Skip to main content
Top
Published in:

2024 | OriginalPaper | Chapter

1. Introduction

Author : HongSheng Qi

Published in: Stochastic Two-Dimensional Microscopic Traffic Model

Publisher: Springer Nature Singapore

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The understanding of vehicular movement has been a long-standing focal point in traffic flow theory. The modeling of such movement methodologies not only finds direct application in traffic analysis, but also holds substantial potential within the AVs industry, particularly as CAVs progressively infuse the system. This section firstly briefly describes the basics of microscopic traffic flow modeling concept, and then discuss the challenges brought by the autonomous vehicles. After that, literature review is conducted for car following and lane changing behaviors, which are major two models of microscopic traffic flow models. The drawbacks of current models are presented, together with the structure of the book.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literature
4.
go back to reference Diandian D, Sun L, Chen S (2017) A car-following model coupling machine learning and dynamic. J Transp Syst Eng Inf Technol 17(6):33–38 [In Chinese] Diandian D, Sun L, Chen S (2017) A car-following model coupling machine learning and dynamic. J Transp Syst Eng Inf Technol 17(6):33–38 [In Chinese]
24.
go back to reference Qin W, Ying L (2020) Combination low-speed car-following model based on IDM and RBFNN. Appl Res Comput 37(8):2354–2357. [In Chinese] Qin W, Ying L (2020) Combination low-speed car-following model based on IDM and RBFNN. Appl Res Comput 37(8):2354–2357. [In Chinese]
33.
go back to reference Xueming X, Jian R, Wang L (2007) Development of a car-following model based on combined neural network model. J Highw Transp Res Dev 3(132):130–132. [In Chinese] Xueming X, Jian R, Wang L (2007) Development of a car-following model based on combined neural network model. J Highw Transp Res Dev 3(132):130–132. [In Chinese]
35.
go back to reference Zhao J, Xiaoyu H, Zhixin Y, Mingmin H (2023) A combination model for connected and autonomous vehicles lane-changing decision-making under multi connectivity range. J Transp Syst Eng Inf Technol 23(1):77–85. [In Chinese] Zhao J, Xiaoyu H, Zhixin Y, Mingmin H (2023) A combination model for connected and autonomous vehicles lane-changing decision-making under multi connectivity range. J Transp Syst Eng Inf Technol 23(1):77–85. [In Chinese]
36.
go back to reference Zhao J, Lanxin J, Zhimin Z, Yunchao Q, Huijun S (2023) A car-following model driven by combination of theory and data considering effects of lane change of side cars. J South China Univ Technol (Natural Science Edition) 51(6):10–19. [In Chinese] Zhao J, Lanxin J, Zhimin Z, Yunchao Q, Huijun S (2023) A car-following model driven by combination of theory and data considering effects of lane change of side cars. J South China Univ Technol (Natural Science Edition) 51(6):10–19. [In Chinese]
39.
go back to reference Zhu M, Du S, Wang X et al (2022) TransFollower: long-sequence car-following trajectory prediction through transformer Zhu M, Du S, Wang X et al (2022) TransFollower: long-sequence car-following trajectory prediction through transformer
Metadata
Title
Introduction
Author
HongSheng Qi
Copyright Year
2024
Publisher
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-97-3597-6_1

Premium Partner