M
Mashrur Chowdhury
Researcher at Clemson University
Publications - 219
Citations - 3740
Mashrur Chowdhury is an academic researcher from Clemson University. The author has contributed to research in topics: Intelligent transportation system & Traffic simulation. The author has an hindex of 26, co-authored 212 publications receiving 2960 citations. Previous affiliations of Mashrur Chowdhury include The Chinese University of Hong Kong & University of Dayton.
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An energy optimization strategy for power-split drivetrain plug-in hybrid electric vehicles
TL;DR: In this article, an energy optimization strategy for a power-split drivetrain PHEV, which utilizes a predicted speed profile, is presented to demonstrate the greater capabilities and benefits achievable with a plug-in hybrid electric vehicle (PHEV).
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Potential of Intelligent Transportation Systems in Mitigating Adverse Weather Impacts on Road Mobility: A Review
TL;DR: Current intelligent transportation systems (ITS)-based solutions for minimizing road weather impacts and possible ITS innovations to incorporate diverse data sources to improve road weather management activities are reviewed.
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Integrated Traffic and Communication Performance Evaluation of an Intelligent Vehicle Infrastructure Integration (VII) System for Online Travel-Time Prediction
TL;DR: A framework for online highway travel-time prediction using traffic measurements that are likely to be available from vehicle infrastructure integration (VII) systems, in which vehicle and infrastructure devices communicate to improve mobility and safety, reveals that the designed communications system can support the travel- time prediction functionality.
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Risk Analysis of Autonomous Vehicles in Mixed Traffic Streams
TL;DR: In this article, the authors identify the risks associated with the failure of an autonomous vehicle in mixed traffic streams and develop a fault tree model to estimate the failure probabilities of each component.
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Real-Time Traffic State Estimation With Connected Vehicles
TL;DR: This paper demonstrates that the integrated CVT-AI method yields a higher accuracy with the increase of CV penetration levels, and is compared with the density estimation algorithm used by the Caltrans Performance Measurement System (PeMS), which relies on the occupancy and flow data collected by the freeway inductive loop detectors.