Research
Bike Infrastructure
This research project aims to develop a software tool to evaluate cycling policies before implementation. The research focuses on creating an easy-to-use tool that predicts the effectiveness of cycling infrastructures in terms of environmental impact, equity, and accessibility. By integrating advanced econometric and machine-learning models, the project will enable planners and engineers to assess and optimize cycling policies effectively. This initiative is essential for promoting sustainable and equitable transportation solutions in Blacksburg and beyond.
Urban Air Mobility
This project aims to evaluate the impact of UAM services on urban and rural transportation in the USA. The research will use a combination of revealed and stated preference surveys and AI-based models to analyze potential UAM demand and supply interactions. Focused on areas like Washington, DC, and Blacksburg, VA, the project will explore socioeconomic patterns, frequency of usage, and potential routes and vertiport locations, considering FAA and zoning regulations. A key goal is to ensure the accessibility and affordability of UAM services across all socioeconomic groups, integrating equity considerations into the planning and implementation phases. The outcomes will include advanced AI models and an open-source software system, providing valuable tools for stakeholders and policymakers in evaluating UAM-related strategies.
E-commerce
The study aims to develop a series of econometric models to estimate household-level online shopping demand. Then a recommendation system will be developed which will be improved using machine learning models so that consumers do not need to search for each item before ordering. Instead, a consumer can just review the proposed shopping list and choose the items which are required. Each household typically has repetitive online shopping demands daily, weekly, or monthly. Individual orders are generally classified as repetitive, seasonal, one-time purchases, etc. The outcome of this study is to generate a set of shopping lists daily, weekly, and monthly so that consumers can review them and order the revised items. This will immensely save consumers browsing time while shopping same products. In addition, the machine learning model will learn individuals’ behavior over time and increase the accuracy of the recommendation system.
Mode Choice
This research investigates the mode choice patterns of post-secondary students in the Greater Toronto and Hamilton Area (GTHA) using data from an online survey. It identifies significant factors like travel time, distance, cost, and personal preferences that influence students’ transportation decisions for two-trip and three-trip tours. Key findings include international students’ preference for transit, the adaptability of students at urban institutions and part-time students between driving and transit, and age-related transit preferences among female students. Policy analysis further reveals that students prefer driving for longer distances but opt for transit as travel time increases, highlighting the importance of flexible transportation policies.
Mode and Departure Time Choice
This study explores the travel behaviors of postsecondary students in the Greater Toronto and Hamilton Area (GTHA), focusing on tour mode choice, tour departure time choice, and activity-duration choice. Utilizing theories such as Kuhn-Tucker optimization, dynamic programming, and discrete choice, the research develops a closed-form modeling framework that is computationally efficient. Insights were drawn from a comprehensive survey of postsecondary students in the GTHA. Key findings include variability in activity duration choices related to different activity types, household size, number of dependent children, and students’ year of study. The study also indicates that substantial changes in travel time are required to alter students’ travel behaviors significantly. Additionally, changes in household size can influence individuals’ preferences for departure times and activity durations. The findings suggest that enhancing intermodal transportation connectivity could encourage a shift towards more sustainable transit options among students.
Acitvity Type Choice
The main focus of the research is to analyze the factors influencing the activity type choices of post-secondary students. It uses mixed and multinomial logit-based Dynamic Discrete Choice models to explore how travel distance costs, student status, and gender influence students’ daily activity choices. This research uniquely focuses on the student demographic, offering critical data that can aid future urban planning and transportation policies. It particularly examines how travel distances, costs, student status, and gender impact students’ decisions regarding their daily activities, highlighting the significance of institutional location on these choices. This study aims to provide insights that can guide the development of more effective transportation policies and infrastructure planning tailored to the unique travel behaviors of students.
Previous Projects
INFLUENCE ON MECHANICAL AND DURABILITY PROPERTIES OF CONCRETE WITH LIME STONE POWDER
Concrete is the most commonly used material in the world for construction and to produce this large amount of concrete requires production of large quantities of cement which results in degradation of environment releasing harmful gases. One approach for making eco-friendly concrete is to use less cement in concrete, and for that aim, other cementitious materials can be utilized as a partial replacement for cement. This research focuses on the mechanical and durability properties of concrete having limestone powder (LSP) as a partial replacement for cement. Five different combinations are considered for this study and level of LSP replacement is 5%, 10%,15% and 20% (by weight). The result indicates that the slump value increases with increasing LSP content. At 28 days compressive strength and flexural strength are found the highest for a replacement of 10% cement with LSP. The concrete cylinders are subjected to elevated temperature (200°C and 400°C) for one hour and then cooled down to room temperature (25°C). Reduction in compressive strength with increased temperature is up to 6% at 200°C temperature and up to 11% at 400°C temperature. Chloride ion penetrability of LSP concrete is also performed using a surface resistivity meter. Penetrability of chloride ion is reducing with increased LSP content in concrete. Therefore, incorporating 10% LSP in concrete as a replacement of Portland cement will increase the mechanical and durability properties of concrete.
MECHANICAL AND DURABILITY PROPERTIES OFFLY ASH BLENDED CONCRETE WITH GI FIBER
Over the last few decades, the global production of building materials has increased. Concrete is one of the highly used construction materials and in recent year different types of concretes are prevailing. Fly ash blended concrete with galvanized iron (GI) fiber is a type of concrete in which the cement can be partially or fully replaced by fly ash. This study investigates the influence of fly ash and GI fiber on the engineering properties of concrete by replacing different percentages of cement (5%, 10% and 15% by weight) with fly ash and by adding 0.5% (by volume) GI fiber into the concrete. Workability, compressive strength, splitting tensile strength, flexural strength, stress strain response under axial compression, durability at high temperature are the engineering properties that have been investigated. It was found that concrete up to 15% fly ash and fiber showed better result of compressive strength than concrete without fly ash and fiber. The splitting tensile strength was also found to be highest in concrete with 15% fly ash and 0.5% fiber. The flexural strength of fiber reinforced concrete with fly ash has been examined by loading concrete beams. According to the findings, increasing the percentage of fly ash and adding GI fiber improved flexural strength and changed the stress strain response of fly ash blended concrete with GI fiber from brittle to ductile. The durability test results showed that higher percentages of fly ash decreased the strength reduction between compressive stresses at high temperature (500°C) and room temperature (25°C).