Rainfall-runoff forecasting is expected to play a crucial role in hydrology. In recent years,
machine learning models have been found to be effective in runoff simulation, and …

While deep neural networks (DNNs) have been increasingly applied to choice analysis
showing high predictive power, it is unclear to what extent researchers can interpret …

This paper proposes a decentralized network-level traffic signal control method addressing
the effects of queue spillbacks. The method is traffic-responsive, does not require data …

Proactive evacuation traffic management largely depends on real-time monitoring and
prediction of traffic flow at a high spatiotemporal resolution. However, evacuation traffic …

Reliable and accurate traffic flow prediction is crucial for the construction and operation of
smart highways, supporting scientific traffic management and planning. However, accurately …

Perimeter traffic flow control based on the macroscopic or network fundamental diagram
provides the opportunity of operating an urban traffic network at its capacity. Because …

Researchers often treat data-driven and theory-driven models as two disparate or even
conflicting methods in travel behavior analysis. However, the two methods are highly …

We present a novel data-driven approach of learning traffic flow patterns of a transportation
network given that many instances of origin to destination (OD) travel demand and link flows …

In recent years, deep learning as a subfield of machine learning has gained increasing
attention due to its potential advantages in empowering autonomous systems with the ability …

The growing concerns surrounding water supply, driven by factors such as population
growth and industrialization, have highlighted the need for accurate estimation of streamflow …