ORCID Profile
0000-0001-9746-8109
Current Organisations
Columbia University
,
City University of New York
,
NASA Goddard Space Flight Center
,
Queensland University of Technology
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Publisher: IEEE
Date: 12-2017
Publisher: Springer Nature Singapore
Date: 2023
Publisher: ASTES Journal
Date: 02-2021
DOI: 10.25046/AJ060193
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2023
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Wiley
Date: 06-03-2018
DOI: 10.1002/EAP.1682
Publisher: Elsevier BV
Date: 03-2022
DOI: 10.1016/J.CELL.2022.01.028
Abstract: Malaria is estimated by the World Health Organization (WHO) to have killed 627,000 in iduals worldwide in 2020, with nearly 80% of deaths in African children younger than five. The recent WHO approval of the RTS,S/AS01 vaccine, which targets Plasmodium falciparum pre-erythrocytic stages, provides hope that its use combined with other interventions can help reverse the current malaria resurgence.
Publisher: Public Library of Science (PLoS)
Date: 05-08-2021
DOI: 10.1371/JOURNAL.PONE.0255828
Abstract: Road crash fatality is a universal problem of the transportation system. A massive death toll caused annually due to road crash incidents, and among them, vulnerable road users (VRU) are endangered with high crash severity. This paper focuses on employing machine learning-based classification approaches for modelling injury severity of vulnerable road users—pedestrian, bicyclist, and motorcyclist. Specifically, this study aims to analyse critical features associated with different VRU groups—for pedestrian, bicyclist, motorcyclist and all VRU groups together. The critical factor of crash severity outcomes for these VRU groups is estimated in identifying the similarities and differences across different important features associated with different VRU groups. The crash data for the study is sourced from the state of Queensland in Australia for the years 2013 through 2019. The supervised machine learning algorithms considered for the empirical analysis includes the K-Nearest Neighbour (KNN), Support Vector Machine (SVM) and Random Forest (RF). In these models, 17 distinct road crash parameters are considered as input features to train models, which originate from road user characteristics, weather and environment, vehicle and driver condition, period, road characteristics and regions, traffic, and speed jurisdiction. These classification models are separately trained and tested for in idual and unified VRU to assess crash severity levels. Afterwards, model performances are compared with each other to justify the best classifier where Random Forest classification models for all VRU modes are found to be comparatively robust in test accuracy: (motorcyclist: 72.30%, bicyclist: 64.45%, pedestrian: 67.23%, unified VRU: 68.57%). Based on the Random Forest model, the road crash features are ranked and compared according to their impact on crash severity classification. Furthermore, a model-based partial dependency of each road crash parameters on the severity levels is plotted and compared for each in idual and unified VRU. This clarifies the tendency of road crash parameters to vary with different VRU crash severity. Based on the outcome of the comparative analysis, motorcyclists are found to be more likely exposed to higher crash severity, followed by pedestrians and bicyclists.
Publisher: IEEE
Date: 12-2019
Publisher: MDPI AG
Date: 06-07-2021
DOI: 10.3390/S21144636
Abstract: Substantial research is required to ensure that micro-mobility ride sharing provides a better fulfilment of user needs. This study proposes a novel crowdsourcing model for the ride-sharing system where light vehicles such as scooters and bikes are crowdsourced. The proposed model is expected to solve the problem of charging and maintaining a large number of light vehicles where these efforts will be the responsibility of the crowd of suppliers. The proposed model consists of three entities: suppliers, customers, and a management party responsible for receiving, renting, booking, and demand matching with offered resources. It can allow suppliers to define the location of their private e-scooters/e-bikes and the period of time they are available for rent. Using a dataset of over 9 million e-scooter trips in Austin, Texas, we ran an agent-based simulation six times using three maximum battery ranges (i.e., 35, 45, and 60 km) and different numbers of e-scooters (e.g., 50 and 100) at each origin. Computational results show that the proposed model is promising and might be advantageous to shift the charging and maintenance efforts to a crowd of suppliers.
Location: United States of America
No related grants have been discovered for Md Mostafizur Rahman Komol.