Speaker: Dr. Jeffrey Cohen, UConn

Location: TBA

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THE UNIVERSITY OF CONNECTICUT

Civil & Environmental Engineering

Albab Un Noor

Masters Thesis Defense

Department of Civil & Environmental Engineering

University of Connecticut

1:30 pm – MONDAY, April 16^th, 2018

CAST 210

Advisory Committee:

Nicholas Lownes (Co-major Advisor)

Karthik Konduri (Co-major Advisor)

John Ivan (Associate Advisor)

Amy Burnicki (Associate Advisor)

Market Segmentation of Travel Mode Choice with an Alternative Source of Travel Time Data

ABSTRACT

Transit ridership has been declining in the United States and is losing market share to private automobiles (Taylor, 2013). Moreover, transportation is now the largest contributor of GHG emissions in the country (EPA, 2018), the bulk of which comes from private automobiles. One effective strategy to reduce GHG emissions from this sector is to nudge people to use public transportation. This is a nontrivial task as the literature on the factors affecting transit usage is surprisingly uneven. It is therefore important to study the characteristics of the travelers in a particular region and understand what influences their travel choices to find possible ways to increase transit mode share. The objective of this thesis is to investigate this topic using an advanced modeling framework and an alternative source of travel time data. Firstly, this thesis explores the use of travel times retrieved from Google Maps over the traditionally used highway and transit skims retrieved from regional travel demand models. It was shown that Google Maps API provides a more accurate representation of the network and result in better choice sets for individuals. Secondly, the concept of market segmentation was applied for investigating travel mode choice in the Hartford metropolitan area using a Latent Class Choice Model (LCCM). Latent classes are formed based on the characteristics of the travelers and choice models are estimated to model their choice behavior. A model with four classes was estimated for this study where only one of segments was multimodal in their choice of travel mode, the rest being auto-dependent. But they’re distinguishable by their sensitivity to transit level of service, as one auto-dependent segment is highly sensitive to transit service whereas others are not. It was found that some segments of the population are likely to respond to transit service improvements, and some segments are unlikely to respond. This thesis examines the characteristics of these segments to understand their choice behavior. Insights from the study can be used to develop strategies to increase public transit mode share.

THE UNIVERSITY OF CONNECTICUT

Civil & Environmental Engineering

Adrita Islam

Masters Thesis Defense

Department of Civil & Environmental Engineering

University of Connecticut

1:00 pm – Tuesday, April 17^th, 2018

CAST 306

Advisory Committee:

Nicholas Lownes (Major Advisor)

Karthik Konduri (Associate Advisor)

Jeffrey Cohen (Associate Advisor)

Minimization of Carbon Footprint of Transit Agencies by Adopting Alternative Fuel Technologies

ABSTRACT

The increasing trend in Greenhouse Gas (GHG) emission around the globe has been of broad and current interest for the past few decades. In the state of Connecticut, transportation is the largest contributor of GHG emission at about 42% of the total emission of 36.5 Million Metric Tons of CO₂ equivalent. Although buses comprise less than 1% of the total transportation emissions in the country, transit agencies are trying to reduce their carbon footprint by adopting alternative fuel technology buses. The overarching objective of this thesis is to aid transit agencies make more informed decisions regarding the process of replacing the diesel fleet with alternative technology buses to minimize GHG emissions. This study investigates the complete course of fleet replacement in a more realistic way incorporating various scenario analysis. The first part of this research provides a more user-friendly approach for a transit agency to document and analyze their current GHG emission footprint from both buses and facilities. The second part of the thesis analyzes the impact of introducing alternative fuel buses into an existing bus fleet on carbon footprint. It also includes life-cycle cost (LCC) analysis of those fleet replacement strategies. As a part of this study, a Python-based web tool was created to let the users (transit agencies) control for the input values, run multiple scenario analysis, and compare results. The final part of this thesis optimizes the fleet replacement schedule by minimizing the LCC of owning and operating a fleet of buses and required infrastructures and reducing GHG emission simultaneously. In all these studies, data from Connecticut Department of Transportation was used as a case study. The problems formulated and tools built in this thesis can help any transit and government agencies determine the most optimized solution to their fleet replacement problem under customizable constraints or desired set of outcomes.

Title: Downscaling techniques for improving hyper-resolution hydrology
Speaker: Dr. Viviana Maggioni, GMU
Host: Nikolopoulos
Location: CAST212