The effectiveness of traditional traffic prediction methods, such as autoregressive or spatio-
temporal models, is often extremely limited when forecasting traffic dynamics in early …

Efficient transportation planning and management are critical for ensuring the smooth
operation of rail transit systems, particularly in urban areas with high passenger demand …

Strategic traffic management is crucial for combating traffic congestion at the macroscopic
level. However, such a field is still relatively unexplored, particularly for microscopic control …

Non-recurrent conditions caused by incidents are different from recurrent conditions that
follow periodic patterns. Existing traffic speed prediction studies are incident-agnostic and …

Demand flexibility is increasingly important for power grids, in light of growing penetration of
renewable generation. Careful coordination of thermostatically controlled loads (TCLs) can …

Traditional network models encapsulate travel behavior among all origin–destination pairs
based on a simplified and generic travelers' utility function. Typically, the utility function …

The main question to address in this paper is to recommend optimal signal timing plans in
real time under incidents by incorporating domain knowledge developed with the traffic …

Conventional automatic incident detection (AID) has relied heavily on all incident reports
exclusively for training and evaluation. However, these reports suffer from a number of …

Non-recurrent congestion is typically caused by traffic incidents in urban networks,
particularly when a valid Traffic Incident Management (TIM) strategy is lacking. This paper …

Artificial Intelligence (AI), including Machine Learning (ML), offers the opportunity to make
transportation systems safer, and more equitable, reliable, accessible, secure, efficient, and …