A5000204972- Urban Transport and Accessibility
- Transportation Planning and Optimization
- Economic and Environmental Valuation
- Transportation and Mobility Innovations
- Traffic and Road Safety
- Urban and Freight Transport Logistics
- Consumer Retail Behavior Studies
- Aviation Industry Analysis and Trends
- Decision-Making and Behavioral Economics
- COVID-19 epidemiological studies
- Disaster Management and Resilience
- Human Mobility and Location-Based Analysis
- Migration, Aging, and Tourism Studies
- Tactile and Sensory Interactions
- Housing Market and Economics
- Smart Parking Systems Research
- Consumer Market Behavior and Pricing
- Automotive and Human Injury Biomechanics
- Urban, Neighborhood, and Segregation Studies
- Energy, Environment, and Transportation Policies
- Noise Effects and Management
- Human-Automation Interaction and Safety
- COVID-19 Pandemic Impacts
- Collaboration in agile enterprises
- Traffic control and management
Howard University
2020-2023
University of Toronto
2016-2022
Massachusetts Institute of Technology
2019-2020
This study presents a combined revealed preference (RP) and stated (SP) survey to understand travelers’ mode choices under the influence of real-time information for different activity types trip lengths. The D-efficient method is adopted generate SP scenarios. empirical data this came from “Survey impact ICT on transportation choices” (SUIT; = communication technology), conducted in July 2023 Washington, DC metro area Charlotte area, North Carolina (NC), USA A RP–SP multinomial logit mixed...
This paper presents a study of various factors that influenced online and in-store shopping behaviors in New York City during the COVID-19 pandemic. It uses panel data collected by Department Transportation for May, July, October 2020. In survey, 696 respondents consistently responded all three months and, therefore, this group was used analysis. The adopts random effect ordered probit models with marginal effects dynamic discrete choice to understand reveal increased subway usage correlated...
The article proposes a two-staged modelling approach to identify the association between one vehicle's attributes, as well roadway engineering, environmental and crash characteristics, injury severity of occupants in partnering vehicle two-vehicle crashes. uses bivariate binary probit model, data for Toronto, first determine probability no occurring, followed by use ordered model investigate conditional specific level. Vehicles crashes are categorized “not-at-fault” (NAF) or “at-fault” (AF)...
This paper presents a tool for the evaluation of employer-based transportation demand management (TDM) strategies. The conventional method evaluating TDM strategies has typically been to conduct expensive before-and-after strategy implementation surveys. As an alternative approach, this research uses joint revealed preference (RP) and stated (SP) survey (the RP–SP survey) administered before deployment strategy, which is more cost-effective efficient. data collected from were used estimate...
This paper investigates the factors influencing physical health condition and trip distance of e-bike users' in Toronto, Canada. The research is based on a survey bicycle travel behaviour Toronto. A Bivariate Ordered Probit model used to draw links between different that affect conditions users. It important note modelling results at best indicate an association (not causation) dependent independent variables. reveals e-bikers from low-income households have higher risk than high-income...
The e-commerce market has grown rapidly in the past two decades. need for predicting demand and evaluating relevant policies solutions is increasing. However, existing simulation models are still limited do not consider impacts of delivery options their attributes that shoppers face on multiple dimensions demand. We propose a novel framework involving disaggregate behavioral jointly predict expenditure, purchase amount per transaction, mode, option choices. proposed can simulate changes be...
In recent years, bikesharing systems have become increasingly popular as affordable and sustainable micromobility solutions. Advanced mathematical models such machine learning are required to generate good forecasts for bikeshare demand. To this end, study proposes a modeling framework estimate hourly demand in large-scale system. Two Extreme Gradient Boosting were developed: one using data from before the COVID-19 pandemic (March 2019 February 2020) other during 2020 2021). Furthermore,...
This paper presents a study of commuters’ responses to various employer-based transportation demand management (TDM) strategies that was conducted in the Region Peel, Ontario, Canada. The involves design and implementation web-based survey daily commuting mode choices an efficient design-based stated preference (SP) experiment on choice effects potential TDM strategies. For SP experiments, also collected elicited confidence rating from respondents. 835 random commuters fall 2014 spring 2015....
This study investigates the factors influencing university student's living arrangement choice, distance between home and university, mode choice. A closed-form probabilistic choice modelling formulation is developed to model three choices jointly: typical commuting campus. The proposed joint harnesses power of both Random Utility Maximisation (RUM) Cobb–Douglas direct utility maximisation principle. identifies nature extent various systematic random residence behaviour provide evidence for...
The objective of this study is to investigate the impact COVID-19 vaccine on bike sharing demand in New York City (NYC). vaccination’s was also compared with other known influences (COVID-19 cases counts, deaths, weather data, trip purpose, and more) help improve share modeling a pandemic setting. Autoregressive integrated moving average (ARIMAX) time series models were estimated for Brooklyn Manhattan both pre-vaccine post-vaccine periods. Mean absolute percentage error (MAPE) used model...