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Research Interest
Research Interest
- Traffic Flow Theory
- Crowd Dynamics
- Connected and Autonomous Vehicles
Macroscopic First order model:
Macroscopic First order model:
While macroscopic models for single or multi-lane traffic flow are well established, these models are not applicable to the dynamics and characteristics of disordered traffic which is characterized by widely different types of vehicles and no lane discipline. We propose a first-order two-dimensional Lighthill–Whitham–Richards (LWR) model for the continuous macroscopic longitudinal and lateral dynamics of this type of traffic flow. The continuity equation is extended into two dimensions and the equation is closed by assuming a longitudinal flow-density relationship as in traditional one-dimensional models while the lateral dynamics is based on boundary repulsion and a desire of a majority of the drivers to go to less dense regions. This is equivalent to Fick’s law giving rise to a lateral diffusion term. Using the proposed model, several numerical tests were conducted under different traffic scenarios representing a wide range of traffic conditions. Even for extreme initial conditions, the model’s outcome turned out to be plausible and consistent with observed traffic flow dynamics. Moreover, the numerical convergence test is performed using an analytical solution for lateral steady-state conditions. The model was applied for bicycle simulation and reproduced the evolution of lateral density profile with asymmetric behavior.
While macroscopic models for single or multi-lane traffic flow are well established, these models are not applicable to the dynamics and characteristics of disordered traffic which is characterized by widely different types of vehicles and no lane discipline. We propose a first-order two-dimensional Lighthill–Whitham–Richards (LWR) model for the continuous macroscopic longitudinal and lateral dynamics of this type of traffic flow. The continuity equation is extended into two dimensions and the equation is closed by assuming a longitudinal flow-density relationship as in traditional one-dimensional models while the lateral dynamics is based on boundary repulsion and a desire of a majority of the drivers to go to less dense regions. This is equivalent to Fick’s law giving rise to a lateral diffusion term. Using the proposed model, several numerical tests were conducted under different traffic scenarios representing a wide range of traffic conditions. Even for extreme initial conditions, the model’s outcome turned out to be plausible and consistent with observed traffic flow dynamics. Moreover, the numerical convergence test is performed using an analytical solution for lateral steady-state conditions. The model was applied for bicycle simulation and reproduced the evolution of lateral density profile with asymmetric behavior.
CastaShockwave.aviTrajectory Data
Trajectory Data
This research presents the design and preliminary results of a first-of-its-kind experiment, to create a trajectory dataset for an urban arterial road (Chennai city, India) under disordered traffic conditions using by flying a swarm of six Unmanned Aerial Vehicles (UAVs). The sequential steps followed to obtain the detailed trajectory data from UAV traffic videos are 1) Geo-registration, 2) Image Stabilization and Stitching, 3) Data Annotation, 4) Vehicle Detection and Classification, 5) Vehicle Tracking, and 6) Trajectory Smoothing. Finally, to remove the noises and disturbances from the trajectory dataset, a Symmetric Exponential Moving Average (sEMA) filter technique was applied to smooth the positions of the vehicles and the first and second derivate of the positions, i.e., speeds and accelerations were obtained using central difference method. The trajectory dataset was used to analyze the lateral position preferences of vehicles and the results highlight the unique behavior of different categories of vehicles under disordered traffic conditions. This dataset is a scale of an order of magnitude higher than existing datasets, which can be used to calibrate and validate the following models and lane-changing models in disordered traffic.
Logitudinal Dynamics in Disordered Traffic
Logitudinal Dynamics in Disordered Traffic
Calibration of those models is necessary to evaluate their predictive power and suitability for analyzing traffic flow under disordered traffic. The present study aims to calibrate a longitudinal dynamics model, High-Speed Social-Force Model (HSFM) using a vehicle trajectory dataset collected from Chennai city. The HSFM was calibrated by minimizing the deviations between the simulated and observed longitudinal coordinates of vehicles using a genetic algorithm. The observed and simulated vehicle trajectories were compared using a goodness of fit function of the positions. The convergence of the objective function has been illustrated with the help of fitness landscapes. The calibration errors were found to be within the acceptable range and the optimal parameter values were found to be consistent. The outcomes of the study indicate that the model can capture the influence of non-overlapping leaders under disordered traffic conditions.
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