Open AccessField data application of a non‐lane‐based multi‐class traffic flow model
Author(s) -
Mohan Ranju,
Ramadurai Gitakrishnan
Publication year - 2020
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2019.0583
Subject(s) - bottleneck , field (mathematics) , traffic flow (computer networking) , outflow , computer science , section (typography) , class (philosophy) , block (permutation group theory) , flow (mathematics) , dissipation , microscopic traffic flow model , key (lock) , simulation , data mining , real time computing , traffic generation model , meteorology , artificial intelligence , geography , mathematics , computer network , physics , geometry , pure mathematics , thermodynamics , embedded system , operating system , computer security
Multi‐class traffic flow modelling has various approaches several of which have focused on analytical proofs. A key limitation in this field of research is the limited field data applications. This study proposes a speed‐gradient‐based multi‐class second‐order model and shows its application to three different road sections, a mid‐block section, a section with a bottleneck, and a section with a signal at the end, in Chennai, India. The model captures the congestion formation and dissipation phenomena well and could predict outflow and speed fluctuations generally observed in the field scenarios accurately. The prediction of traffic flow dynamics by the proposed model is also observed to be better when compared with two existing higher‐order multi‐class models.