Advancing sustainable transportation through AI, data analytics, and innovative simulation technologies.
Ongoing research initiatives pushing the boundaries of transportation technology.
AI-Enhanced EV Charging Infrastructure Equity Analysis System. A comprehensive platform that analyzes electric vehicle charging station deployment equity across different regions, integrating multiple data sources with LLM-powered insights for policy recommendations.
Intelligent Real-Time Parking Analysis System for Singapore with LLM-powered insights. Analyzes parking availability patterns, predicts demand, and provides natural language explanations for urban parking dynamics.
AI-powered transportation policy simulation platform. Enables policymakers and researchers to simulate the impact of various transportation policies before implementation, with transparent mathematical models and scenario comparison capabilities.
An open-source, Streamlit-based platform that simplifies the creation and analysis of SUMO (Simulation of Urban Mobility) traffic scenarios. Features interactive editors for nodes, edges, vehicle types, routes, flows, detectors, and traffic light programs with full XML generation and analytics dashboard.
Successfully completed research contributing to the field of transportation engineering.
First academic analysis of Malaysia's newly released GTFS datasets, assessing public transit network efficiency and accessibility in Johor Bahru and Penang using data-driven approaches.
Investigating risky riding behaviors among young motorcyclists in Bangladesh using a modified Motorcycle Rider Behavior Questionnaire (MRBQ) adapted for local context.
Understanding post-COVID-19 household vehicle ownership dynamics through explainable machine learning using the 2022 U.S. National Household Travel Survey (NHTS) data.
Core domains where my research makes an impact.
Exploring applications of LLMs in transportation research, from traffic incident analysis to policy recommendations and natural language interfaces.
Developing smart solutions for traffic management, real-time monitoring, and adaptive control systems using AI and IoT technologies.
Researching solutions for environmentally friendly transportation including EV infrastructure, public transit optimization, and emission reduction strategies.
Applying machine learning, deep learning, and computer vision to solve complex transportation challenges and optimize systems.
Developing SUMO-based microsimulations, traffic modeling tools, and optimization algorithms for transportation network analysis.
Creating real-time digital replicas of transportation networks for monitoring, scenario analysis, and predictive optimization.
I'm always open to research collaborations, industry partnerships, and academic exchanges. Let's work together to advance transportation technology.