Michail Makridis


Last Name

Makridis

First Name

Michail

ORCID

0000-0001-7462-4674

Organisational unit

08686 - Gruppe Strassenverkehrstechnik

Search Results

Publications 1 - 10 of 107
  • MFC free-flow model
    Item type: Journal Article
    Makridis, Michail; Fontaras, Georgios; Ciuffo, Biagio; et al. (2019)
    Transportation Research Record
    Free-flow movement of vehicles in microsimulation software is usually defined by a set of equations with no explicit link to the instantaneous dynamics of the vehicles. In some cases, the car and the driver are modeled in a deterministic way, producing a driving behavior, which does not resemble real measurements of car dynamics or driving style. Depending on the research topic, the interest in microsimulation is to capture traffic dynamics phenomena, such as shockwave propagation or hysterisis. Existing car-following models are designed to simulate more the traffic evolution, rather than the vehicle motion, and consequently, minimal computational complexity is a strong requirement. However, traffic-related phenomena, such as the capacity drop are influenced by the free-flow acceleration regime. Furthermore, the acceleration pattern of a vehicle plays an essential role in the estimation of the energy required during its motion, and therefore in the fuel consumption and the CO2 emissions. The present work proposes a lightweight microsimulation free-flow acceleration model (MFC) that is able to capture the vehicle acceleration dynamics accurately and consistently, it provides a link between the model and the driver and can be easily implemented and tested without raising the computational complexity. The proposed model is calibrated, validated, and compared with known car-following models on road data on a fixed route inside the Joint Research Centre of the European Commission. Finally, the MFC is assessed based on 0–100 km/h acceleration specifications of vehicles available in the market. The results prove the robustness and flexibility of the model.
  • Antifragile perimeter control
    Item type: Working Paper
    Sun, Linghang; Makridis, Michail; Genser, Alexander; et al. (2024)
    arXiv
    The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that struggle to cope with real-world uncertainties, leading to a significant decline in effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to improving the robustness or resilience of transportation systems against disruptions, this paper applies the cutting-edge concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility sets itself apart from robustness and resilience as it represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such adversarial events. Hence, modern transportation systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) scheme to control a two-region urban traffic perimeter network. The system exploits the learning capability of RL under disruptions to achieve antifragility. By monitoring the change rate and curvature of the traffic state with the RL framework, the proposed algorithm anticipates imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to improve the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of constant magnitude; and (b) increasingly superior performance under growing disruptions.
  • Resilience-oriented design for public transport networks
    Item type: Conference Paper
    Iliopoulou, Christina; Makridis, Michail; Kouvelas, Anastasios (2023)
    hEART 2023: 11th Symposium of the European Association for Research in Transportation
    Public transport systems are typically designed based on estimated passenger demand and sup-ply patterns, yet may often be called to operate under vastly different operational settings. To systematically design resilient transit systems, it is necessary to “weave” resilience-oriented thinking into the established public transport network design process, moving from an abstract concept to an implementable methodology. This study aims to effectively and efficiently design resilient public transport networks through the integration of Reinforcement Learning (RL), Local Search operators and Particle Swarm Optimization. We present a redundancy indicator and integrate it within a hybrid RL-enhanced metaheuristic solution framework to design more resilient route structures. We apply the proposed Memetic algorithm to an established benchmark from the literature and validate the proposed approach under a series of random and targeted attacks, simulating link disruptions. Results demonstrate that resilience can be enhanced through redundancy without adversely impacting average travel times.
  • Priority pass
    Item type: Conference Paper
    Riehl, Kevin; Kouvelas, Anastasios; Makridis, Michail (2024)
    Signalized intersection management is typically designed with a focus on transportation efficiency metrics such as throughput, queue length and average delay time, to the neglect of vehicle-specific urgencies. This conceptual work proposes a Priority Pass for urban networks as a feasible, economic instrument to expedite entitled vehicles at auction-controlled signalized intersections using movement-phase bidders. The interplay of transportation and economic efficiency at intersections with varying saturation, symmetry, and entitlement is analyzed. The value of the concept is robustly demonstrated for a wide range of scenarios. The Priority Pass creates significant benefits for entitled vehicles without causing arbitrary delays for not-entitled vehicles or de trop worsening transportation efficiency. What’s more, no significant conflict between transportation and economic efficiency was found in the given setup.
  • Exploring antifragility in traffic networks
    Item type: Other Conference Item
    Sun, Linghang; Makridis, Michail; Genser, Alexander; et al. (2024)
    2024 TRB Annual Meeting Online Program Archive
    The optimal operation of transportation networks is often susceptible to unexpected disruptions, such as traffic incidents and social events. Many established control strategies rely on mathematical models that often struggle to cope with real-world uncertainties, leading to a significant decline in their effectiveness when faced with substantial disruptions. While previous research works have dedicated efforts to enhancing the robustness or resilience of transportation systems against disruptions, in this paper, we use the concept of antifragility to better design a traffic control strategy for urban road networks. Antifragility represents a system's ability to not only withstand stressors, shocks, and volatility but also thrive and enhance performance in the presence of such disruptions. Hence, modern transport systems call for solutions that are antifragile. In this work, we propose a model-free deep Reinforcement Learning (RL) algorithm to regulate perimeter control in a two-region urban traffic network to exploit and strengthen the learning capability of RL under disruptions and achieve antifragility. By incorporating antifragility terms based on the change rate and curvature of the traffic state into the RL framework, the proposed algorithm further gains knowledge of the traffic state, which helps in anticipating imminent disruptions. An additional term is also integrated into the RL algorithm as redundancy to enhance the performance under disruption scenarios. When compared to a state-of-the-art model predictive control approach and a state-of-the-art RL algorithm, our proposed method demonstrates two antifragility-related properties: (a) gradual performance improvement under disruptions of similar magnitude; and (b) increasingly superior performance under growing disruptions.
  • Introducing the platoon fundamental diagram for automated vehicles based on large-scale empirical observations
    Item type: Conference Paper
    Makridis, Michail; Kouvelas, Anastasios (2022)
    Abstract Book: 10th Symposium of the European Association for Research in Transport (hEART 2022)
    The interest in understanding the coordinated behavior of car-platoons and their impact on traffic efficiency, safety and energy demand is high. Recently, real-world experiments towards observing car-platoons with ACC-driven vehicles became available. Literature highlights that such car-platoons are string unstable, energy inefficient and potentially safety critical. However, few studies link their behavior at microscopic level to the macroscopic impact on traffic. In this paper, we propose a novel model, namely the Platoon Fundamental Diagram (PFD), that creates explicitly such a link. We validate PFD through worldwide experimental observations and we show that PFD can be reliably used as a means of cross-comparison between platoons with automated vehicles. Furthermore, we present evidence about the invariability of PFD to heterogeneity, i.e. the number of vehicles in the platoon, the order of vehicles, the vehicles' powertrains, the vehicle brands or models within the car- platoon, the particularities of the road infrastructure and the data acquisition methods.
  • The energy impact of adaptive cruise control in real-world highway multiple-car-following scenarios
    Item type: Journal Article
    He, Yinglong; Makridis, Michail; Fontaras, Georgios; et al. (2020)
    European Transport Research Review
    Background: Surging acceptance of adaptive cruise control (ACC) across the globe is further escalating concerns over its energy impact. Two questions have directed much of this project: how to distinguish ACC driving behaviour from that of the human driver and how to identify the ACC energy impact. As opposed to simulations or test-track experiments as described in previous studies, this work is unique because it was performed in real-world car-following scenarios with a variety of vehicle specifications, propulsion systems, drivers, and road and traffic conditions. Methods: Tractive energy consumption serves as the energy impact indicator, ruling out the effect of the propulsion system. To further isolate the driving behaviour as the only possible contributor to tractive energy differences, two techniques are offered to normalize heterogeneous vehicle specifications and road and traffic conditions. Finally, ACC driving behaviour is compared with that of the human driver from transient and statistical perspectives. Its impact on tractive energy consumption is then evaluated from individual and platoon perspectives. Results: Our data suggest that unlike human drivers, ACC followers lead to string instability. Their inability to absorb the speed overshoots may partly be explained by their high responsiveness from a control theory perspective. Statistical results might imply the followers in the automated or mixed traffic flow generally perform worse in reproducing the driving style of the preceding vehicle. On the individual level, ACC followers have tractive energy consumption 2.7–20.5% higher than those of human counterparts. On the platoon level, the tractive energy values of ACC followers tend to consecutively increase (11.2–17.3%). Conclusions: In general, therefore, ACC impacts negatively on tractive energy efficiency. This research provides a feasible path for evaluating the energy impact of ACC in real-world applications. Moreover, the findings have significant implications for ACC safety design when handling the stability-responsiveness trade-off.
  • Exploring antifragility in traffic networks
    Item type: Other Conference Item
    Sun, Linghang; Makridis, Michail; Genser, Alexander; et al. (2023)
  • Estimating reaction time in adaptive cruise control system
    Item type: Conference Paper
    Makridis, Michail; Mattas, Konstantinos; Borio, Daniele; et al. (2018)
    2018 IEEE Intelligent Vehicles Symposium (IV)
  • Microscopic simulation of bicycle traffic flow incorporating cyclists’ heterogeneous dynamics and non-lane-based movement strategies
    Item type: Journal Article
    Brunner, Johannes; Ni, Ying-Chuan; Kouvelas, Anastasios; et al. (2024)
    Simulation Modelling Practice and Theory
    Cycling as a mode of transport is on an upward trend as a low-emission alternative to driving in urbanized areas nowadays. With the increasing number of cyclists, it is of great importance to assess the capacity of cycling infrastructure in practice. Simulation models are useful tools to investigate bicycle flow performance considering cyclists’ distinct moving behaviors. However, existing bicycle simulation models are restricted by either space discretization, lane-based setup, adaptation from models for car traffic, or complicated calibration requirement in a force-based environment. In addition, cyclists’ decision-making ability in the operational-level cycling behavior are not well-captured in these models. This paper proposes a comprehensible microscopic bicycle simulation model which includes a detailed decision-making process and the ability to simulate continuous-space lateral movement. The model consists of three levels, maneuver decision, movement planning, and physical acceleration. It is able to simulate bicycle flow dynamics in undersaturated traffic conditions on an exclusive bike path. As we do not intend to show the empirical validity of the proposed model, the simulation experiment aims at verifying the model and exploring bicycle flow performance in various scenarios by estimating the fundamental diagrams (FDs). The effect of different path widths on bicycle flow capacity is first explored. Other behavioral factors, including desired speed heterogeneity, overtaking incentive, and safety region size perceived by cyclists, which can potentially influence the shape of the FD are also tested. The model can be further extended to simulate relatively complex cycling behavior with cooperative and anticipative strategies and investigate bicycle flow characteristics in congested traffic conditions.
Publications 1 - 10 of 107