An efficient variational Bayesian algorithm for calibrating fundamental diagrams and its probabilistic sensitivity analysis
Fundamental diagrams (FDs) are the basis of traffic flow theory. Efficient model calibration
from noisy traffic data is essential to identify the parameters of FDs to describe the traffic flow …
from noisy traffic data is essential to identify the parameters of FDs to describe the traffic flow …
Learn from one and predict all: single trajectory learning for time delay systems
This paper focuses on learning the dynamics of time delay systems from trajectory data and
proposes the use of the maximal Lyapunov exponent (MLE) as an indicator to describe the …
proposes the use of the maximal Lyapunov exponent (MLE) as an indicator to describe the …
Modeling multimodal curbside usage in dynamic networks
The proliferation of emerging mobility technology has led to a significant increase in demand
for ride-hailing services, on-demand deliveries, and micromobility services, transforming …
for ride-hailing services, on-demand deliveries, and micromobility services, transforming …
Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach
The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic
flow, density, and speed at the network level. It is a simple yet powerful tool for modeling …
flow, density, and speed at the network level. It is a simple yet powerful tool for modeling …
Learning time delay systems with neural ordinary differential equations
A novel way of using neural networks to learn the dynamics of time delay systems from
sequential data is proposed. A neural network with trainable delays is used to approximate …
sequential data is proposed. A neural network with trainable delays is used to approximate …