ArtEMon: Artificial Intelligence and Internet of Things Powered Greenhouse Gas Sensing for Real-Time Emissions Monitoring.

Greenhouse gas (GHG) emissions reporting and sustainability are increasingly important for businesses around the world. Yet the lack of a single standardised method of measurement, when coupled with an inability to understand the true state of emissions in complex logistics activities, presents enormous barriers for businesses to understanding the extent of their emissions footprint. One of the traditional approaches to accurately capturing and monitoring gas emissions in logistics is through using gas sensors.

ParcEMon: IoT Platform for Real-Time Parcel Level Last-Mile Delivery Greenhouse Gas Emissions Reporting and Management.

Transport is Australia's third-largest source of greenhouse gases accounting for around 17% of emissions. In recent times, and particularly as a result of the global pandemic, the rapid growth within the e-commerce sector has contributed to last-mile delivery becoming one of the main emission sources. Delivery vehicles operating at the last-mile travel long routes to deliver to customers an array of consignment parcels in varying numbers and weights, and therefore these vehicles play a major role in increasing emissions and air pollutants.

A systematic review of the impacts of the coronavirus crisis on urban transport: Key lessons learned and prospects for future cities.

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 many cities leading to severe travel restrictions and travel demand reduction to limit the spread of the disease. This article provides bibliometric evidence-based insights into how the pandemic has exposed the vulnerability of established public transport systems and shared mobility solutions.

Development and evaluation of bidirectional LSTM freeway traffic forecasting models using simulation data.

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 learning models (BiLSTM) have extended the LSTM capabilities by training the input data twice in forward and backward directions. In this paper, BiLSTM short term traffic forecasting models have been developed and evaluated using data from a calibrated micro-simulation model for a congested freeway in Melbourne, Australia.