Search Results for author:
Found 8 papers, 1 papers with code
Learning When to See for Long-term Traffic Data Collection on Power-constrained Devices
We introduce a novel learning-based framework that strategically decides observation timings for battery-powered devices and reconstructs the full data stream from sparsely sampled observations, resulting in minimal performance loss and a significantly prolonged system lifetime.
Physics-informed Machine Learning for Calibrating Macroscopic Traffic Flow Models
Our approach informs the decoder of the physical traffic flow models and thus induces the encoder to yield reasonable traffic parameters given flow and speed measurements.
Robust Queue Length Estimation for Ramp Metering in a Connected Vehicle Environment
Connected vehicles (CVs) can provide numerous new data via vehicle-to-vehicle or vehicle-to-infrastructure communication.
How Does Driver Non-compliance Destroy Traffic Routing Control?
For links admiting congestion propagation, we present one stability condition and one instability condition.
Agent-based Simulation Model and Deep Learning Techniques to Evaluate and Predict Transportation Trends around COVID-19
The COVID-19 pandemic has affected travel behaviors and transportation system operations, and cities are grappling with what policies can be effective for a phased reopening shaped by social distancing.
Using LDA and LSTM Models to Study Public Opinions and Critical Groups Towards Congestion Pricing in New York City through 2007 to 2019
This study explores how people view and respond to the proposals of NYC congestion pricing evolve in time.
An Interactive Data Visualization and Analytics Tool to Evaluate Mobility and Sociability Trends During COVID-19
The COVID-19 outbreak has dramatically changed travel behavior in affected cities.
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously.
