Patrick Stefan Adriaan Stokkink

In ride-sharing, commuters with similar itineraries share a vehicle for their trip. Despite its clear benefits in terms of reduced congestion, ride-sharing is not yet widely accepted. We propose a specific ride-sharing variant, where drivers are completely inflexible. This variant can form a competitive alternative against private transportation, due to the limited efforts that need to be made by drivers. However, due to this inflexibility, matching of drivers and riders can be substantially more complicated, compared to the situation where drivers can deviate. In this work, we propose a four-step procedure to identify the effect of such a ride-sharing scheme. We use a dynamic mesoscopic traffic simulator which computes departure-time choices and route choices for each commuter. The optimal matching of potential drivers and riders is obtained outside the simulation framework through an exact formulation of the problem. We evaluate the potential of this ride-sharing scheme on a real network of the Paris metropolitan area for the morning commute. We show that even with inflexible drivers and when only a small share of the population is willing to participate in the ride-sharing scheme, ride-sharing can alleviate congestion. Further improvements can be obtained by increasing the capacity of the vehicles or by providing small monetary incentives, but without jeopardizing the inflexibility of the drivers. Thereby, we show that ride-sharing can lead to fuel savings, CO(2 )emission reductions and travel time savings on a network level, even with a low participation rate.

SPRINGER2022

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Predictive user-based relocation through incentives in one-way car-sharing systems

Nikolaos Geroliminis, Patrick Stefan Adriaan Stokkink

Car-sharing systems are an attractive alternative to private vehicles due to their benefits in terms of mobility and sustainability. However, the distribution of vehicles throughout the network in one-way systems is disturbed due to asymmetry and stochasticity in demand. As a consequence, vehicles need to be relocated to maintain an adequate service level. In this paper, we develop a user-based vehicle relocation approach through the incentivization of customers and a predictive model for the state of the system based on Markov chains. Our methods determine the optimal incentive as a trade-off between the cost of an incentive and the expected omitted demand loss while taking into account the value of time of customers. We introduce a learning algorithm that allows the operator to estimate unknown customer preferences to find the optimal incentive. Experimental results in an event-based simulation of a real system show that the use of incentives can significantly increase the service level and profitability of a car-sharing system and decrease the number of staff members needed to balance the vehicles in the system. Thereby, incentives are a more sustainable alternative to staff-based relocations. Extensive sensitivity analyses show the prospective benefits in terms of customer flexibility and the robustness of our results to varying customer preferences.

2021

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Predictive user-based relocation through incentives in one-way car-sharing systems

Nikolaos Geroliminis, Patrick Stefan Adriaan Stokkink

2020

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