MATISSE 3.0: A Large-Scale Multi-agent Simulation System for Intelligent Transportation Systems

Torabi, Behnam; Al-Zinati, Mohammad; Wenkstern, Rym Z.

doi:10.1007/978-3-319-94580-4_38

Cited by 13 publications

(10 citation statements)

References 2 publications

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“…There are also other advanced 3D simulators, not included in the table, like the CARLA Simulator (Dosovitskiy et al 2017), the LGSVL Simulator 2 and the MATISSE (Multi-Agent based TraffIc Safety Simulation systEm) (Torabi et al 2018;Al-Zinati and Wenkstern 2019). These simulators require powerful computers with GPU video cards, because (Schaefer et al 2016(Schaefer et al , 2017 focuses mainly on aspects like trajectory planning, collision avoidance, and cooperative lane changing.…”

Section: Traffic Simulatorsmentioning

confidence: 99%

Solutions to the routing problem: towards trustworthy autonomous vehicles

Varga

2022

Artif Intell Rev

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The general expectation is that the traffic in the cities will be almost optimal when the collective behaviour of autonomous vehicles will determine the traffic. Each member of the collective of autonomous vehicles tries to adapt to the changing environment, therefore together they execute decentralised autonomous adaptation by exploiting real-time information about their environment. The routing of these vehicles needs proper computer science models to be able to develop the best information technology for their control. We review different traffic flow models in computer science, and we evaluate their usefulness and applicability to autonomous vehicles. The classical game theory model implies flow level decision making in route selection. Non-cooperative autonomous vehicles may produce unwanted traffic patterns. Improved decentralised autonomous adaptation techniques try to establish some kind of coordination among autonomous vehicles, mainly through intention awareness. The aggregation of the intentions of autonomous vehicles may help to predict future traffic situations. The novel intention-aware online routing game model points out that intention-awareness helps to avoid that the traffic generated by autonomous vehicles be worse than the traffic indicated by classical traffic flow models. The review helps to make the first steps towards research on global level control of autonomous vehicles by highlighting the strengths and weaknesses of the different formal models. The review also highlights the importance of research on intention-awareness and intention-aware traffic flow prediction methods.

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Section: Traffic Simulatorsmentioning

confidence: 99%

Solutions to the routing problem: towards trustworthy autonomous vehicles

Varga

2022

Artif Intell Rev

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“…16 traffic simulator software or platforms are identified in these studies to simulate the traffic. These include SUMO 3 , VISSIM 4 , PARAMICS 5 , GLD 6 , AIMSUN 7 , ITSUMO (Silva et al, 2004), CityFlow 8 , TRANSYT 9 /TRANSYT- 7F 10 , MATLAB 11 , AIM (Dresner and Stone, 2008), TSIS 12 , MATISSE (Torabi et al, 2018b), USTCMTS (used in (Shi and F. Chen, 2018)), SMPL (used in (Su and Tham, 2007)), and SeSAm (used in (Bazzan et al, 2007)). See the distribution of frequency of the traffic simulators in included papers in Figure 9.…”

Section: Simulation and Simulated Networkmentioning

confidence: 99%

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Noaeen¹,

Naik²,

Goodman³

et al. 2021

Preprint

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Improvement of traffic signal control (TSC) efficiency has been found to lead to improved urban transportation and enhanced quality of life. Recently, the use of reinforcement learning (RL) in various areas of TSC has gained significant traction; thus, we conducted a systematic literature review as a systematic, comprehensive, and reproducible review to dissect all the existing research that applied RL in the network-level TSC (NTSC) domain. The review only targeted the network-level articles that tested the proposed methods in networks with two or more intersections. We used natural language processing to define the search strings and searched Google Scholar, Web of Science, IEEE Xplore, ACM Digital Library, Springer Link, and Science Direct databases. This review covers 160 peer-reviewed articles from 30 countries published from 1994 to March 2020. The goal of this study is to provide the research community with statistical and conceptual knowledge, summarize existence evidence, characterize RL applications in NTSC domains, explore all applied methods and major first events in the defined scope, and identify areas for further research based on the explored research problems in current research.

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“…We have reviewed a wide range of available tra c simulators in detail (inter alia [3], [5], [19], [8], [20], [6], [7], [21], [22], [23]). In particular, the AgentPolis approach stands out as it also reflects on the shortcomings of reusability in available simulation models.…”

Section: Related Workmentioning

confidence: 99%

Using Semantic Technology To Model Persona For Adaptable Agents

Nguyen¹,

Farrenkopf²,

Guckert³

et al. 2021

ECMS 2021 Proceedings Edited by Khalid Al-Begain, Mauro Iacono, Lelio Campanile, Andrzej Bargiela

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In state of the art research a growing interest in the application of agent models for the simulation of road traffic can be observed. Software agents are particularly suitable for the representation of travellers and their goal-oriented behaviour. Although numerous applications based on these types of models are already available, the options for modelling and calibration of the agents as goal-oriented individuals are either simplified to aggregated parameters or associated with overly complex and opaque implementation details. This makes it difficult to reuse available simulation models. In this paper, we demonstrate how the combination of persona models together with semantic methods can be applied to achieve a well-structured agent model that allows for improved reusability.

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MATISSE 3.0: A Large-Scale Multi-agent Simulation System for Intelligent Transportation Systems

Cited by 13 publications

References 2 publications

Solutions to the routing problem: towards trustworthy autonomous vehicles

Solutions to the routing problem: towards trustworthy autonomous vehicles

Reinforcement Learning in Urban Network Traffic Signal Control: A Systematic Literature Review

Using Semantic Technology To Model Persona For Adaptable Agents

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