  Most car-following models were originally developed for lane-based traffic.
Over the past two decades, efforts have been made to calibrate car-following
models for non-lane-based traffic. However, traffic conditions with varying
vehicle dimensions, intermittent following, and multiple leaders often occur
and make subjective Leader-Follower (LF) pair identification challenging. In
this study, we analyze Vehicle Following (VF) behavior in traffic with a lack
of lane discipline using high-resolution microscopic trajectory data collected
in Chennai, India. The paper's main contributions are threefold. Firstly, three
criteria are used to identify LF pairs from the driver's perspective, taking
into account the intermittent following, lack of lane discipline due to
consideration of lateral separation, and the presence of in-between vehicles.
Second, the psycho-physical concept of the regime in the Wiedemann-99 model is
leveraged to determine the traffic-dependent "influence zone" for LF
identification. Third, a joint and consistent framework is proposed for
identifying LF pairs and estimating VF parameters. The proposed methodology
outperforms other heuristic-based LF identification methods from the literature
in terms of quantitative and qualitative performance measures. The proposed
approach can enable robust and more realistic LF identification and VF
parameter calibration with practical applications such as LOS analysis,
capacity, and travel time estimation.
