Nikolas Geroliminis

Prof. Dr.
Nikolas Geroliminis
PI
Executive Committee Member
NCCR Automation will revolutionize how emerging technologies and intelligent systems can reshape mobility in resilient and sustainable ways.

Prof. Nikolas Geroliminis is an Associate Professor at EPFL and the head of the Urban Transport Systems Lab. Before joining EPFL he was an Assistant Professor  at the U of Minnesota. He has a diploma in Civil Engineering from NTU Athens and a MSc and Ph.D. in civil engineering from UC Berkeley.  He is the Editor-in-Chief of Transportation Research part C: Emerging Technologies . His research interests focus on transport systems, traffic modeling and control,  on-demand transport, car sharing, optimization and  networks. He is a recipient of the ERC Starting Grant METAFERW: Modeling and controlling traffic congestion and propagation in large-scale urban multimodal networks. Among his recent initiatives is the creation of an open-science large-scale dataset of naturalistic urban trajectories collected  by a swarm of drones experiment ( https://open-traffic.epfl.ch).

Scientific Publications

Published
Data-Enabled Predictive Control for Empty Vehicle Rebalancing
European Control Conference (ECC 23)
Published
Hierarchical Pricing Game for Balancing the Charging of Ride-Hailing Electric Fleets
IEEE Transactions on Control Systems Technology
Published
On Finding the Leader's Strategy in Quadratic Aggregative Stackelberg Pricing Games
European Control Conference (ECC 23)
Published
On the utilization of dedicated bus lanes for pooled ride-hailing services
Transportation Research Part B: Methodological
Vol 169 Pages 29-52
Published
Idle-vehicle rebalancing coverage control for ride-sourcing systems
2022 European Control Conference (ECC)
Pages 1970-1975
Published
Incentive-based electric vehicle charging for managing bottleneck congestion
European Journal of Control
Published
A Pricing Mechanism for Balancing the Charging of Ride Hailing Electric Vehicle Fleets
2022 European Control Conference (ECC)
Pages 1976-1981
Modeling, estimation, and control in large-scale urban road networks with remaining travel distance dynamics
Transportation Research Part C: Emerging Technologies
Vol 128 No 103157
Stabilization of city-scale road traffic networks via macroscopic fundamental diagram-based model predictive perimeter control
Control Engineering Practice
Vol 109 No 104750

Research projects

Title
Principal Investigators

Optimization of space allocation for multi-modal transport systems

Summary

To improve mobility, urban space should be allocated spatiotemporally for travellers that are willing to use different modes of transport (shared mobility, autonomous vehicles,  public transport). Some streets could admittedly be reserved (similarly to dedicated bus lanes) but more effort is needed to create fully connected sub-networks that provides access to all parts of the city. Another  alternative would be time segregation, fully time-based (capacity is allocated to only one class of vehicles at a time), or hybrid (spatial but time-dependent) with temporary reserved lanes. How to develop real-time feedback-oriented control strategies for infrastructure management and how to integrate with operational strategies for the individual modes that are controlled by different operators is an additional challenge.

Optimization of space allocation for multi-modal transport systems

To improve mobility, urban space should be allocated spatiotemporally for travellers that are willing to use different modes of transport (shared mobility, autonomous vehicles,  public transport). Some streets could admittedly be reserved (similarly to dedicated bus lanes) but more effort is needed to create fully connected sub-networks that provides access to all parts of the city. Another  alternative would be time segregation, fully time-based (capacity is allocated to only one class of vehicles at a time), or hybrid (spatial but time-dependent) with temporary reserved lanes. How to develop real-time feedback-oriented control strategies for infrastructure management and how to integrate with operational strategies for the individual modes that are controlled by different operators is an additional challenge.

141
58997232-70ac-4557-98b7-e87940b7c374

Large-scale hierarchical and distributed control for congested urban networks

Summary

Decentralized algorithms for governing multi-modal transport networks with multiple entities and operators might not work properly when congestion is unevenly distributed. As we are scaling-up from the local bottleneck-oriented issues we must consider how local regulators (e.g. traffic lights, ramp meters, pricing) can best communicate in a distributed manner to reach an agreement on their control decisions, so as to ensure local cooperation with its geographical neighbours towards realization of city-level mobility objectives. Establishing proper rules and mechanisms within each subsystem allows self-organization of individual components while ensuring coherence.

Large-scale hierarchical and distributed control for congested urban networks

Decentralized algorithms for governing multi-modal transport networks with multiple entities and operators might not work properly when congestion is unevenly distributed. As we are scaling-up from the local bottleneck-oriented issues we must consider how local regulators (e.g. traffic lights, ramp meters, pricing) can best communicate in a distributed manner to reach an agreement on their control decisions, so as to ensure local cooperation with its geographical neighbours towards realization of city-level mobility objectives. Establishing proper rules and mechanisms within each subsystem allows self-organization of individual components while ensuring coherence.

139
be01ba18-f879-4485-b815-182184701590

Coupled incentives to ease congestion on the electricity and road networks

Summary

The electrification of transportation forecast for the near future will couple two problems of similar nature that have been studied independently until now: congestion on the road network and congestion on the electric network. In this project, we will study the viability of coordinated incentives that exploit this coupling to simultaneously ease these congestions more effectively than independently designed incentives.

Coupled incentives to ease congestion on the electricity and road networks

The electrification of transportation forecast for the near future will couple two problems of similar nature that have been studied independently until now: congestion on the road network and congestion on the electric network. In this project, we will study the viability of coordinated incentives that exploit this coupling to simultaneously ease these congestions more effectively than independently designed incentives.

118
9505eadd-0857-434d-9fa7-1b5b0a84194c

Distributed Dynamic Coverage Control for On-Demand Transportation Operations

Summary

Emerging shared-mobility systems create additional opportunities to decrease car ownership and congestion. Asymmetric demand creates imbalances in the distribution of vehicles for these systems. To maximize the covered demand and decrease the waiting time of passengers, vehicle distribution has to be rebalanced with relocations. The potentially very high number of vehicles necessitates using distributed control algorithms to efficiently solve this problem. Presence of multiple companies competing to serve the same demand can be addressed via game theoretic approaches. Μatching algorithms to create shared rides can be combined with the coverage problem to increase coverage in areas with high demand. Fairness in covering low demand areas will also be investigated.

Distributed Dynamic Coverage Control for On-Demand Transportation Operations

Emerging shared-mobility systems create additional opportunities to decrease car ownership and congestion. Asymmetric demand creates imbalances in the distribution of vehicles for these systems. To maximize the covered demand and decrease the waiting time of passengers, vehicle distribution has to be rebalanced with relocations. The potentially very high number of vehicles necessitates using distributed control algorithms to efficiently solve this problem. Presence of multiple companies competing to serve the same demand can be addressed via game theoretic approaches. Μatching algorithms to create shared rides can be combined with the coverage problem to increase coverage in areas with high demand. Fairness in covering low demand areas will also be investigated.

105
91547dbb-fb8b-4801-9b24-09f41492e308