Deep learning methods are being increasingly applied in transport studies, while the
methods require modellers to go through a try-and-error model tuning process particularly …

Trip purpose is crucial to travel behavior modeling and travel demand estimation for
transportation planning and investment decisions. However, the spatial-temporal complexity …

Although researchers increasingly adopt machine learning to model travel behavior, they
predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in …

The rail transit has difficulties in meeting daily travel needs of passengers owing to a large
population and accelerating urbanization. Analyzing urban travel behaviors with big data …

Nowadays, one of the most outstanding new urban transport model is the ridesharing
service, in which two or more users share a ride. This transport model reduces costs and the …

Federated learning (FL) is a promising technique for collaboratively training machine-
learning models on massively distributed clients data under privacy constraints. However …

The accurate depiction and understanding of the travel behavior characteristics of public
transport (PT) commuters is an important foundation for better improving PT service and …

Walking is the primary means of transportation. For assessing individual's health, travel
behaviour and benchmarking service levels of pedestrian infrastructure, walking/crossing …

Previous activity-based studies have shown that behavioural outcomes are the result of
complex and multidimensional processes. In this context, identifying and characterizing …

With the increase in the demand for and transportation of hazardous materials (Hazmat),
frequent Hazmat road transport accidents, high death tolls and property damage have …