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 …

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

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 …

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 …

It is well-known that system performance can experience significant degradation from the
self-interested choices of human users. Accordingly, in this article, we study the question of …

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 …

Historically, forecasts of travel demand on public transit infrastructures have been found to
be optimistically biased. However, there has been a lack of data available for statistically …

Accurate travel demand forecasting is crucial before finalizing the decision to implement a
public–private partnership (PPP). While government needs to determine the government …

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