Traffic forecasting has been regarded as the basis for many intelligent transportation system
(ITS) applications, including but not limited to trip planning, road traffic control, and vehicle …

Abstract Short-term Electric Vehicle (EV) charging demand prediction is an essential task in
the fields of smart grid and intelligent transportation systems, as understanding the …

In recent years, bike-sharing systems (BSS) are being widely established in urban cities to
provide a sustainable mode of transport, by fulfilling the mobility requirements of public …

This paper investigates bike-share activities and explores their relationships with
neighborhood features, advancing our current knowledge for integrating cycle facilities into …

Bike-sharing is a powerful solution to urban challenges (eg, expanding bike communities,
lowering transportation costs, alleviating traffic congestion, reducing emissions, and …

The shared electric scooter (E-scooter) is an emerging micro-mobility mode in sustainable
cities. Accurate hourly trip demand prediction is critical for effective service maintenance, but …

In this paper the performance of different Machine Learning and Deep Learning approaches
is evaluated in problems related to green mobility in big cities. Specifically, the forecasting of …

Understanding the travel flux between urban blocks is fundamental for traffic demand
prediction, urban area planning and urban traffic management. However, the uncertainty of …

Bike-sharing systems can definitely contribute to the achievement of sustainable urban
mobility. In spite of this potential, their planning and operation are not free of difficulties. The …

The mining of diverse patterns from bike flow has attracted widespread interest from
researchers and practitioners. Prior arts concentrate on forecasting the flow evolution from …