Patrick Stokkink

% VERIFIED; verified 2026-10-11
@article{orbit-b7a9b36bff7fbe36,
  title = {A decomposition-based matheuristic for the passenger-oriented integrated passenger-freight transport problem},
  author = {Li, Siqiao and Stokkink, Patrick and Cats, Oded and Saeednia, Mahnam},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2027},
  volume = {194},
  part = {Part A},
  pages = {105996},
  eid = {105996},
  doi = {10.1016/j.trc.2026.105996},
  url = {https://doi.org/10.1016/j.trc.2026.105996},
  issn = {0968-090X, 1879-2359},
  publisher = {Elsevier},
  abstract = {Integrating passenger and freight transport has become increasingly important for improving the efficiency of urban mobility systems, as planning them separately often results in underutilized capacity and redundant vehicle movements. Coordinating the two enables more effective use of existing transport resources for public transport operators, helps logistics providers maintain cost-effective and reliable deliveries, and assists public authorities in alleviating urban congestion. When implementing such integration, railway stations play a key role as urban hotspots with limited access, where road-rail connections can facilitate efficient intermodal operations. In this work, we introduce a passenger-oriented pickup and delivery problem with explicit road-rail connections and a fleet of shared-use light vehicles. The problem is formulated as a mixed-integer linear program to jointly determine fleet size, routing, and scheduling plans, ensuring a balanced service quality for both passengers and freight. A decomposition-based matheuristic is developed that iteratively coordinates a routing-oriented master problem and a scheduling-oriented subproblem, with feasibility partially restored through a schedule-shifting strategy. The master problem is tackled using an adaptive large neighborhood search enhanced with variable neighborhood search, featuring a novel adaptive selection mechanism based on the relative contributions of service quality indicators. The resulting routing decisions are then passed to the scheduling subproblem, which is solved efficiently using a propagation-based approach that guarantees local optimality. Extensive sensitivity and benchmark analyses demonstrate the benefits of integrated transport in reducing delays, particularly in resource-constrained settings and across different passenger–freight demand compositions.},
  keywords = {Pickup and delivery problem, Integrated transport, Matheuristic, Adaptive large neighborhood search},
}

% VERIFIED; verified 2026-10-11
@article{orbit-acfda03c40168bfa,
  title = {Role of topology and recovery strategies on multi-dimensional resilience of road networks},
  author = {Balakrishnan, Srijith and Stokkink, Patrick},
  journal = {International Journal of Disaster Risk Reduction},
  year = {2026},
  volume = {144},
  pages = {106366},
  eid = {106366},
  doi = {10.1016/j.ijdrr.2026.106366},
  url = {https://doi.org/10.1016/j.ijdrr.2026.106366},
  issn = {2212-4209},
  publisher = {Elsevier BV},
  abstract = {This paper examines how road network topology and post-disruption recovery strategies jointly shape recovery processes following disruptions. We develop a scalable stress-testing framework to simulate disruption scenarios and generate recovery data for road networks, which is subsequently analyzed with regression models to identify the effects of structural characteristics, recovery strategy, and disruption severity on multi-dimensional resilience. Using 224 real-world road networks, we assess their post-disruption recovery across five resilience dimensions: operational efficiency, accessibility, local redundancy, global connectivity, and spatial fairness. The results yield three key insights with significant implications on infrastructure design and management. First, road network resilience is fundamentally multidimensional: no single recovery strategy consistently performs best across all dimensions. Second, topological characteristics influence recovery outcomes. Third, strategy-based recovery provides limited benefits under minor disruptions but substantially accelerates recovery under severe damage. These findings highlight the need for policies that integrate robust and diverse network design with post-disaster recovery planning for resilience management.},
  keywords = {Disaster resilience, Disruptions, Restoration, Statistical analysis, Topological metrics, Urban transport planning},
}

% VERIFIED; verified 2026-10-11
@article{orbit-866cac1d1b0899c6,
  title = {A two-stage stochastic programming approach for strategic rolling stock scheduling in passenger railway transport},
  author = {Li, Siqiao and van Lieshout, Rolf and Stokkink, Patrick},
  journal = {Omega},
  year = {2026},
  volume = {144},
  pages = {103593},
  eid = {103593},
  doi = {10.1016/j.omega.2026.103593},
  url = {https://doi.org/10.1016/j.omega.2026.103593},
  issn = {0305-0483, 1873-5274},
  publisher = {Elsevier BV},
  abstract = {Conventional railway planning follows a sequential approach in which rolling stock scheduling is performed only after timetables are constructed, forcing early-stage line-planning decisions to rely on simplified rolling stock cost approximations. The lack of precise cost information hampers line-plan evaluation and limits the design of cost-efficient services. This study considers rolling stock scheduling in a strategic setting, aiming to estimate fleet size and operational costs for a given line plan without solving the timetabling problem. Since predefined train services are unavailable at this level, unlike in tactical planning, we introduce a network representation incorporating shunting movements like coupling and uncoupling. To account for demand uncertainty, a two-stage stochastic programming approach is proposed, in which first-stage decisions determine fleet acquisition and second-stage decisions optimize rolling stock circulation, including shunting, under each demand scenario. Two models are developed, differing in whether the fleet size is estimated using a flow-based or circulation-based approach. The applicability of the proposed models is demonstrated using instances derived from the Dutch Intercity network. Compared to a trip-based benchmark, the circulation-based model yields more accurate cost estimates, while short and simple cycles retain most of its value at much lower computational cost. Moreover, stochastic planning better captures the procurement–service trade-off under demand uncertainty and produces more robust fleet plans than a deterministic approach. Under strong emphasis on investment costs, fleet choices are dominated by flexible, cost-effective unit types. Allowing shunting enables flexible capacity redistribution, reducing fleet requirements and investment costs while maintaining comparable service levels.},
  keywords = {Railway planning, Rolling stock scheduling, Shunting, Uncertain demand, Stochastic programming},
}

% VERIFIED; verified 2026-10-11
@article{orbit-6ba9bfc7dcd9b629,
  title = {A synthesis of the European copper supply chain under the Critical Raw Materials Act},
  author = {van Citters, Veerle and van Binsbergen, Arjan and Stokkink, Patrick},
  journal = {Cleaner Logistics and Supply Chain},
  year = {2026},
  volume = {20},
  pages = {100344},
  eid = {100344},
  doi = {10.1016/j.clscn.2026.100344},
  url = {https://doi.org/10.1016/j.clscn.2026.100344},
  issn = {2772-3909},
  publisher = {Elsevier BV},
  abstract = {The European Union’s (EUs) Critical Raw Materials Act (CRMA) aims to strengthen the EUs resource resilience by increasing the autonomy of Critical Raw Material (CRM) supply through EU-based extraction, processing, and recycling, thereby reducing external dependencies and promoting a circular economy. Copper, a key CRM, faces growing demand that cannot be fulfilled by mining alone. This research analyzes the European copper supply chain under the CRMA and evaluates the role of recycling in meeting its requirements. An initial qualitative analysis suggests that recycling is the most promising solution to comply with the CRMA. The study develops a Mixed Integer Linear Programming (MILP) model to optimize the European copper recycling network under the CRMA recycling requirement and to validate the potential of recycling in a quantitative way. Results identify four optimal facility locations spread through Europe based on geographic centrality weighted by supply and demand quantities. Although the collection of European waste can feasibly satisfy the minimum recycling requirements as set out by the CRMA, changing regulations, disruptions, or changes in demand and external supply may lead to shortages. Improving recycling efficiency has therefore been marked as an important direction of future research.},
  keywords = {Copper, Critical Raw Materials Act, Mixed integer linear programming, Recycling network optimization, Sustainability},
}

% VERIFIED; verified 2026-10-11
@article{orbit-9279300322b318ea,
  title = {Redesigning urban intermodal transit for passenger and freight co-modal mobility: Metro line planning with integrated truck routing},
  author = {Lu, Yahan and Peng, Yuwen and Xia, Dongyang and Stokkink, Patrick and Sharif Azadeh, Shadi},
  journal = {Transportation Research Part B: Methodological},
  year = {2026},
  volume = {211},
  pages = {103538},
  eid = {103538},
  doi = {10.1016/j.trb.2026.103538},
  url = {https://doi.org/10.1016/j.trb.2026.103538},
  issn = {0191-2615, 1879-2367},
  publisher = {Elsevier BV},
  abstract = {Urban transportation systems are under increasing pressure to efficiently accommodate both passenger and freight mobility within limited infrastructure and operational budgets. Operators face growing pressure to make more efficient use of existing infrastructure due to economic and environmental constraints. In this regard, we investigate the capacitated line planning problem with integrated truck routing for passenger and freight co-modal mobility in an intermodal network composed of an urban rail transit (URT) system and road-based truck services. Different from existing research that either focuses on passenger-oriented line planning in a single-modal network or optimizes tactical- and operational-level decisions for passenger and freight co-modal mobility based on fixed line plans, we consider a strategic-level problem where urban planners aim to fundamentally re-optimize the allocation of existing infrastructure resources and budget across the intermodal network. We formulate this problem as a path-based mixed-integer linear programming model. The model determines line plans and frequency for both URT and truck services, as well as the passenger and freight routing, with the aim of optimizing the total weighted travel time of passengers and freight. To solve large-scale instances, we develop a column generation algorithm with a two-phase diving procedure. Our algorithm incorporates acceleration techniques and budget reservation strategies derived from the mathematical properties of the formulation. We evaluate our approaches on both artificial instances and real-world case studies from the Rotterdam intermodal network. The results show that our algorithm obtains solutions with an average optimality gap of 0.77\%, while reducing computation time by 95.53\% compared to GUROBI. Using the existing Rotterdam URT line plan with optimized frequencies as a benchmark, we demonstrate that our approach improves service quality by 6.50\% without increasing operational costs. From a practical perspective, real-life case studies suggest that accommodating the passenger and freight co-modal mobility in existing passenger-oriented metro systems requires redesigning the line plan from scratch, rather than merely adjusting frequencies or incrementally expanding the current network. The results further indicate that effective freight integration depends on reallocating budget across modes, with URT serving as the backbone of the intermodal system and truck services providing a complementary function.},
  keywords = {Intermodal transit networks, Line planning, Truck routing, Passenger and freight co-modal, Column generation},
}

% VERIFIED; verified 2026-10-11
@article{orbit-12a1791a0a4fab30,
  title = {A rolling horizon approach for multi-objective optimization of two-way logistics with postponed orders},
  author = {Tran, Phuong Anh Thi and Stokkink, Patrick},
  journal = {Computers \& Industrial Engineering},
  year = {2026},
  volume = {217},
  pages = {112046},
  eid = {112046},
  doi = {10.1016/j.cie.2026.112046},
  url = {https://doi.org/10.1016/j.cie.2026.112046},
  issn = {0360-8352, 1879-0550},
  publisher = {Elsevier},
  abstract = {Last-mile delivery of e-commerce is a complex mix of simultaneous delivery of goods and pickup of returned goods. With return rates being substantial in the e-commerce sector, this problem is highly relevant. To address this challenge, this study formulates a rolling horizon simultaneous pickup and delivery problem with time windows in a two-way logistics system. The proposed approach integrates a multi-objective mixed-integer linear programming model with a rolling-horizon framework, allowing forward deliveries and reverse pickups to be postponed based on different priorities and penalties, while capturing the interdependence between outbound and return flows. Economic efficiency, environmental impact, and social factors are explicitly incorporated into the objective function. For large-scale instances, an adaptive large neighborhood search with simulated annealing metaheuristic is designed and compared against a weighted-sum mixed integer linear program approach. Numerical experiments on Solomon datasets demonstrate that the proposed method efficiently achieves high-quality solutions, reveals trade-offs among sustainability objectives, and shows how coordinating forward and reverse flows can enhance operational adaptability. The results also highlight an unintended outcome of the proposed rolling-horizon planning: generation and propagation of backlog of demand. This highlights the need for adaptive mechanisms that can manage such issues in multi-period planning.},
  keywords = {Last-mile delivery, Multi-objective optimization, Rolling-horizon planning, Two-way logistics},
}

% VERIFIED; verified 2026-10-11
@article{orbit-0ff17dc05ca7a7fc,
  title = {Trade-offs in multi-channel delivery network design with perishable and non-perishable goods},
  author = {Cao, Luyang and Tavasszy, Lóránt and Stokkink, Patrick},
  journal = {Journal of Supply Chain Management Science},
  year = {2025},
  volume = {6},
  number = {3-4},
  doi = {10.59490/jscms.2025.8229},
  url = {https://doi.org/10.59490/jscms.2025.8229},
  issn = {2451-9901},
  publisher = {TU Delft OPEN Publishing},
  abstract = {This study focuses on optimizing last-mile delivery in e-commerce by balancing cost efficiency and customer preferences, particularly for mixed perishable and non-perishable goods distribution. As online grocery shopping grows, ensuring the timely and efficient delivery of perishable products while maintaining quality remains a critical challenge. The problem is modelled as a variant of the multi-objective Vehicle Routing Problem (VRP), where customer utility and operational costs are incorporated as two objectives. Customer utility is computed with parameters estimated using the Best-Worst Method (BWM). The multi-objective model is solved by linearizing the non-linear objectives and using a weighted sum method. The model evaluates home delivery, attended pickup points, and lockers, revealing that cost-driven strategies shift deliveries toward self-pickup, with perishable items primarily assigned to attended pickup points due to temperature control. The findings provide insights for improving delivery network design, enhancing service quality, and optimizing the distribution of both perishable and non-perishable products.},
  keywords = {Last mile delivery, Perishable goods logistics, Vehicle routing problem, Mixed integer linear programming, Best-World Method},
}

% VERIFIED; verified 2026-10-11
@article{orbit-a204c054b4312934,
  title = {On the optimal micro-hub locations in a multi-modal last-mile delivery system},
  author = {Stokkink, Patrick and Geroliminis, Nikolas},
  journal = {Transportation Research Part E: Logistics and Transportation Review},
  year = {2025},
  volume = {203},
  pages = {104344},
  eid = {104344},
  doi = {10.1016/j.tre.2025.104344},
  url = {https://doi.org/10.1016/j.tre.2025.104344},
  issn = {1366-5545, 1878-5794},
  publisher = {Elsevier BV},
  abstract = {Last-mile delivery is one of the most polluting parts of the supply chain. This is partially caused by increased congestion in urban areas and repetitive stop-and-go traffic. One possible alternative to this is to use micro-mobility to replace large motorized vehicles. However, these vehicles are usually slower and have lower capacity. In this work, we propose a multi-modal logistics system for last-mile delivery that combines the use of trucks, metro and micro-mobility. This innovative type of system uses the metro to distribute the parcels to micro-hubs across the network and uses micro-mobility only for the final part of the parcel's itinerary from the micro-hub to the front door of the customer. We focus on finding the optimal micro-hub locations in such a system. We use a continuum approximation of the operational and tactical decisions which includes routing of the micro-mobility vehicles. The whole problem is then modeled as a Mixed Integer Linear Programming (MILP) model for the strategic decisions regarding the micro-hubs, which include location, capacity, and fleet-assignment decisions. We evaluate the results of a case study of the city of Madrid, which illustrates that a multi-modal last-mile delivery system can significantly improve a traditional last-mile delivery system in terms of operational costs and pollution.},
  keywords = {Continuum approximation, Last-mile delivery, Location problems, Mixed integer linear programming, Multi-modal transportation},
}

% VERIFIED; verified 2026-10-11
@article{orbit-612ca8a1aca33200,
  title = {Optimal matching for ridesharing systems with endogenous and flexible user participation},
  author = {Stokkink, Patrick and Yang, Zhenyu and Geroliminis, Nikolas},
  journal = {Transportation Research Part B: Methodological},
  year = {2025},
  volume = {200},
  pages = {103290},
  eid = {103290},
  doi = {10.1016/j.trb.2025.103290},
  url = {https://doi.org/10.1016/j.trb.2025.103290},
  issn = {0191-2615, 1879-2367},
  publisher = {Elsevier BV},
  abstract = {The performance of ridesharing systems is intricately entwined with user participation. To characterize such interplay, we adopt a repeated multi-player, non-cooperative game approach to model a ridesharing platform and its users’ decision-making. Users reveal to the platform their participation preferences over being only riders, only drivers, flexible users, and opt-out based on the expected utilities of each mode. The platform optimally matches users with different itineraries and participation preferences to maximize social welfare. We analytically establish the existence and uniqueness of equilibria and design an iterative algorithm for the solution, for which convergence is guaranteed under mild conditions. A case study is conducted with real travel demand data in Chicago. The results highlight the effect of users’ flexibility regarding mode preferences on system performance (i.e., the average utility of users and the percentage of successful matches). A sensitivity analysis on the level of subsidy and the distribution of utility between matched riders and drivers shows that uneven distributions of utility may lead to a higher percentage of successful matches. Additional insights are provided on the effect of a user's origin and destination locations on their role choice and likelihood to be matched.},
  keywords = {Linear programming, Mode choice, Random utility model, Repeated multi-player non-cooperative game, Ridesharing},
}

% VERIFIED; verified 2026-10-11
@article{orbit-afa4804285f227aa,
  title = {Optimizing multi-modal ride-matching with transfers},
  author = {Stokkink, Patrick and de Palma, André and Geroliminis, Nikolas},
  journal = {Transportation Research Part C: Emerging Technologies},
  year = {2025},
  volume = {179},
  pages = {105274},
  eid = {105274},
  doi = {10.1016/j.trc.2025.105274},
  url = {https://doi.org/10.1016/j.trc.2025.105274},
  issn = {0968-090X, 1879-2359},
  publisher = {Elsevier BV},
  abstract = {One of the limitations of ride-sharing is that matched drivers and riders need to have similar itineraries and desired arrival times for ride-sharing to be competitive against other transport modes. By allowing a single transfer at a designated transfer hub, their itineraries need to be only partially similar, and therefore more matching options are created. In this paper, we develop an optimal matching approach that matches riders to drivers, taking into account multi-modal routing options to model competition and collaboration between multiple modes of transport. We allow for transfers between modes and between multiple drivers. We model this as a path-based integer programming problem and we develop a simulated annealing algorithm to efficiently solve realistic large-scale instances of the problem. Our analysis indicates that a single transfer hub can reduce significantly the average generalized cost of riders and the total vehicle hours traveled by creating efficient matches. As opposed to previous studies, our work shows that ride-sharing not only attracts former public transport users but also former private car users. By allowing for intermodal transfers and by choosing the cost parameters such that transfers are favorable, itineraries where commuters use their car first, before sharing a ride on the second part of their journey, becomes an appealing alternative. Multi-modal ride-matching with transfers has the potential to increase ride-sharing, reduce the number of vehicle hours traveled in private cars, and reduce the number of cars that are present in urban areas during peak hours of congestion.},
  keywords = {Integer programming, Multi-modal, Ride-matching, Ride-sharing, Simulated annealing, Transfers},
}

% VERIFIED; verified 2026-10-11
@article{orbit-c7431635f361772d,
  title = {A column and row generation approach to the crowd-shipping problem with transfers},
  author = {Stokkink, Patrick and Cordeau, Jean-François and Geroliminis, Nikolas},
  journal = {Omega},
  year = {2024},
  volume = {128},
  pages = {103134},
  eid = {103134},
  doi = {10.1016/j.omega.2024.103134},
  url = {https://doi.org/10.1016/j.omega.2024.103134},
  issn = {0305-0483, 1873-5274},
  publisher = {Elsevier BV},
  abstract = {Crowd-shipping is a last-mile delivery concept in which commuters pick up and deliver parcels on their pre-existing paths. In urban areas, crowd-shipping circumvents problems that traditional last-mile delivery systems suffer from, such as road congestion and lack of parking spaces, especially if more sustainable modes of transport are utilized, like bikes or e-bikes. Using transfers between crowd-shippers allows for expanding the service area and improving the overall performance. However, as this requires synchronization over space and time, it makes the problem more complex. In this work, we develop a model that can encompass fully heterogeneous crowd-shippers and parcels. Thereby, it allows for both direct time-synchronized transfers as well as intermediate storage at designated parcel lockers. We design a column generation algorithm to solve large-scale realistic instances to optimality. We extend the problem to allow crowd-shippers to carry multiple parcels at the same time and for this, we extend the algorithm to simultaneous column and row generation. We evaluate the performance of our algorithm as well as the potential of crowd-shipping with transfers on a realistic case study of a bike-based crowd-shipping system in Washington DC. Our methods solve realistic instances with 1000 crowd-shippers and 1000 parcels within minutes. The results show that a gain in revenue and service level of 30\% can be obtained by allowing transfers. By letting part of the population of crowd-shippers carry two or three parcels at the same time, the revenue and service level can be further increased by 30 to 50\%. Maximum locker capacities are shown to be reasonable and are the highest in areas where there is a large gap between the moment when parcels are dropped off and when they are picked up from parcel points, which are mainly in the city center.},
  keywords = {Column generation, Crowd-shipping, Last-mile delivery, Row generation, Transfers},
}

% VERIFIED; verified 2026-10-11
@article{orbit-42c77161fdad46ad,
  title = {A continuum approximation approach to the depot location problem in a crowd-shipping system},
  author = {Stokkink, Patrick and Geroliminis, Nikolas},
  journal = {Transportation Research Part E: Logistics and Transportation Review},
  year = {2023},
  volume = {176},
  pages = {103207},
  eid = {103207},
  doi = {10.1016/j.tre.2023.103207},
  url = {https://doi.org/10.1016/j.tre.2023.103207},
  issn = {1366-5545, 1878-5794},
  publisher = {Elsevier BV},
  abstract = {Last-mile delivery in the logistics chain contributes to congestion in urban networks due to frequent stops. Crowd-shipping is a sustainable and low-cost alternative to traditional delivery but relies heavily on the availability of occasional couriers. In this work, we propose a crowd-shipping system that uses depots to improve accessibility for potential crowd-shippers to serve a large portion of the demand for small parcels. While small-scale versions of this problem have been recently addressed, scaling to larger instances significantly complexifies the problem. A heuristic approach based on continuum approximation is designed to evaluate the quality of a potential set of depots. By combining an efficient and accurate approximation method with a large neighborhood search heuristic, we can efficiently find a good set of depots, even for large-scale networks. The proposed methodology allows for heterogeneity among crowd-shippers and allows identifying the expected number of delivered parcels in every region, which can be used to enhance lower-level assignment decisions. A case study on the Washington DC network shows that depots are built at geographically central locations but most importantly at locations around popular origins for crowd-shippers. The optimal number of depots is mainly dependent on the marginal number of parcels that can be served by crowd-shippers from a specific depot, relative to the costs involved in opening that depot. The operational costs approximated by our continuum approximation approach deviate on average 2\% from the actual operational costs using dynamic assignment strategies. For small instances, our algorithm finds better solutions than solving a discrete formulation using CPLEX, while being almost 200 times faster. For large instances, the discrete formulation cannot be constructed by CPLEX, whereas the CA-based approach finds good solutions within minutes. Finally, the results show that using CA-based strategies in all three layers of decision-making can improve overall performance by 15\% compared to non-predictive strategies.},
}

% VERIFIED; verified 2026-10-11
@article{orbit-32d4a3e6c5b6933c,
  title = {Ride-sharing with inflexible drivers in the Paris metropolitan area},
  author = {de Palma, André and Javaudin, Lucas and Stokkink, Patrick and Tarpin-Pitre, Léandre},
  journal = {Transportation},
  year = {2024},
  volume = {51},
  number = {3},
  pages = {963--986},
  doi = {10.1007/s11116-022-10361-1},
  url = {https://doi.org/10.1007/s11116-022-10361-1},
  issn = {0049-4488, 1572-9435},
  publisher = {Springer US},
  abstract = {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}\$\$ 2 emission reductions and travel time savings on a network level, even with a low participation rate.},
}

% VERIFIED; verified 2026-10-11
@article{orbit-3976200fa93ad9da,
  title = {Incentive-based electric vehicle charging for managing bottleneck congestion},
  author = {Cenedese, Carlo and Stokkink, Patrick and Geroliminis, Nikolas and Lygeros, John},
  journal = {European Journal of Control},
  year = {2022},
  volume = {68},
  pages = {100697},
  eid = {100697},
  doi = {10.1016/j.ejcon.2022.100697},
  url = {https://doi.org/10.1016/j.ejcon.2022.100697},
  issn = {0947-3580, 1435-5671},
  publisher = {Elsevier BV},
  abstract = {We propose an incentive-based traffic demand management policy to alleviate traffic congestion on a road stretch that creates a bottleneck for the commuters. The incentive targets electric vehicles owners by proposing a discount on the energy price they use to charge their vehicles if they are flexible in their departure time. We show that, with a sufficient monetary budget, it is possible to completely eliminate the traffic congestion and we compute the optimal discount. We analyse also the case of limited budget, when the congestion cannot be completely eliminated. We compute analytically the policy minimising the congestion and estimate the level of inefficiency for different budgets. We corroborate our theoretical findings with numerical simulations that allow us to highlight the power of the proposed method in providing practical advice for the design of policies.},
  keywords = {Bottleneck model, Dynamic energy price, Electric vehicles, Incentive-based traffic demand management},
}

% VERIFIED; verified 2026-10-11
@article{orbit-c1e3b2228ed5ef6f,
  title = {Influence of dynamic congestion with scheduling preferences on carpooling matching with heterogeneous users},
  author = {de Palma, André and Stokkink, Patrick and Geroliminis, Nikolas},
  journal = {Transportation Research Part B: Methodological},
  year = {2022},
  volume = {155},
  pages = {479--498},
  doi = {10.1016/j.trb.2021.11.005},
  url = {https://doi.org/10.1016/j.trb.2021.11.005},
  issn = {0191-2615, 1879-2367},
  publisher = {Elsevier BV},
  abstract = {Carpooling is an efficient measure to fight car ownership and reduce vehicle kilometres travelled. By individuals sharing their commutes, vehicle occupancy increases and congestion is reduced. We develop a dynamic ADL (Arnott, de Palma, Lindsey)–Vickrey approach for a corridor monocentric city à la Hotelling. First, we formulate the matching problem of heterogeneous users in carpooling as an MILP problem and we discuss its analytical properties when there is no congestion. Next, we construct a bi-level optimization problem involving matching (first stage) and dynamic traffic congestion with scheduling preferences (second stage) when congestion is endogenous. We provide a heuristic to attain an optimal matching for a dynamic traffic equilibrium with congestion. Such a template allows studying the two-way causality between dynamic congestion and carpooling matching.},
  keywords = {Economics, Engineering, Civil, Operations Research \& Management Science, Transportation, Transportation Science \& Technology, Business \& Economics, Engineering, Operations Research \& Management Science, Transportation, carpooling, ride-sharing, matching, scheduling delay, dynamic congestion, bi-level optimization, morning commute, traffic flows, model, bottleneck, benefits, optimization, service, design, scheme},
}

% VERIFIED; verified 2026-10-11
@article{orbit-0aacb503839ea34f,
  title = {Predictive user-based relocation through incentives in one-way car-sharing systems},
  author = {Stokkink, Patrick and Geroliminis, Nikolas},
  journal = {Transportation Research Part B: Methodological},
  year = {2021},
  volume = {149},
  pages = {230--249},
  doi = {10.1016/j.trb.2021.05.008},
  url = {https://doi.org/10.1016/j.trb.2021.05.008},
  issn = {0191-2615, 1879-2367},
  publisher = {Elsevier BV},
  abstract = {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.},
  keywords = {One-way car-sharing system, Prediction, User-based vehicle relocations, Incentivization},
}