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.
Papers
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Investigating hierarchical effects of adaptive signal control system on crash severity using random-parameter ordered regression models incorporating observed heterogeneity.
TL;DR: Observed heterogeneity of ASCS effects on the crash severity is captured by variables related to the intersection and corridor features, which has practical implications for establishing ASCS implementation guidelines in lowering the probability of higher crash severity.
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Review of State DOTs Policies for Overweight Truck Fees and Relevant Stakeholders’ Perspectives
TL;DR: In this paper, the authors characterize overweight load permit practices among all U.S. states and identify stakeholders' perspectives on how to modernize current overweight permit practices, and evaluate these practices through an analysis of existing fee policies.
Posted Content
Long Short-Term Memory Neural Networks for False Information Attack Detection in Software-Defined In-Vehicle Network
TL;DR: A long short term memory (LSTM) neural network based false information attack/anomaly detection model for the real-time detection of anomalies within the in-vehicle network and can detect false information with an accuracy, precision and recall of 95, 95% and 87%, respectively.
Journal ArticleDOI
Development of a Professional Services Management Training Program
TL;DR: The lack of training programs in systems that allow insufficient references in departments of transportation (DOTs) can create a procurement and administration process that is inefficient, inconsistent, and ineffective as discussed by the authors.
Posted Content
Multi-class Twitter Data Categorization and Geocoding with a Novel Computing Framework
TL;DR: In this paper, a case study conducted for the New York City and its surrounding areas demonstrates the feasibility of the analytical approach and demonstrates that the analytical framework, which uses the L-LDA incorporated SVM, can classify roadway transportation-related data from Twitter with over 98.3% accuracy.