Leveraging Social Media Data to Identify Factors Influencing Public Attitude Towards Accessibility, Socioeconomic Disparity and Public Transportation

This study proposes a novel method to understand the factors affecting individuals' perception of transport accessibility, socioeconomic disparity, and public infrastructure. As opposed to the time consuming and expensive survey-based approach, this method can generate organic large-scale responses from social media and develop statistical models to understand individuals' perceptions of various transportation issues. This study retrieved and analyzed 36,098 tweets from New York City from March 19, 2020, to May 15, 2022. A state-of-the-art natural language processing algorithm is used for text mining and classification. A data fusion technique has been adopted to generate a series of socioeconomic traits that are used as explanatory variables in the model. The model results show that females and individuals of Asian origin tend to discuss transportation accessibility more than their counterparts, with those experiencing high neighborhood traffic also being more vocal. However, disadvantaged individuals, including the unemployed and those living in low-income neighborhoods or in areas with high natural hazard risks, tend to communicate less about such issues. As for socioeconomic disparity, individuals of Asian origin and those experiencing various types of air pollution are more likely to discuss these topics on Twitter, often with a negative sentiment. However, unemployed, or disadvantaged individuals, as well as those living in areas with high natural hazard risks or expected losses, are less inclined to tweet about this subject. Lack of internet accessibility could be a reason why many disadvantaged individuals do not tweet about transport accessibility and subsidized internet could be a possible solution

Community-based Behavioral Understanding of Crisis Activity Concerns using Social Media Data: A Study on the 2023 Canadian Wildfires in New York City

New York City (NYC) topped the global chart for the worst air pollution in June 2023, owing to the wildfire smoke drifting in from Canada. This unprecedented situation caused significant travel disruptions and shifts in traditional activity patterns of NYC residents. This study utilized large-scale social media data to study different crisis activity concerns (i.e., evacuation, staying indoors, shopping, and recreational activities among others) in the emergence of the 2023 Canadian wildfire smoke in NYC. In this regard, one week (June 02 through June 09, 2023) geotagged Twitter data from NYC were retrieved and used in the analysis. The tweets were processed using advanced text classification techniques and later integrated with national databases such as Social Security Administration data, Census, and American Community Survey. Finally, a model has been developed to make community inferences of different activity concerns in a major wildfire. The findings suggest, during wildfires, females are less likely to engage in discussions about evacuation, trips for medical, social, or recreational purposes, and commuting for work, likely influenced by workplaces maintaining operations despite poor air quality. There were also racial disparities in these discussions, with Asians being more likely than Hispanics to discuss evacuation and work commute, and African Americans being less likely to discuss social and recreational activities. Additionally, individuals from low-income neighborhoods and non-higher education students expressed fewer concerns about evacuation. This study provides valuable insights for policymakers, emergency planners, and public health officials, aiding them in formulating targeted communication strategies and equitable emergency response plans

An Interpretable Machine Learning Framework to Understand Bikeshare Demand before and during the COVID-19 Pandemic in New York City

In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such as machine learning are required to generate good forecasts for bikeshare demand. To this end, this study proposes a machine learning modeling framework to estimate hourly demand in a large-scale bikesharing system. Two Extreme Gradient Boosting models were developed: one using data from before the COVID-19 pandemic (March 2019 to February 2020) and the other using data from during the pandemic (March 2020 to February 2021). Furthermore, a model interpretation framework based on SHapley Additive exPlanations was implemented. Based on the relative importance of the explanatory variables considered in this study, share of female users and hour of day were the two most important explanatory variables in both models. However, the month variable had higher importance in the pandemic model than in the pre-pandemic model

Beyond Learning Analytics: Framework for Technology-Enhanced Evidence-Based Education and Learning

26th International Conference on Computers in Education, Metro Manila, Philippines, November 26-30, 2018.Currently eLearning infrastructure across various institutions often includes aLearning Management System (LMS), various ubiquitous and classroom learning tools, Learning Record Stores (LRS) and Learning Analytics Dashboards (LAD). Such aninfrastructure can apply Learning Analytics (LA) methods to process log data and supportvarious stakeholders. Teachers can refine their instructional practices, learners can enhancelearning experiences and researchers can study the dynamics of the teaching-learningprocess with it. While LA platforms gathers and analyses the data, there is a lack of specificdesign framework to capture the technology-enhanced teaching-learning practices. Thisposition paper focuses the research agenda on evidence in a data-driven educationalscenario. We propose the Learning Evidence Analytics Framework (LEAF) and present theresearch challenges involved

ゴイガクシュウニテキセツナガゾウノスイセンニカンスルケンキュウ

博士（工学）東京農工大

Tour-based Mode Choice Model in Activity-based Modelling Framework

This thesis presents a tour-based mode choice modelling structure for activity-based travel demand models by exploiting the classical dynamic discrete choice modelling approach. Many activity-based modelling systems rely on either trip-based or ‘simplified’ tour-based mode choice models that in many cases completely overlook the dynamics of mode choice behaviour. To contribute to filling this gap, this thesis applies a systematic approach of investigation to better understand the nature of tour-based mode choices and to propose a parsimonious modelling structure for it. The first investigation looks into the trip-based mode choice behaviour of post-secondary students commuting to universities in the City of Toronto. The second investigation uses a heteroskedastic dynamic discrete choice model for tour-based mode choices modelling with an empirical investigation of university students’ daily mode choices in Toronto. The third investigation uses a computationally tractable dynamic discrete choice modelling framework for modelling tour-based mode choices. The fourth investigation uses a random utility maximization -based dynamic discrete-continuous modelling approach to capture individuals’ tour-based modes and continuous time-expenditure choice trade-offs in a 24-hour time frame. The model results reveal that individuals’ sensitivity to travel costs varies, while their sensitivity to travel time remains stable. The empirical model reveals that users of newly introduced mobility services (e.g., Uber, Lyft) tend to have different mode choice patterns and value of travel time savings than non-users of these services. The forward-looking component reveals that availability of the modes for subsequent trips in the tour represent a significant portion of the utility of the current mode choices. In terms of the time-expenditure choice model, it is found that full-time employees and younger individuals tend to leave home earlier than part-time employees and older individuals. It is found that individuals are likely to spend long hours at work or school if they leave home early. Furthermore, individuals are likely to schedule non-mandatory activities, such as shopping, later in the day. The validation and policy evaluation results are promising. While the models proposed here can be easily developed in different regions across North America, opportunities also exist for the application of this type of analysis globally.Ph.D

A Comprehensive Study on the Effectiveness of Office-based TDM Policies

The objective of this thesis is to develop an employer-based Transportation Demand Management (TDM) evaluation tool that can be used for evaluating various employer-based TDM policies. The conventional method of evaluating TDM policies has typically been conducting expensive before and after TDM policy implementation surveys. On the contrary, this research used a pre-policy deployment joint Revealed Preference and Stated Preference (RP-SP) survey, where the data were collected to develop a TDM policy sensitive mode choice model, which is packaged into a software system for TDM investment decision support. The evaluation tool (named Off-TET) developed by integrating the mode choice model predicts changes in modal share by integrating all possible effects of single or multiple TDM policies implemented in isolation or combined. While the tool presented in this thesis was developed for the region of Peel, there exist opportunities for the application of this type of analysis across Canada.M.A.S

PARTICLE INDUCED TRANSITION IN HIGH-SPEED BOUNDARY-LAYER FLOWS

Boundary-layer transition to turbulence presents a critical challenge in aerospace engineering due to its impact on thermal load, especially for hypersonic vehicles. This transition, influenced by various disturbances such as acoustic waves, entropy waves, and particle impingement, follows complex and non-unique pathways to turbulence. It significantly affects the surface heat flux and thus will impact the design of thermal protection systems. This dissertation focuses on the transition process initiated by particle impingement, which introduces small-scale disturbances through a complex receptivity process that typically initiates a natural transition path. Using direct numerical simulations, this study explores the particle-induced transition process. The disturbance spectrum, consisting of both stable and unstable modes along with continuous acoustic contributions, is meticulously reconstructed near the particle impingement site using biorthogonal decomposition to assess the contributions of different eigenmodes to the initial disturbance spectrum. A large number of discrete and continuous eigenmodes are seeded, but the dominant eigenmodes capture only a small fraction of the disturbance energy, with the majority reflected into the freestream through the continuous modes associated with the continuous acoustic branches. The modeling fidelity is also investigated, particularly the particle-source-in-cell (PSIC) approach, commonly used due to its efficiency in capturing particle-flow interactions. Comparisons with the Immersed-Boundary-Method (IBM), however, reveal that PSIC inadequately captures particle-wall interactions and needs correction for accurate disturbance modeling. Finally, a reduced-order model is developed for the prediction of particle-induced transition. This model integrates data from high-fidelity simulations, linear stability theory, and a saturation amplitude model while also considering particle characteristics like size, density and concentration. The model’s capability is demonstrated for a wide range of transition scenarios, including data from the HIFiRE-1 flight test, offering a robust tool for rapid transition prediction in hypersonicvehicle design