Evaluating performance of selected vehicle following models using trajectory data under mixed traffic conditions

Stability Analysis of Mixed Traffic Flow using Car-Following Models on Trajectory Data

“…Smoothening techniques are applied, and data is processed in MATLAB software, velocities, accelerations are computed. Further, leader-follower pairs are extracted based on logic given in [2]. In the second stage, the car-following models are identified and calibrated with trajectory data to replicate the driving behavior.…”

“…As a result, traffic tends to display weak lane discipline. The driving behavior studies [2,3] from mixed traffic demonstrates smaller vehicles' lateral behavior impacting mixed traffic performance. Simultaneously, given the variation in the vehicles' physical properties, earlier studies [4,5] reported the underperformance of automated traffic tools in monitoring mixed traffic conditions.…”

A Review of Car-Following Models and Modeling Tools for Human and Autonomous-Ready Driving Behaviors in Micro-Simulation

“…in which a is the maximum acceleration, b is the maximum deceleration, is the stopping distance, T is the minimum time headway, and 0 is the free speed. The above expression has been simplified in [39] and described as:…”

Traffic and granular flow: the role of data and technology in the understanding of particle dynamics

“…The previous three papers focus on data-driven approaches to capture different homogeneous traffic phenomena and to offer connected control measures to alleviate congestion and to improve safety. Raju et al (2019) attempted instead to analyze and model heterogeneous following behaviors with the presence of two-wheel/ three-wheel vehicles, cars, buses, trucks, and light-commercial vehicles (LCVs) sharing the roadway (i.e., a mixed environment). The authors utilize trajectory data extracted from videos monitoring Indian roadway sections (a Delhi-Gurgaon road segment and a Chennai urban arterial).…”

“…The seven final published papers are associated with vehicular traffic (i.e., motorized vehicles) that is lanebased with a focus on empirical characterization (D€ ulgar et al, 2019), data-driven microscopic control (Molzahn et al, 2019), and simulation (Wegerle et al, 2019). Two papers deal with heterogeneous traffic modeling that involves different types of vehicles: the first paper focuses on data extraction and analysis for calibration purposes (Raju et al, 2019) and the second paper formulates a traffic flow model for non-lane-based heterogeneous traffic (Gaddam & Rao, 2020). The final two papers deal with pedestrian traffic: the first paper focuses on predicting the movement of pedestrians using an AI model (Tordeux et al, 2019) while the second paper applies an automated video detection algorithm to capture highdensity pedestrian flow dynamics (Baqui et al, 2019).…”