@techreport{MTMT:34827436,
	title = {A Python tool for SUMO traffic simulation to model Hybrid Electric Vehicles (HEV) for comprehensive emission analysis},
	url = {https://m2.mtmt.hu/api/publication/34827436},
	author = {Tettamanti, Tamás and Chinedu, Amabel Okolie and Ormándi, Tamás and Varga, Balázs},
	doi = {10.13140/RG.2.2.33734.69443},
	unique-id = {34827436},
	year = {2024},
	pages = {1-22},
	orcid-numbers = {Tettamanti, Tamás/0000-0002-8934-3653; Ormándi, Tamás/0000-0002-7897-8573; Varga, Balázs/0000-0002-2945-7974}
}

@article{MTMT:34803158,
	title = {Adaptive Curriculum Learning with Successor Features for Imbalanced Compositional Reward Functions},
	url = {https://m2.mtmt.hu/api/publication/34803158},
	author = {Szőke, László and Shperberg, Shahaf S. and Holtz, Jarrett and Allievi, Alessandro},
	doi = {10.1109/LRA.2024.3387134},
	journal-iso = {IEEE ROBOT AUTOM LETT},
	journal = {IEEE ROBOTICS AND AUTOMATION LETTERS},
	unique-id = {34803158},
	issn = {2377-3766},
	abstract = {This work addresses the challenge of reinforcement learning with reward functions that feature highly imbalanced components in terms of importance and scale. Reinforcement learning algorithms generally struggle to handle such imbalanced reward functions effectively. Consequently, they often converge to suboptimal policies that favor only the dominant reward component. For example, agents might adopt passive strategies, avoiding any action to evade potentially unsafe outcomes entirely. To mitigate the adverse effects of imbalanced reward functions, we introduce a curriculum learning approach based on the successor features representation. This novel approach enables our learning system to acquire policies that take into account all reward components, allowing for a more balanced and versatile decision-making process. IEEE},
	keywords = {SAFETY; reinforcement learning; reinforcement learning; KNOWLEDGE MANAGEMENT; training; decision making; Accident prevention; Curricula; Learning algorithms; VECTORS; job analysis; vehicle dynamics; Knowledge transfer; Knowledge transfer; Trajectory; LEARNING APPROACH; Task analysis; Task analysis; Adverse effect; reward function; Reinforcement learnings; Vehicle's dynamics; continual learning; continual learning; Reinforcement learning algorithms; Adaptive curriculums},
	year = {2024},
	eissn = {2377-3766},
	orcid-numbers = {Szőke, László/0000-0001-9926-4054}
}

@misc{MTMT:34791003,
	title = {Korszerű eszközök a közúti járműforgalom modellezésére és irányítására. Bemutatkozik a BME Traffic Lab},
	url = {https://m2.mtmt.hu/api/publication/34791003},
	author = {Tettamanti, Tamás and Ormándi, Tamás and Wágner, Tamás and Varga, Balázs and Varga, István},
	unique-id = {34791003},
	abstract = {A 2007 óta működő BME Traffic Lab kutatócsoport kutat, fejleszt és nem utolsósorban oktatási tevékenységet is ellát a közúti járműforgalom mérése, modellezése, valamint irányítása területén. Munkája során az alapvető matematikai forgalommodellezésből kiindulva, illetve korszerű forgalomszimulációs eljárások alkalmazásával vizsgálja és fejleszti a különböző forgalomirányító algoritmusokat, egészen a forgalomirányító rendszerek programozásáig. Az elmúlt 10 évben a kutatásai kiegészültek az autonóm járművek forgalomra gyakorolt hatásainak vizsgálatával is, valamint természetszerűen a mesterségesintelligencia-alkalmazások is beszűrődtek a feladatokba. A BME Traffic Lab folyamatosan részt vesz hazai és nemzetközi K+F-projektekben, frissen tartva a szaktudást, és garantálva, hogy az elmélet a gyakorlattal is találkozzon.},
	year = {2024},
	pages = {34-37},
	orcid-numbers = {Tettamanti, Tamás/0000-0002-8934-3653; Ormándi, Tamás/0000-0002-7897-8573; Wágner, Tamás/0000-0001-6396-8922; Varga, Balázs/0000-0002-2945-7974; Varga, István/0000-0002-5727-9415}
}

@article{MTMT:34788066,
	title = {Multiobjective Model Predictive Control Based on Urban and Emission Macroscopic Fundamental Diagrams},
	url = {https://m2.mtmt.hu/api/publication/34788066},
	author = {Tesone, Alessio and Tettamanti, Tamás and Varga, Balázs and Bifulco, Gennaro Nicola and Pariota, Luigi},
	doi = {10.1109/ACCESS.2024.3387664},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {12},
	unique-id = {34788066},
	issn = {2169-3536},
	abstract = {Increasing motorization represents a severe problem worldwide, also affecting the emission levels of the road network. Accordingly, congestion management has obtained growing importance because of its strong economic, social, and environmental implications. Macroscopic Fundamental Diagram (MFD) based traffic control is a popular and efficient approach in this scientific field. In our research work the urban network has been divided into homogeneous regions, each of them characterized by its own MFD, and they are regulated using a network-level control scheme. The proposed Multiobjective Model Predictive Control (M-MPC) takes into account the congestion and <italic>CO</italic>2 emission levels of the urban network, modelled by the emerging Emission Macroscopic Fundamental Diagram (e-MFD). The applied strategy has been demonstrated in a realistic traffic scenario (Luxembourg City) using validated microscopic traffic simulation. According to the introduced multiobjective approach, the control method can better exploit the road network capacity while efficiently reducing traffic-induced emissions. Authors},
	year = {2024},
	eissn = {2169-3536},
	pages = {52583-52602},
	orcid-numbers = {Tesone, Alessio/0000-0002-8093-8175; Tettamanti, Tamás/0000-0002-8934-3653; Varga, Balázs/0000-0002-2945-7974; Pariota, Luigi/0000-0001-9173-666X}
}

@article{MTMT:34756334,
	title = {Model Development for Off-Road Traction Control: A Linear Parameter-Varying Approach},
	url = {https://m2.mtmt.hu/api/publication/34756334},
	author = {Szabó, Ádám and Doba, Dániel Károly and Aradi, Szilárd and Kiss, Péter},
	doi = {10.3390/agriculture14030499},
	journal-iso = {AGRICULTURE-BASEL},
	journal = {AGRICULTURE-BASEL},
	volume = {14},
	unique-id = {34756334},
	abstract = {The number of highly automated machines in the agricultural sector has increased rapidly in recent years. To reduce their fuel consumption, and thus their emission and operational cost, the performance of such machines must be optimized. The running gear–terrain interaction heavily affects the behavior of the vehicle; therefore, off-road traction control algorithms must effectively handle this nonlinear phenomenon. This paper proposes a linear parameter-varying model that retains the generality of semiempirical models while supporting the development of real-time state observers and control algorithms. First, the model is derived from the Bekker–Wong model for the theoretical case of a single wheel; then, it is generalized to describe the behavior of vehicles with an arbitrary number of wheels. The proposed model is validated using an open-source multiphysics simulation engine and experimental measurements. According to the validated results, it performs satisfactorily overall in terms of model complexity, calculation cost, and accuracy, confirming its applicability.},
	year = {2024},
	eissn = {2077-0472},
	orcid-numbers = {Kiss, Péter/0000-0002-5265-8133}
}

@article{MTMT:34724815,
	title = {Macroscopic modeling of connected, autonomous and human-driven vehicles: A pragmatic perspective},
	url = {https://m2.mtmt.hu/api/publication/34724815},
	author = {Imran, Waheed and Tettamanti, Tamás and Varga, Balázs and Bifulco, Gennaro Nicola and Pariota, Luigi},
	doi = {10.1016/j.trip.2024.101058},
	journal-iso = {TRANSPORT RES INTERDISC PERSP},
	journal = {TRANSPORTATION RESEARCH INTERDISCIPLINARY PERSPECTIVES},
	volume = {24},
	unique-id = {34724815},
	issn = {2590-1982},
	abstract = {Several interdisciplinary studies have investigated the impact of Connected and Autonomous Vehicles (CAVs) on the performance of traffic networks, which expect positive effects. Nevertheless, there will be a transitional period during which both Human-Driven Vehicles (HDVs) and CAVs shall operate simultaneously. Adequate modeling of the interactions between CAVs and HDVs is vital to understand the mixed traffic dynamics. We propose a second-order macroscopic model by reconstructing the backward propagation speed of perturbation based on the dynamic headway distance between vehicles in mixed traffic. The proposed model is validated using microscopic simulations, and it replicates the given traffic scenarios subjected to assorted Penetration Rate (PR) of CAVs. The proposed model is employed to investigate the dynamics of mixed traffic. The results demonstrate that the average traffic velocity and the Level of Service (LOS) significantly improve with the increase in the PR of CAVs. Additionally, the performance of the proposed model is compared with the well-known Jiang-Qing-Zhu (JQZ) model, and it outperforms the JQZ model. The proposed model can be employed in traffic forecasting and real-time traffic control.},
	year = {2024},
	orcid-numbers = {Tettamanti, Tamás/0000-0002-8934-3653; Varga, Balázs/0000-0002-2945-7974; Pariota, Luigi/0000-0001-9173-666X}
}

@article{MTMT:34687657,
	title = {Deep Reinforcement Learning combined with RRT for trajectory tracking of autonomous vehicles.},
	url = {https://m2.mtmt.hu/api/publication/34687657},
	author = {Kővári, Bálint and Angyal, Balint Gergo and Bécsi, Tamás},
	doi = {10.1016/j.trpro.2024.02.032},
	journal-iso = {TRANSP RES PROCEDIA},
	journal = {TRANSPORTATION RESEARCH PROCEDIA},
	volume = {78},
	unique-id = {34687657},
	issn = {2352-1465},
	abstract = {Sample inefficiency is a long-standing problem in Deep Reinforcement Learning based algorithms, which shadows the potential of these techniques. So far, the primary approach for tackling this issue is prioritizing the gathered experiences. However, the strategy behind collecting the experiences received less atention, but it is also a legitimate approach for prioritizing. In this paper, the Rapidly exploring Random Trees algorithm and Deep Reinforcement Learning are combined for the trajectory tracking of autonomous vehicles to mitigate the issues regarding sample efficiency. The core of the concept is to utilize the tremendous explorational power of RRT for covering the state space via experiences for the Agent to diversify its training data buffer. The results demonstrate that this approach outperforms the classic trial-and-error-based concept according to several performance indicators. © 2024 The Authors. Published by ELSEVIER B.V.},
	keywords = {reinforcement learning; Exploration-exploitation trade-off; RRT},
	year = {2024},
	eissn = {2352-1457},
	pages = {246-253},
	orcid-numbers = {Bécsi, Tamás/0000-0002-1487-9672}
}

@article{MTMT:34687431,
	title = {Choosing Routes in Urban Areas that are Robust Against Minor Nonrecurring Traffic Incidents},
	url = {https://m2.mtmt.hu/api/publication/34687431},
	author = {Fazekas, Zoltán and Obaid, Mohammed and Gáspár, Péter},
	doi = {10.3311/PPtr.23792},
	journal-iso = {PERIOD POLYTECH TRANSP ENG},
	journal = {PERIODICA POLYTECHNICA TRANSPORTATION ENGINEERING},
	volume = {52},
	unique-id = {34687431},
	issn = {0303-7800},
	abstract = {The paper looks at certain vehicle-level re-routing issues within urban road networks, and related network-level traffic management issues. These arise mostly when the traffic along a route of significance is hindered, slowed down, or even blocked because of some – possibly minor – unexpected, nonrecurring traffic incident at a sensitive road location, or road section. Considerations for planning routes in urban areas – routes that are in some sense robust against such incidents – are presented herein. Also, the on–the–spot detection of traffic queues by an ego-vehicle – relying on data streams from on-board visual line-of-sight (LoS) exteroceptive sensors watching, scanning and monitoring the ego-vehicle's road environment, and by some on-board dedicated real-time detection systems processing and analyzing the incoming data streams – is touched upon. However, this traffic congestion avoidance and mitigating approach – effectuated either by individual autonomous vehicles, or by human drivers – presumes availability of alternative routes, which is not the case for a good portion of the route considered. A route planning approach that could be used for routes with such critical sections is proposed and motivated through an example of an urban route of significance.},
	year = {2024},
	eissn = {1587-3811},
	pages = {173-180},
	orcid-numbers = {Fazekas, Zoltán/0000-0001-5159-4476; Gáspár, Péter/0000-0003-3388-1724}
}

@techreport{MTMT:34589396,
	title = {A Python tool for SUMO traffic simulation to model start-stop system for comprehensive emission analysis},
	url = {https://m2.mtmt.hu/api/publication/34589396},
	author = {Tettamanti, Tamás and Okolie, C.A. and Ormándi, Tamás and Varga, Balázs},
	doi = {10.13140/RG.2.2.12554.24003},
	unique-id = {34589396},
	year = {2024},
	pages = {1-21},
	orcid-numbers = {Tettamanti, Tamás/0000-0002-8934-3653; Ormándi, Tamás/0000-0002-7897-8573; Varga, Balázs/0000-0002-2945-7974}
}

@article{MTMT:34588733,
	title = {Urban Traffic Congestion Alleviation Relying on the Vehicles’ On-board Traffic Congestion Detection Capabilities},
	url = {https://m2.mtmt.hu/api/publication/34588733},
	author = {Fazekas, Zoltán and Obaid, Mohammed and Karim, L and Gáspár, Péter},
	doi = {10.12700/APH.21.6.2024.6.1},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {34588733},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {7-31},
	orcid-numbers = {Fazekas, Zoltán/0000-0001-5159-4476; Gáspár, Péter/0000-0003-3388-1724}
}