Zobrazeno 1 - 10
of 89
pro vyhledávání: '"Hussein Dia"'
Autor:
Ali Matin, Hussein Dia
Publikováno v:
Journal of Intelligent and Connected Vehicles, Vol 7, Iss 2, Pp 108-128 (2024)
This study investigates the attitudes and concerns of the Australian public toward connected and autonomous vehicles (CAVs), and the factors influencing their willingness to adopt this technology. Through a comprehensive survey, a diverse group of re
Externí odkaz:
https://doaj.org/article/3515ace89aee492f8e99f3cd5138dd8c
Publikováno v:
EURO Journal on Transportation and Logistics, Vol 13, Iss , Pp 100130- (2024)
Cities around the globe face major last mile delivery (LMD) challenges as a result of surging online commerce activity, increased parcel delivery demands, lack of parking capacity, and severe traffic congestion particularly in inner city areas. Altho
Externí odkaz:
https://doaj.org/article/bcae609eabce494f90a079cd3ab83128
Publikováno v:
Journal of Urban Management, Vol 11, Iss 3, Pp 365-380 (2022)
Accurate short-term forecasting of public transport demand is essential for the operation of on-demand public transport. Knowing where and when future demands for travel are expected allows operators to adjust timetables quickly, which helps improve
Externí odkaz:
https://doaj.org/article/bb1d659899e8486795b3c56195ac85e7
Autor:
Ali Yavari, Irfan Baig Mirza, Hamid Bagha, Harindu Korala, Hussein Dia, Paul Scifleet, Jason Sargent, Caroline Tjung, Mahnaz Shafiei
Publikováno v:
Sensors, Vol 23, Iss 18, p 7971 (2023)
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 emissi
Externí odkaz:
https://doaj.org/article/b628d5d2a0314319a3edf3d8c9fb6095
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-16 (2021)
Abstract 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
Externí odkaz:
https://doaj.org/article/4978b2ba44b344d9bdd51e616da634e0
Autor:
Damian Moffatt, Hussein Dia
Publikováno v:
Future Transportation, Vol 1, Iss 2, Pp 134-153 (2021)
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 passenger cars accounting for around half of all transport emi
Externí odkaz:
https://doaj.org/article/9d53fc15b2694e8ebbd56e16703368cd
Autor:
Chris Mccarthy, Irene Moser, Prem Prakash Jayaraman, Hadi Ghaderi, Adin Ming Tan, Ali Yavari, Ubaid Mehmood, Matthew Simmons, Yehuda Weizman, Dimitrios Georgakopoulos, Franz Konstantin Fuss, Hussein Dia
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 2, Pp 384-401 (2021)
The planning of public transport operations is an essential component of urban transport management systems that aims to provide the most efficient, safe and effective way to support movement of people. Improving the customer journey experience is a
Externí odkaz:
https://doaj.org/article/3facac071c6445b5882676d4ee1e30af
Publikováno v:
Machines, Vol 10, Iss 10, p 926 (2022)
Public safety is prime concern in rail industry and driver training on hazard perception is crucial. Additionally, a new driver’s skill set determines the productivity and quality of existing driver training methods. Apprentice train drivers are re
Externí odkaz:
https://doaj.org/article/b26a4330d4bc479eb9a3fdb0214d3f3e
Autor:
Ali Yavari, Hamid Bagha, Harindu Korala, Irfan Mirza, Hussein Dia, Paul Scifleet, Jason Sargent, Mahnaz Shafiei
Publikováno v:
Sensors, Vol 22, Iss 19, p 7380 (2022)
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-mi
Externí odkaz:
https://doaj.org/article/710c2a8c6e9a40e1b931187a91a3f6fb
Publikováno v:
Journal of Advanced Transportation, Vol 2021 (2021)
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 networks. The unidirectional LSTM (Uni-LSTM) model provides high perf
Externí odkaz:
https://doaj.org/article/6d9cb459e2d74df0a15bb90f675c844a