5 papers
Modeling Chaotic Pedestrian Behavior Using Chaos Indicators and Supervised Learning
Md. Muhtashim Shahrier, Nazmul Haque, Md Asif Raihan +1
As cities around the world aim to improve walkability and safety, understanding the irregular and unpredictable nature of pedestrian behavior has become increasingly important. Thi…
CNN-Based Framework for Pedestrian Age and Gender Classification Using Far-View Surveillance in Mixed-Traffic Intersections
Shisir Shahriar Arif, Md. Muhtashim Shahrier, Nazmul Haque +2
Pedestrian safety remains a pressing concern in congested urban intersections, particularly in low- and middle-income countries where traffic is multimodal, and infrastructure ofte…
How does the Performance of the Data-driven Traffic Flow Forecasting Models deteriorate with Increasing Forecasting Horizon? An Extensive Approach Considering Statistical, Machine Learning and Deep Learning Models
Amanta Sherfenaz, Nazmul Haque, Protiva Sadhukhan Prova +2
With rapid urbanization in recent decades, traffic congestion has intensified due to increased movement of people and goods. As planning shifts from demand-based to supply-oriented…
Trajectory-based real-time pedestrian crash prediction at intersections: A novel non-linear link function for block maxima led Bayesian GEV framework addressing heterogeneous traffic condition
Parvez Anowar, Nazmul Haque, Md Asif Raihan +1
This study develops a real-time framework for estimating pedestrian crash risk at signalized intersections under heterogeneous, non-lane-based traffic. Existing approaches often as…
DEEGITS: Deep Learning based Framework for Measuring Heterogenous Traffic State in Challenging Traffic Scenarios
Muttahirul Islam, Nazmul Haque, Md. Hadiuzzaman
This paper presents DEEGITS (Deep Learning Based Heterogeneous Traffic State Measurement), a comprehensive framework that leverages state-of-the-art convolutional neural network (C…