Standard
An Event-Driven Multi Agent System for Scalable Traffic Optimization. / Horn, G.; Przeźdiȩk, T.
; Büscher, M. et al.
Workshops of the International Conference on Advanced Information Networking and Applications: WAINA 2020: Web, Artificial Intelligence and Network Applications. ed. / L. Barolli; F. Amato; F. Moscato; T. Enokido; M. Takizawa. Cham: Springer, 2020. p. 1373-1382 (Advances in Intelligent Systems and Computing; Vol. 1150).
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Harvard
Horn, G, Przeźdiȩk, T
, Büscher, M, Venticinque, S, Aversa, R, Martino, BD, Esposito, A, Skrzypek, P & Leznik, M 2020,
An Event-Driven Multi Agent System for Scalable Traffic Optimization. in L Barolli, F Amato, F Moscato, T Enokido & M Takizawa (eds),
Workshops of the International Conference on Advanced Information Networking and Applications: WAINA 2020: Web, Artificial Intelligence and Network Applications. Advances in Intelligent Systems and Computing, vol. 1150, Springer, Cham, pp. 1373-1382.
https://doi.org/10.1007/978-3-030-44038-1_125APA
Horn, G., Przeźdiȩk, T.
, Büscher, M., Venticinque, S., Aversa, R., Martino, B. D., Esposito, A., Skrzypek, P., & Leznik, M. (2020).
An Event-Driven Multi Agent System for Scalable Traffic Optimization. In L. Barolli, F. Amato, F. Moscato, T. Enokido, & M. Takizawa (Eds.),
Workshops of the International Conference on Advanced Information Networking and Applications: WAINA 2020: Web, Artificial Intelligence and Network Applications (pp. 1373-1382). (Advances in Intelligent Systems and Computing; Vol. 1150). Springer.
https://doi.org/10.1007/978-3-030-44038-1_125Vancouver
Horn G, Przeźdiȩk T
, Büscher M, Venticinque S, Aversa R, Martino BD et al.
An Event-Driven Multi Agent System for Scalable Traffic Optimization. In Barolli L, Amato F, Moscato F, Enokido T, Takizawa M, editors, Workshops of the International Conference on Advanced Information Networking and Applications: WAINA 2020: Web, Artificial Intelligence and Network Applications. Cham: Springer. 2020. p. 1373-1382. (Advances in Intelligent Systems and Computing). Epub 2020 Mar 31. doi: 10.1007/978-3-030-44038-1_125
Author
Horn, G. ; Przeźdiȩk, T.
; Büscher, M. et al. /
An Event-Driven Multi Agent System for Scalable Traffic Optimization. Workshops of the International Conference on Advanced Information Networking and Applications: WAINA 2020: Web, Artificial Intelligence and Network Applications. editor / L. Barolli ; F. Amato ; F. Moscato ; T. Enokido ; M. Takizawa. Cham : Springer, 2020. pp. 1373-1382 (Advances in Intelligent Systems and Computing).
Bibtex
@inproceedings{ee5a2f98a7d34fedaf50f734de391941,
title = "An Event-Driven Multi Agent System for Scalable Traffic Optimization",
abstract = "Global demand for mobility will grow from 44 trillion to 122 trillion passenger-kilometres by 2050, and freight demand will triple in that time increasing traffic emissions by 60%. With current innovation and policy measures we are {\textquoteleft}on course for a 3.2C temperature rise{\textquoteright}, according to the 2019 UN Emissions Gap Report. Nothing short of revolutionary is required to address this emergency. However, there is hope: shared mobility and widespread adoption of autonomous vehicles could cut emissions by 73% and congestion by 24% if managed by appropriate policies. This paper presents a vision and a concept for future distributed management systems for complex multi-modal transport networks that exploit Multi Agent Systems (MAS) to support individual actors based on data collected from heterogeneous sources like vehicles, freight items, infrastructures, Global Positioning Systems (GPS); and simulations of the behaviour of the many different actors involved in the transport system. Event driven approaches are envisioned to react and respond to real-time events efficiently. The main objective is to identify the best optimization strategies to reduce traffic emissions and maximize the use of the public infrastructures and shared mobility. Motivations, expected impacts, and challenges are also discussed.",
keywords = "Future mobility, Transport system management, Multi Agent System, Event driven simulation",
author = "G. Horn and T. Prze{\'z}diȩk and M. B{\"u}scher and S. Venticinque and R. Aversa and B.D. Martino and A. Esposito and P. Skrzypek and M. Leznik",
year = "2020",
month = apr,
day = "15",
doi = "10.1007/978-3-030-44038-1_125",
language = "English",
isbn = "9783030440374",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer",
pages = "1373--1382",
editor = "L. Barolli and F. Amato and F. Moscato and T. Enokido and M. Takizawa",
booktitle = "Workshops of the International Conference on Advanced Information Networking and Applications",
}
RIS
TY - GEN
T1 - An Event-Driven Multi Agent System for Scalable Traffic Optimization
AU - Horn, G.
AU - Przeźdiȩk, T.
AU - Büscher, M.
AU - Venticinque, S.
AU - Aversa, R.
AU - Martino, B.D.
AU - Esposito, A.
AU - Skrzypek, P.
AU - Leznik, M.
PY - 2020/4/15
Y1 - 2020/4/15
N2 - Global demand for mobility will grow from 44 trillion to 122 trillion passenger-kilometres by 2050, and freight demand will triple in that time increasing traffic emissions by 60%. With current innovation and policy measures we are ‘on course for a 3.2C temperature rise’, according to the 2019 UN Emissions Gap Report. Nothing short of revolutionary is required to address this emergency. However, there is hope: shared mobility and widespread adoption of autonomous vehicles could cut emissions by 73% and congestion by 24% if managed by appropriate policies. This paper presents a vision and a concept for future distributed management systems for complex multi-modal transport networks that exploit Multi Agent Systems (MAS) to support individual actors based on data collected from heterogeneous sources like vehicles, freight items, infrastructures, Global Positioning Systems (GPS); and simulations of the behaviour of the many different actors involved in the transport system. Event driven approaches are envisioned to react and respond to real-time events efficiently. The main objective is to identify the best optimization strategies to reduce traffic emissions and maximize the use of the public infrastructures and shared mobility. Motivations, expected impacts, and challenges are also discussed.
AB - Global demand for mobility will grow from 44 trillion to 122 trillion passenger-kilometres by 2050, and freight demand will triple in that time increasing traffic emissions by 60%. With current innovation and policy measures we are ‘on course for a 3.2C temperature rise’, according to the 2019 UN Emissions Gap Report. Nothing short of revolutionary is required to address this emergency. However, there is hope: shared mobility and widespread adoption of autonomous vehicles could cut emissions by 73% and congestion by 24% if managed by appropriate policies. This paper presents a vision and a concept for future distributed management systems for complex multi-modal transport networks that exploit Multi Agent Systems (MAS) to support individual actors based on data collected from heterogeneous sources like vehicles, freight items, infrastructures, Global Positioning Systems (GPS); and simulations of the behaviour of the many different actors involved in the transport system. Event driven approaches are envisioned to react and respond to real-time events efficiently. The main objective is to identify the best optimization strategies to reduce traffic emissions and maximize the use of the public infrastructures and shared mobility. Motivations, expected impacts, and challenges are also discussed.
KW - Future mobility
KW - Transport system management
KW - Multi Agent System
KW - Event driven simulation
U2 - 10.1007/978-3-030-44038-1_125
DO - 10.1007/978-3-030-44038-1_125
M3 - Conference contribution/Paper
SN - 9783030440374
T3 - Advances in Intelligent Systems and Computing
SP - 1373
EP - 1382
BT - Workshops of the International Conference on Advanced Information Networking and Applications
A2 - Barolli, L.
A2 - Amato, F.
A2 - Moscato, F.
A2 - Enokido, T.
A2 - Takizawa, M.
PB - Springer
CY - Cham
ER -
