Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

被引:2
作者
Hollosi, Gergely [1 ]
Lukovszki, Csaba [1 ]
Bancsics, Mate [1 ]
Magyar, Gabor [1 ]
机构
[1] Budapest Univ Technol & Econ, Dept Telecommun & Media Informat, Budapest, Hungary
来源
INFOCOMMUNICATIONS JOURNAL | 2021年 / 13卷 / 04期
关键词
traffic swarm; traffic behaviour; behaviour analysis; game theory; machine learning; deep learning; MODEL; FRAMEWORK; EVOLUTION; STATES;
D O I
10.36244/ICJ.2021.4.3
中图分类号
TN [电子技术、通信技术];
学科分类号
0809 ;
摘要
High density traffic on highways and city streets consists of endless interactions among participants. These interactions and the corresponding behaviours have great impact not only on the throughput of traffic but also on safety, comfort and economy. Because of this, there is a great interest in deeper understanding of these interactions and concluding the impacts on traffic participants. This paper explores and maps the world of traffic behaviour analysis, especially researches focusing on groups of vehicles called traffic swarm, while presents the state-of-the-art methods and algorithms. The conclusion of this paper states that there are special areas of traffic behaviour analysis which have great research potential in the near future to describe traffic behaviour in more detail than present methods.
引用
收藏
页码:19 / 27
页数:9
相关论文
共 50 条
  • [41] Food products pricing theory with application of machine learning and game theory approach
    Mamoudan, Mobina Mousapour
    MohammadNazari, Zahra
    Ostadi, Ali
    Esfahbodi, Ali
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2024, 62 (15) : 5489 - 5509
  • [42] Equitable Valuation of Crowdsensing for Machine Learning via Game Theory
    He, Qiangqiang
    Qiao, Yu
    Yang, Shang
    Wang, Chongjun
    WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS, WASA 2021, PT III, 2021, 12939 : 133 - 141
  • [43] Joint Machine Learning and Game Theory for Rate Control in High Efficiency Video Coding
    Gao, Wei
    Kwong, Sam
    Jia, Yuheng
    IEEE TRANSACTIONS ON IMAGE PROCESSING, 2017, 26 (12) : 6074 - 6089
  • [44] Prediction of pedestrian crossing behaviour at unsignalized intersections using machine learning algorithms: analysis and comparison
    Singh, Dungar
    Das, Pritikana
    Ghosh, Indrajit
    JOURNAL ON MULTIMODAL USER INTERFACES, 2024, 18 (2-3) : 239 - 256
  • [45] Analysis of the robustness of railway traffic management to driving behaviour noise
    David, Balraj
    Pascariu, Bianca
    Pellegrini, Paola
    Marliere, Gregory
    2023 8TH INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS, MT-ITS, 2023,
  • [46] A Review on Text Sentiment Analysis With Machine Learning and Deep Learning Techniques
    Mamani-Coaquira, Yonatan
    Villanueva, Edwin
    IEEE ACCESS, 2024, 12 : 193115 - 193130
  • [47] Research on the mobility behaviour of Chinese construction workers based on evolutionary game theory
    Sun Jide
    Wang Xincheng
    Shen Liangfa
    ECONOMIC RESEARCH-EKONOMSKA ISTRAZIVANJA, 2018, 31 (01): : 1 - 14
  • [48] Balanced hydropower and ecological benefits in reservoir-river-lake system: An integrated framework with machine learning and game theory
    Liu, Shuangjun
    Fu, Xiang
    Li, Yu
    Chu, Xuefeng
    JOURNAL OF ENVIRONMENTAL MANAGEMENT, 2025, 373
  • [49] A Machine Learning Based Approach to Human Observer Behaviour Analysis in CCTV Video Analytics & Forensics
    Al Raisi, Seema F.
    Edirisinghe, Eran
    PROCEEDINGS OF THE 1ST INTERNATIONAL CONFERENCE ON INTERNET OF THINGS AND MACHINE LEARNING (IML'17), 2017,
  • [50] Concrete Creep Prediction Based on Improved Machine Learning and Game Theory: Modeling and Analysis Methods
    Li, Wenchao
    Li, Houmin
    Liu, Cai
    Min, Kai
    BUILDINGS, 2024, 14 (11)