Including heavy vehicles in a car-following model: modelling, calibrating and validating

Kayvan Aghabayk, Majid Sarvi, William Young

Research output: Contribution to journalArticleResearchpeer-review

4 Citations (Scopus)

Abstract

Heavy vehicles influence general traffic in many different ways compared with passenger vehicles, and this may result in different levels of traffic instability. Increases in the number and proportion of heavy vehicles in the traffic stream will therefore result in different traffic flow conditions. This research initially outlines the different car-following behaviour of drivers in congested heterogeneous traffic conditions indicating the necessity for developing a car-following model, which includes these differences. A psychophysical car-following model, similar in form to Weideman's car-following model, was developed. Due to the complexity of the developed model, the calibration of the model was undertaken using a particle swarm optimisation algorithm with the data recorded under congested traffic conditions. This was then incorporated into a traffic microsimulation model. The results showed that the car-following perceptual thresholds and thus action points of drivers differ based on their vehicle and the lead vehicle types. The inclusion of the heavy vehicles in the model showed significant impacts on the traffic dynamic and interactions amongst different vehicles.

Original languageEnglish
Pages (from-to)1432-1446
Number of pages15
JournalJournal of Advanced Transportation
Volume50
Issue number7
DOIs
Publication statusPublished - 1 Nov 2016

Keywords

  • auto calibration
  • evolutionary algorithm
  • heavy vehicle car-following
  • heterogeneous traffic
  • particle swarm optimisation algorithm
  • traffic microsimulation

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