While people with disabilities have different travel patterns compared with the general
traveller population, such discrepancies are ignored in mainstream travel demand modelling …

Real-time traffic state (eg, speed) prediction is an essential component for traffic control and
management in an urban road network. How to build an effective large-scale traffic state …

Traffic flow forecasting is of great significance to urban traffic control and public safety
applications. The key challenge of traffic flow forecasting is how to capture the complex …

Incident detection is fundamental for freeway management to reduce non-recurrent
congestions and secondary incidents. Recently, machine learning technologies have made …

Researchers have improved travel demand forecasting methods in recent decades but
invested relatively little to understand their accuracy. A major barrier has been the lack of …

Deep-learning-based data-driven forecasting methods have achieved impressive results for
traffic forecasting. Specifically, spatiotemporal graph neural networks have emerged as a …

Deep neural networks (DNNs) have emerged as a dominant approach for develo** traffic
forecasting models. These models are typically trained to minimize error on averaged test …

Deep-learning-based data-driven forecasting methods have produced impressive results for
traffic forecasting. A major limitation of these methods, however, is that they provide …

The enhancement of flexibility, energy efficiency, and environmental friendliness constitutes
a widely acknowledged trend in the development of urban infrastructure. The proliferation of …

Preliminary feasibility studies (PFSs) have become Korea's unique budgetary and fiscal
management system. With a critical role in decisions regarding the viability of a proposed …