arXiv subjects

Bhargava Rama Chilukuri

Publications and source records attributed to Bhargava Rama Chilukuri.

5 recordsLinked to original sources

arXiv2026

Temporal Boundaries of Newell's Car-Following Model: Insights from Lane-Free Traffic

Newell's simplified car-following model offers behavioral interpretability with minimal parameters, yet the temporal limits within which it holds during actual car-following interactions, particularly in lane-free traffic, remain poorly understood. This has direct consequences for Newell parameter estimation. This study evaluates the model using high-resolution UAV trajectories from a lane-free highway corridor in Chennai, applying two independent estimation approaches: linear regression of the speed-spacing relationship (aggregate and pair specific), and a shifting optimization method that recovers parameters by minimizing spacing variance. A boundary-corrected variant of the shifting method is then introduced to isolate transitional regimes at interaction endpoints while preserving the original Newell formulation. Pair-specific regression substantially outperforms the aggregate specification ($R^2$ = 0.84 vs. 0.54), underscoring pronounced driver heterogeneity. Parameters recovered via trajectory shifting are statistically equivalent to regression estimates, providing independent support for the trajectory translation principle underlying Newell's model. Boundary correction markedly improves model fit, increasing the mean $\bar{R^2}$ from 0.66 to 0.95, and is preferred for 81% of pairs according to the Bayesian Information Criterion (BIC). The results indicate that interaction boundary effects, rather than failures of the core behavioral assumptions, are the principal source of estimation error in this setting. Correcting for these effects improves parameter recovery without compromising model parsimony. Boundary correction is therefore a necessary step in trajectory-based calibration of Newell-type models, with implications for traffic state estimation and microscopic traffic simulation.

physics.soc-ph↗

arXiv2025

Leader-Follower Identification Methodology for Non-Lane Disciplined Heterogeneous Traffic Using Steady State Features

Road traffic in developing countries, such as India, features a heterogeneous mix of vehicles operating under weak lane discipline (HWLD), encompassing both motorised and non-motorised modes with diverse sizes and manoeuvrability. These conditions lead to complex driver interactions, complicating the reliable identification of vehicle-following (VF) behaviour and leader-follower (LF) pairs. Traditional identification methods based on fixed thresholds for longitudinal and lateral proximity often misclassify non-following instances as valid LF pairs, degrading model performance. This study presents a refined and adaptive method for LF identification in HWLD traffic. It employs vehicle-type- and speed-specific desirable gap thresholds derived from the fundamental density-speed diagram to eliminate false-positive pairs. Additionally, Mexican Hat Wavelet Transform (MWT) is employed to analyse LV and SV speed profiles, verifying LV-SV interaction for LF pair identification. The three-stage filtering includes: (i) speed-gap consistency, (ii) approach/diverge detection via relative velocity sign changes and gap range, and (iii) wavelet-based speed correlation using MWT to confirm LV influence on SV. The framework effectively filters out LF pairs associated with overtaking, tailgating, and inconsistent gap dynamics, retaining only those with consistent VF behaviour and improving model accuracy. Analysis across thirteen LF combinations shows that VF dynamics depend on both SV and LV types. Symmetric pairs (e.g., CAR-CAR, AUTO-CAR) exhibit higher predictability and lower errors, while asymmetric pairs with heavy vehicles or two-wheelers show greater variability. The framework offers a robust foundation for traffic modelling and behaviour analysis.

stat.ME↗

arXiv2024

An areal continuum model for mixed traffic

A novel continuum model has been developed to address the vehicle size heterogeneity in mixed traffic. By incorporating the principle of vehicle area conservation, a new set of traffic flow variables centered on the concept of vehicle area has been introduced. These variables, namely areal flow and areal density, exhibit the remarkable characteristic of being conserved across time and space and seamlessly related to the traditional space mean speed. Next, the study successfully established a bi-variate relationship among these newly introduced area-based traffic variables based on empirical data from three locations for stream and mode-specific. It is shown that the conventional solution methods for the hyperbolic conservation laws remain applicable to the proposed continuum model. Finally, a multi-class cell transmission model numerical scheme is developed and used to illustrate the performance of the proposed model in replicating the seepage and platoon dispersion behavior in mixed traffic conditions.

math.AP↗

arXiv2024

An Extended AW-Rascle Model with Source Terms and Its Numerical Solution

Nonlinear hyperbolic partial differential equations govern continuum traffic flow models. Higher-order traffic flow models consisting of continuum equations and velocity dynamics were introduced to address the limitations of the Lighthill, Whitham, and Richards (LWR) model. However, these models are ineffective in incorporating road heterogeneity. This paper integrates an extended AW-Rascle higher-order model with the source terms in the continuum equation to predict the traffic states in heterogeneous road conditions. The system of the equations was solved numerically with the central dispersion (CD) method incorporated into the standard McCormack scheme. Smoothing is applied to take care of the numerical oscillation of the higher-order model. Different combinations of initial conditions with source terms showed that the proposed model with the numerical methods could produce a stable solution and eliminate oscillation of the McCormack scheme.

math.AP↗

arXiv2024

Leader-Follower Identification with Vehicle-Following Calibration for Non-Lane-Based Traffic

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.

physics.soc-ph↗