The COVID-19 pandemic continues to have a significant impact on the transport sector
worldwide. Lockdown and physical distancing requirements continue to be enforced in …

This paper presents the development and evaluation of short‐term traffic prediction models
using unidirectional and bidirectional deep learning long short‐term memory (LSTM) neural …

Autonomous spatial navigation (ASN) is a challenge for millions of disabled people globally,
especially persons with visual impairment (PwVI) who depend on either caregivers or …

Long short-term memory (LSTM) models provide high predictive performance through their
ability to recognize longer sequences of time series data. More recently, bidirectional deep …

Traffic forecasting remains an active area of research in the transport and data science
fields. Decision-makers rely on traffic forecasting models for both policy-making and …

This study addresses the challenges of predicting traffic flow on arterial roads where
dynamic behaviours such as passenger pick-ups, vehicle turns, and parking interruptions …

The IEEE Transactions on Intelligent Transport Systems was founded in 2000 to enhance
the sharing of international research on theoretical and practical technology developments …

The rapid development of intelligent transport systems (ITS) has increased the need to
propose advanced methods to predict traffic information. These methods play an important …

The transport sector is a significant contributor to global emissions. In Australia, it is the third
largest source of greenhouse gases and is responsible for around 17% of emissions with …

This research investigates the impacts of connected and automated vehicles (CAVs) on
traffic efficiency, safety and environmental impacts in the Australian context. A Melbourne …