A vehicle type-dependent visual imaging model for analysing the heterogeneous car-following dynamics

Liang Zheng, Peter J. Jin, Helai Huang, Mingyun Gao, Bin Ran

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


Heterogeneity is an essential characteristic in car-following behaviours, which can be defined as the differences between the car-following behaviours of driver/vehicle combination under comparable conditions. This paper proposes a visual imaging model (VIM) with relaxed assumption on (1) a driver's perfect perception for the states of the neighbouring vehicles (e.g. spacing, velocity, etc.) and (2) uniform reaction to vehicles with different sizes in most existing car-following models. VIM utilises the visual imaging information subtended by the preceding vehicle as the stimuli drivers react to, and can generate greater stimuli from the preceding vehicle with larger apparent size (i.e. vehicle width × vehicle height) under short gap distance with the follower, but less change in stimuli from the distant leading vehicle under various apparent sizes. The NGSIM data containing vehicle type/size information is used to evaluate VIM at different levels. At the level of single trajectory pair, the calibrated VIM occupies the well capability of reproducing the trajectory of the follower, and can also reproduce statistical results from the field data, that is, the gap distance for car-following truck (C-T) is greater than that for car-following car (C-C). At the level of vehicle type, the calibration results also show the promising performance of VIM in describing heterogeneous car-following behaviours with the simple model formulation and limited model parameters compared with other six reference models.

Original languageEnglish (US)
Pages (from-to)68-85
Number of pages18
JournalTransportmetrica B
Issue number1
StatePublished - Jan 2 2016

All Science Journal Classification (ASJC) codes

  • Software
  • Modeling and Simulation
  • Transportation


  • NGSIM data
  • car-following model
  • heterogeneity
  • visual imaging model

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