On hyperparameter optimization of machine learning methods using a Bayesian optimization algorithm to predict work travel mode choice

M Aghaabbasi, M Ali, M Jasiński, Z Leonowicz… - IEEE …, 2023 - ieeexplore.ieee.org
Prediction of work Travel mode choice is one of the most important parts of travel demand
forecasting. Planners can achieve sustainability goals by accurately forecasting how people …

A Systematic Review of the Coopetition Relationship between Bike‐Sharing and Public Transit

J Ye, J Bai, WY Hao - Journal of Advanced Transportation, 2024 - Wiley Online Library
The sharing economy, mobile Internet, and smartphones have been widely utilized in recent
years to promote the development of bike‐sharing services. Bike‐sharing serves as a …

Understanding influencing factors of travel mode choice in urban-suburban travel: a case study in Shanghai

J Le, J Teng - Urban Rail Transit, 2023 - Springer
After the rapid expansion of the subway system over the past two decades, some cities are
preparing to build more suburban railways. The emergence of suburban railways is bound …

[HTML][HTML] Comparative analysis of the optimized KNN, SVM, and ensemble DT models using Bayesian optimization for predicting pedestrian fatalities: an advance …

L Yang, M Aghaabbasi, M Ali, A Jan, B Bouallegue… - Sustainability, 2022 - mdpi.com
Over the past three decades, more than 8000 pedestrians have been killed in Australia due
to vehicular crashes. There is a general assumption that pedestrians are often the most …

[HTML][HTML] An advanced machine learning approach to predicting pedestrian fatality caused by road crashes: A step toward sustainable pedestrian safety

W Tao, M Aghaabbasi, M Ali, AH Almaliki, R Zainol… - Sustainability, 2022 - mdpi.com
More than 8000 pedestrians were killed due to road crashes in Australia over the last 30
years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility …

[HTML][HTML] Travel mode choice prediction to pursue sustainable transportation and enhance health parameters using R

M Ali, E Macioszek, N Ali - Sustainability, 2024 - mdpi.com
Travel mode choice (TMC) prediction, improving health parameters, and promoting
sustainable transportation systems are crucial for urban planners and policymakers. Past …

Application of machine learning to child mode choice with a novel technique to optimize hyperparameters

H Naseri, EOD Waygood, B Wang… - International Journal of …, 2022 - mdpi.com
Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous
studies have focused on TMC in adults, whereas predicting TMC in children has received …

[HTML][HTML] Predicting rock brittleness using a robust evolutionary programming paradigm and regression-based feature selection model

M Jamei, AS Mohammed, I Ahmadianfar, MMS Sabri… - Applied Sciences, 2022 - mdpi.com
Brittleness plays an important role in assessing the stability of the surrounding rock mass in
deep underground projects. To this end, the present study deals with develo** a robust …

[HTML][HTML] Interaction of activity travel, GHG emissions, and health parameters using R–A Step towards sustainable transportation system

M Ali, E Macioszek, K Onyelowe, CW Yuen… - Ain Shams Engineering …, 2024 - Elsevier
Physical activity (PA) has the feasibility to enhance health parameters; however, the intensity
such as frequency (days/week) and duration (minutes/day) are yet to be investigated. The …

Travel mode choice prediction using imbalanced machine learning

H Chen, Y Cheng - IEEE Transactions on Intelligent …, 2023 - ieeexplore.ieee.org
Travel mode choice prediction is critical for travel demand prediction, which influences
transport resource allocation and transport policies. Travel modes are often characterised by …