A survey on traffic flow prediction and classification

被引:5
作者
Gomes, Bernardo [1 ]
Coelho, Jose [1 ,2 ]
Aidos, Helena [1 ]
机构
[1] Univ Lisbon, Fac Ciencias, Dept Informat, LASIGE, Lisbon, Portugal
[2] Estoril Higher Inst Tourism & Hotel Studies, ESHTE, Cascais, Portugal
来源
INTELLIGENT SYSTEMS WITH APPLICATIONS | 2023年 / 20卷
关键词
Road traffic; Prediction; Classification; Europe traffic flow;
D O I
10.1016/j.iswa.2023.200268
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As cities continue to grow and the number of vehicles on the road increases, traffic congestion and pollution have become major issues. Fortunately, significant efforts have been made in recent decades to alleviate these problems through research and the development of Intelligent Transportation Systems (ITS). Governments are now utilizing advanced ITS technologies to better understand traffic patterns and make informed decisions on how to manage traffic. In this paper, we will explore the state-of-the-art methods employed in ITS for predicting traffic flow and speed, as well as classifying different traffic situations. We will also examine the preprocessing techniques used in these tasks, along with the metrics used to evaluate the results. By understanding the latest advancements in ITS, we can work towards creating more efficient and sustainable transportation systems that benefit both individuals and society as a whole.
引用
收藏
页数:13
相关论文
共 50 条
  • [31] Research on prediction of traffic flow based on dynamic fuzzy neural networks
    Li, Haitao
    NEURAL COMPUTING & APPLICATIONS, 2016, 27 (07) : 1969 - 1980
  • [32] Prediction for traffic flow of BP neural network based on DE algorithm
    Hou Yue
    Li Haiyan
    CONSTRUCTION AND URBAN PLANNING, PTS 1-4, 2013, 671-674 : 2951 - +
  • [33] Prediction Model for Ship Traffic Flow Considering Periodic Fluctuation Factors
    Wan Jianxia
    Li Jing
    Zhang Shukui
    PROCEEDINGS OF 2018 IEEE 3RD ADVANCED INFORMATION TECHNOLOGY, ELECTRONIC AND AUTOMATION CONTROL CONFERENCE (IAEAC 2018), 2018, : 1506 - 1510
  • [34] A methodology using classification for traffic prediction: Featuring the impact of COVID-19
    Liapis, Stergios
    Christantonis, Konstantinos
    Chazan-Pantzalis, Victor
    Manos, Anastassios
    Filippidou, Despina Elizabeth
    Tjortjis, Christos
    INTEGRATED COMPUTER-AIDED ENGINEERING, 2021, 28 (04) : 417 - 435
  • [35] Investigation and Prediction of Traffic Flow in Holidays in Zhejiang Section of Shenhai Freeway
    Dai, Hongliang
    Liu, Qinglin
    Wang, Fujian
    Gong, Chengyu
    3RD INTERNATIONAL CONFERENCE ON TRANSPORTATION INFORMATION AND SAFETY (ICTIS 2015), 2015, : 195 - 201
  • [36] A novel iterated multi-step prediction method of traffic flow
    Zhu, Zhengyu
    Guo, Chongxiao
    Liu, Lin
    Journal of Information and Computational Science, 2014, 11 (08): : 2569 - 2584
  • [37] Traffic Flow Prediction via Weighted Combination of ARIMA and WASDNN Models
    Liu, Xiao
    Zhang, Yunong
    Yang, Min
    Xue, Zhongxian
    Ye, Chengxu
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 604 - 611
  • [38] Short-term real-time traffic prediction methods: a survey
    Barros, Joaquim
    Araujo, Miguel
    Rossetti, Rosaldo J. F.
    2015 INTERNATIONAL CONFERENCE ON MODELS AND TECHNOLOGIES FOR INTELLIGENT TRANSPORTATION SYSTEMS (MT-ITS), 2015, : 132 - 139
  • [39] Survey of classification algorithms for formulating yield prediction accuracy in precision agriculture
    Savla, Anshal
    Dhawan, Parul
    Bhadada, Himtanaya
    Israni, Nivedita
    Mandholia, Alisha
    Bhardwaj, Sanya
    2015 INTERNATIONAL CONFERENCE ON INNOVATIONS IN INFORMATION, EMBEDDED AND COMMUNICATION SYSTEMS (ICIIECS), 2015,
  • [40] Data mining and machine learning methods for sustainable smart cities traffic classification: A survey
    Shafiq, Survey Muhammad
    Tian, Zhihong
    Bashir, Ali Kashif
    Jolfaei, Alireza
    Yu, Xiangzhan
    SUSTAINABLE CITIES AND SOCIETY, 2020, 60