Phd Dissertation

Disaggregate Control of Vehicles using In-Vehicle Advisories and Peer-to-Peer Negotiations

Abstract

Traffic advisories to travelers are based upon traffic state information at the link level. This is due to existing infrastructure which sometimes can only provide link-level information. However, the primary justification for providing link-level data is the reluctance of Traffic Management Agencies to consider more detailed traffic state data for operational and safety reasons. However, with the advances in automotive technology, sensing equipment, and the Internet of Things (IoT), we can do better. Research shows that faster and more accurate travel paths can be obtained by using lane data rather than link data. Our contention is that for vehicles to be able to change lanes to improve their travel times, operationally, they would need to enter into Peer-to-Peer negotiations with surrounding vehicles, where they can trade their position in time and space in accordance to their own perceptions of their values of time and satisfaction and possibly in exchange for monetary benefits. Our work is an exploration of this idea. We begin with a simple in-vehicle advisory control policy, partially inspired by the Kinetic theory of traffic. We then move towards an individual-level Peer-to-Peer negotiated lane change framework by first investigating its efficacy by means of microsimulation studies. We then propose an agent-based optimization framework for this system, which minimizes both travel time and the “envy” induced among drivers when they are assigned paths that are inferior to their peers. Numerical results from running our optimization on an illustrative network show that the proposed model converges to both envy-free and system optimum traffic states, even at a net zero budget, meaning this system can be used by transportation agencies without exacting tolls or giving subsidies. Our proposed framework of routing vehicles on a lane to lane basis can only be realized in the field if the mediating agency (TMC, or a mobility service) has accurate information about traffic conditions. We propose multiple algorithms, including a LSTM (Long Short Term Memory) neural network architecture-based framework to estimate traffic states solely using information collected from sensor-equipped probe vehicles, without the need for any other data such as those obtained from traditional embedded loop detectors. 

policy brief

Higher Bus Ridership Unlikely to Increase Community COVID-19 Transmission

Abstract

Public transportation has been blamed for facilitating the spread of COVID-19 in dense, urban areas. As a response to the COVID-19 pandemic, transit agencies have reduced service and implemented mask-wearing mandates and social distancing aboard transit. Some prior studies that address public transportation provide some evidence that negative COVID-19 outcomes are linked to high transit use. One early study of COVID-19 transmission on trains in China found that transmission is also affected by the density of passengers, seat spacing, and length of time traveled with other passengers aboard the trains.

policy brief

Non-myopic pathfinding for shared-ride vehicles: A bicriteria best-path approach considering travel time and proximity to demand

research report

Non-myopic pathfinding for shared-ride vehicles: A bicriteria best-path approach considering travel time and proximity to demand

research report

Software and Hardware Systems for Autonomous Smart Parking Accommodating both Traditional and Autonomous Vehicles

Phd Dissertation

Understanding the Travel Behaviors and Activity Patterns Using Household-based Travel Diary Data: An Activity Space-based Approach in a Developing Country Context

Publication Date

February 28, 2021

Author(s)

Abstract

Measuring the geographic extent of travel-activity patterns is important to develop our knowledge on potential and actual activity spaces around individual travel routes and activity locations which will enrich our understanding of human activities. Although a handful of studies integrate activity space within the travel behavior analysis in Europe and U.S. context, few studies have measured the size, structure, and implications of human activity spaces in the context of developing countries. To address these concerns, this dissertation examines the impact of land-use characteristics, socio-demographics, individual trip characteristics, and personal attitudes on travel-activity based spatial behavior in Dhaka, capital city of Bangladesh. Two methods-shortest-path network (SPN) and road network buffer (RNB) were used for calculating activity space in a geographic information system (GIS). First, a household-based travel diary pilot survey was carried out in 2017. Pilot data shows some specific socio-economic and travel differences across two study subareas. Results of this essay help to understand the differences between travel and activity space patterns by study subareas and population subgroups and give specific directions in terms of survey sampling and methodology for the full study to identify most suitable models, sets of indicators, and measurement techniques. Based on lessons learned from the pilot study, a weeklong household-based travel diary survey was conducted in 2018. Multiple Regression Analysis (MRA) results show that mainly land use characteristics are found to be consistently significant predictors of both individual and household activity space size. In this dissertation, Exploratory and Confirmatory Factor Analysis (EFA and CFA) are used to identify attitudinal factors to influence spatial behavior. Household accessibility to different facilities was assessed under this essay using RNB measure. Positive correlations are found between the area and number of all opportunities except open space facility. While examining heterogeneity in activity spaces, results indicate that activity spaces vary from day to day. To further analyze the impact of different indicators on this variability, Panel Regression Model (PRM) is used. My findings help transport planners, researchers, and policy makers to reshape land use policies while keeping in mind human accessibility and activity space variability issues. 

policy brief

Software and Hardware Systems for Autonomous Smart Parking Accommodating both Traditional and Autonomous Vehicles

policy brief

California Can Simplify the Housing Element Law to Reduce Administrative Burdens and Improve Social Equity

Abstract

California’s Housing Element law requires all local governments to adequately plan to meet the state’s existing and future housing needs. The law establishes processes for determining regional housing needs and requires regional councils of governments (COGs) with allocating these housing needs to cities and counties in the form of numerical targets. Local governments must update the housing element of their general plans and adopt policies to accommodate the housing targets. The California Department of Housing and Community Development (HCD) reviews all local housing elements and determines whether the elements comply with state law.

Phd Dissertation

GREENING U.S. HOUSEHOLDS’ DRIVING CHOICES: Insights from the 2017 NHTS about carsharing and BEV adoption

Abstract

According to the California Air Resources Board (CARB, 2020), light-duty vehicles are responsible for 13 percent of statewide NOx emissions and 28 percent of statewide greenhouse gas emissions. Scientists, policymakers, and car manufacturers have been striving to reduce the air pollution and greenhouse gas emissions from the transportation sector using various measures, ranging from cleaner engines to alternatives to driving to reduce VMT. In this dissertation, I focus on a subset of these measures: carsharing programs and Battery Electric Vehicles (BEVs). In the first part of this dissertation, I explore the profile of households engaging in carsharing by estimating zero-inflated negative binomial (ZINB) models on data from the 2017 National Household Travel Survey (NHTS). My results show that households who are more likely to carshare are those who participate in other forms of sharing, have more Silent generation members, are less educated (the highest educational achievement is a high school degree), and have fewer vehicles than drivers. Conversely, households with more young adults (18 – 20 years old), with 2 or more adults and no children, take part in carsharing program less often. Moreover, households who took more part in ridesharing and have fewer vehicles than drivers are less likely to never carshare. Furthermore, households whose annual income between $75,000 and $150,000 are more likely to never carshare.In the second part of this dissertation, I concentrate on the adoption of BEVs. More specifically, I focus on two questions: 1) What are the characteristics of households who own battery electric vehicles (BEVs)?; and 2) Does the travel behavior of these households differ from the travel of households who have motor vehicles but not BEVs? To answer those questions, I characterize three groups of households based on their vehicle holdings: BEV-only, BEV+ (i.e., households with both one or more BEV and at least one conventional vehicle), and non-BEV households. I analyze data from the 2017 NHTS using mixed methods. Results show that BEV households are more likely to be Asian, well-educated, with a higher income and to live in higher population and employment density areas. Furthermore, BEV-only households are more likely to be composed of one adult (not retired) with fewer Baby Boomers. Yet, BEV+ households are more likely to be larger households with 2 or more adults. Also, BEV+ households are more likely to have more Generation X (37-52 years old in 2017) and Z members (20 years old or younger in 2017). They are also more likely to own their home. My analysis on gender (at the individual level) concluded that BEV owners are more likely to be men. Furthermore, I find that BEV households travel as much as non-BEV households.Although carsharing and BEVs could substantially decrease the environmental footprint of transportation, they are currently far from mainstream. To promote carsharing programs, their reach could be extended, they could be made more affordable, while increasing the cost of owning and operating private vehicles. Similarly, state and federal governments could continue to provide financial incentives to lower the purchase price difference between conventional and BE vehicles, manufacturers could provide extended warranties on batteries, and the charging infrastructure needs to be developed in order to attract more customers. The Covid-19 crisis is giving governments around the world an opportunity to invest in clean technologies to jumpstart the economy. It is critical to take advantage of this crisis to reduce air pollution and greenhouse gas emissions from transportation for the good of current and future generations.

research report

Accessibility, Affordability, and the Allocation of Housing Targets to California’s Local Governments

Abstract

California’s Housing Element law establishes processes for determining regional housing needs and allocating these housing needs to cities and counties in the form of numerical targets. This study assesses whether the state’s housing allocation process achieves the state’s goals of promoting housing development in areas accessible to transit, jobs, and socioeconomic opportunities. The first analysis compares the mechanism that the Southern California Association of Governments (SCAG) uses to allocate housing units to local governments with two simpler alternatives. For all three allocation mechanisms, the research team assesses whether the resulting allocations align with the goal of promoting housing development in areas with high social mobility and near transit and jobs. The team finds that the Southern California Association of Governments’ allocation method may be unnecessarily complex and that simpler allocation methods – which are less susceptible to technical difficulties and political wrangling – could achieve the state’s policy objectives with less administrative burden. The second analysis, based on case studies of two Southern California cities, provides preliminary evidence that current enforcement mechanisms adopted in California may be insufficient to ensure that local governments accommodate their housing targets and promote housing development near transit and job centers.