conference paper
published journal article
A broad range of advanced air mobility (AAM) aircraft are currently in development, each with varying community noise footprints and energy consumption depending on the specifics of their departure and arrival flight trajectories, which must be understood for effective airspace integration. This work presents a framework for analyzing AAM trajectory design, focusing on key performance characteristics, including community noise impact, energy consumption, and flight duration. The framework can be applied to diverse AAM vehicle types, as demonstrated in this work on a blown-flap short takeoff and landing vehicle, a tilt-rotor vertical takeoff and landing vehicle, and a lift-plus-cruise vertical takeoff and landing vehicle. Results of comparing various takeoff procedures for each vehicle show tradeoffs between community noise, energy consumption, and flight duration, highlighting the importance of strategic trajectory design.
published journal article
working paper
Rapid, but unplanned urban development and the consequent urban sprawl coupled with economic growth have aggravated auto dependency in India over the last two decades. This has resulted in congestion and pollution in cities. The central and state Governments have taken many ameliorative measures to reduce vehicular emissions. However, evolution of scientific methods for accurate emission inventory is crucial. Therefore, an attempt has been made to estimate the emissions (running and start) from on-road vehicles in Chennai using IVE model in this paper. GPS was used to collect driving patterns.The estimated emissions from motor vehicles in Chennai in 2005 were 431, 119, 46, 6 and 4575 tons/days respectively for CO, VOC, NOx, PM and CO2. It is observed from the results that air quality in Chennai has degraded. The estimation revealed that two and three-wheelers emitted about 64 percent of the total CO emissions and heavy-duty vehicles accounted for more than 60 percent and 36 percent of the NOx and PM emissions respectively. About 19 percent of total emissions were that of start emissions. The estimated health damage cost of automobile emissions in Chennai is Rs. 6488.16 million (US$162.20 million). This paper has further examined various mitigation options to reduce vehicular emissions. The Study has concluded that advanced vehicular technology and augmentation of public transit would have significant impact on reducing vehicular emissions.
This concluding chapter presents a summary of the research findings in the previous chapters, along with some reflections for each of the five themes of the book and a discussion of necessary future responses (post-pandemic or in the event of a new pandemic) and topics that require further exploration. The pandemic brought into sharp relief pre-existing social disparities and affected vulnerable populations the most. The economic impacts of the pandemic were diverse and varied by geography, but again certain geographies and economic sectors were more buffered from negative outcomes than others. A lesson and a challenge for policymakers is to find ways to understand and reduce these disparities, instead of pushing them under the rug. The impacts on mobility and travel were dramatic as total trips decreased, transit usage fell dramatically, and telecommuting and active modes of transportation increased. Some positive impacts included an improved air quality, a reduced number of traffic crashes, and a proliferation of walking and biking in some neighbourhoods. As cities are slowly recovering from the pandemic, the challenge is to keep the positive impacts but also find ways to help the transit industry rebound from its plunge. Long-term impacts of the pandemic in terms of changing patterns of work and work arrangements, shopping, recreation, and other human activities that will affect travel need additional time and more research to discern.
policy brief
The COVID-19 pandemic catalyzed a massive and abrupt shift in work arrangements across the United States, with telecommuting (or working from home) becoming a dominant mode for a substantial portion of the workforce. This shift not only disrupted traditional employment structures but also significantly altered daily activity schedules and travel behavior. As policymakers and planners seek strategies to manage travel demand, mitigate congestion, and reduce greenhouse gas emissions, understanding the long-term implications of telecommuting on travel patterns is essential. The pandemic offers a unique opportunity to study these changes at scale and across diverse geographic and demographic groups.
published journal article |
Apr 2023
book/book chapter
working paper
This paper presents a policy sensitive approach to modeling travel behavior based on activity pattern analysis. A theoretical model of complex travel behavior is formulated on a recognition of a wide range of interdependencies associated with an individual’s travel decisions in a constrained environment. Travel is viewed as input to a more basic process involving activity decisions. A fundamental tenet of this approach is that travel decisions are driven by the collection of activities that form an agenda for participation; the utility of any specific travel decision can be determined only within the context of the entire agenda. Based on the theoretical model of complex travel behavior, an operational system of models, STARCHILD (Simulation of Travel/Activity Responses to Complex Household Interactive Logistic Decisions), has been developed to examine the formation of household travel/activity patterns, and is presented in a companion paper (Recker et al, 1985).
Phd Dissertation
Alternative fuel adoption by heavy-duty vehicle (HDV) fleets can bring substantial benefits to both current local communities and future generations by reducing air pollutants and greenhouse gas emissions. However, the penetration rate of alternative fuel vehicles (AFVs) is still very low in the HDV sector. Revealing HDV fleet operator perspectives towards alternative fuels can serve as the basis for developing effective policies for accelerating the diffusion of these technologies. This dissertation aims to fill a key knowledge gap, where such fleet operator perspectives have rarely been addressed, by exploring alternative fuel adoption behavior of HDV fleets.An initial theoretical framework was first developed based upon existing theories and literature to conceptually understand AFV fleet adoption behavior in organizations. This initial framework consists of a five-stage adoption process as well as two levels of sub-frameworks: at the decision-making unit level and the individual (e.g., vehicle drivers) acceptance level. Next, it was attempted to empirically improve the initial framework by investigating 20 organizations operating HDVs in the State of California via in-depth qualitative interviews and project reports. A total of 29 adoption and 42 non-adoption cases was probed across various alternative fuel technologies, including natural gas, propane, electricity, hydrogen, biodiesel, and renewable diesel options. The qualitative data was analyzed using content and thematic analyses, by which numerous themes and hypotheses were developed to build a theory explaining heavy-duty AFV fleet adoption behavior. Based on these qualitative inferences, a conceptual modelling framework was proposed for estimating demand for heavy-duty AFVs under different policy and technology advancement scenarios. An overall structure along with specific modules and components for this framework are presented. As an ongoing work, a stated preference choice experiment was designed to quantitatively operationalize one of the modules, to estimate AFV choice probabilities. The feasibility of this modelling approach is proposed to be examined in a case study interviewing California drayage fleet operators. Finally, the research findings contribute theoretically and empirically to a better understanding of the demand-side aspects of AFV adoption by HDV fleet operators, particularly in California and in the other US states that follow California’s environmental policies.
published journal article |
Jan 2022
journal article preprint
Structural equation modeling (SEM) is an extremely flexible linear-in-parameters multivariate statistical modeling technique. It has been used in modeling travel behavior and values since about 1980, and its use is rapidly accelerating, partially due to the availability of improved software. The number of published studies, now known to be more than fifty, has approximately doubled in the past three years. This review of SEM is intended to provide an introduction to the field for those who have not used the method, and a compendium of applications for those who wish to compare experiences and avoid the pitfall of reinventing previous research.