presentation

Kent Distinguished Lecture, University of Illinois Transportation Center, Nov 2022: "Data, modeling and emerging technologies on the road to sustainable freight transportation."

Publication Date

November 1, 2022

Associated Project

Transition Pathways to a More Sustainable Heavy-Duty Vehicle Sector

Author(s)

Stephen Ritchie

Areas of Expertise

Freight, Logistics, & Supply Chain Zero-Emission Vehicles & Low-Carbon Fuels

View presentation

Related Publications

presentation | April 9, 2021

Invited Expert Testimony in 2021 on the California ”Heavy Duty Vehicle Sector” to the Joint Informational Hearing of the California Senate Committee on Transportation and Senate Budget Subcommittee 2 on Resources, Environmental Protection, and Energy, on The California Energy Commission’s Clean Transportation Program and California’s Zero Emissions Vehicle Deployment Strategy

Phd Dissertation

Matching Mechanisms for social good: case studies in transport congestion, low-income housing, and food surplus redistribution

Publication Date

August 1, 2022

Author(s)

Julius Aguma

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

For several decades now, matching mechanisms have been deployed, sometimes implicitly, to solve economic and social problems with unprecedented efficiency. Most notably, the kidney transplant matching algorithm has been key in saving many lives, 39,000 donation in 2019 alone. Likewise, the National Residency Matching Program successfully employed in the United States today uses a matching mechanism that places medical students into hospital residencies. A similar mechanism is used in college and public school admissions around the United States, most famously, in the Boston and New York City. This dissertation will continue this long history of applying matching mechanisms towards efficiently solving social problems.

We begin with a summary of relevant definitions and other terminology in chapter1, all of which will be applied in the chapters that follow. Chapter 2 will examine two concurrent social problems, over-production of resources that contributes to global waste, and lack of access to wasted resources by people living on the economic margin. We will contrast two possible solutions to both problems, that is, a decentralized vs a centralized matching solution. The feasibility of both solutions will be tested through theoretical investigation and two qualitative case studies in food surplus redistribution in the United Kingdom and allocation of housing to unhoused household in Los Angeles County during the pandemic.

Chapter 3 will give a more detail examination of allocation of housing to the unhoused by examining the efficiency and robustness to manipulation of the algorithm that was employed by LA county vs a centralized matching mechanism. Whereas Chapter 4 will explore an online matching mechanism solution to the problem of traffic congestion pricing. The proposed solution combines a matching algorithm, which assigns drivers to routes at the time of travel, with an anticipatory pricing mechanism that determines how much each traveling driver pays if they choose to use a congested route.

The conclusion will present open problems implied in the preceding three chapters.

Phd Dissertation

PUBLIC TRANSPORTATION AT A CROSSROADS Transportation Network Companies, COVID-19, and Transit Ridership

Publication Date

September 1, 2022

Author(s)

Farzana Khatun

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation

View Phd Dissertation

Abstract

Public transportation in the U.S., including in California, was declining before COVID-19, and the pandemic made a bad situation much worse. In this dissertation, I analyze data from the 2009 and 2017 National Household Travel Surveys and from a California survey administered in May 2021 by IPSOS using both discrete choice (cross-nested logit and generalized ordered logit) and quasi-experimental (propensity score matching) tools first to investigate how Transportation Network Companies (TNCs, e.g., Uber and Lyft) impacted transit ridership before COVID-19, before analyzing how COVID-19 affected transit and other modes.In Chapter 2, my results for the U.S. show that individuals/households who use either public transit or TNCs share socio-economic characteristics, reside in similar areas, and differ from individuals/households who use neither public transit nor TNCs. In addition, individuals/households who use both public transit and TNCs tend to be Millennials or belong to Generation Z, with a higher income, more education, no children, and fewer vehicles than drivers.In Chapter 3, I quantify the impact of TNCs on household transit use by comparing travel for households from the 2017 NHTS (who had access to both transit and TNCs) matched with households from the 2009 NHTS (who only had access to transit) using propensity score matching. Overall, I find a 22% drop for weekdays (1.6 fewer daily transit trips by each household) and a 15% decrease for weekends (1.4 fewer daily transit trips by each household).In Chapter 4, I analyze how Californians changed transportation modes due to COVID-19 and explore their intentions to use different modes after COVID-19. I find that driving but especially transit and TNCs could see substantial drops in popularity after the pandemic. Many Hispanics, African Americans, Asians, lower-income people, and people who would like to telecommute more intend to use transit less. Key obstacles to a resurgence of transit after COVID-19 are insufficient reach and frequency, shortcomings that are especially important to younger adults, people with more education, and affluent households (“choice riders”).My findings highlight the danger of public transit entering into outsourcing agreements with TNCs, neglecting captive riders, and exposing choice riders to TNCs.

published journal article

Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

Publication Date

August 1, 2022

Associated Project

Examining Spatial Disparities in Access to Electric Vehicle Charging Stations in Southern California

Author(s)

Avipsa Roy

Areas of Expertise

Zero-Emission Vehicles & Low-Carbon Fuels

View published journal article

Abstract

Electric vehicles (EVs) are an emerging mode of transportation that has the potential to reshape the transportation sector by significantly reducing carbon emissions thereby promoting a cleaner environment and pushing the boundaries of climate progress. Nevertheless, there remain significant hurdles to the widespread adoption of electric vehicles in the United States ranging from the high cost of EVs to the inequitable placement of EV charging stations (EVCS). A deeper understanding of the underlying complex interactions of social, economic, and demographic factors that may lead to such emerging disparities in EVCS placements is, therefore, necessary to mitigate accessibility issues and improve EV usage among people of all ages and abilities. In this study, we develop a machine learning framework to examine spatial disparities in EVCS placements by using a predictive approach. We first identify the essential socioeconomic factors that may contribute to spatial disparities in EVCS access. Second, using these factors along with ground truth data from existing EVCS placements we predict future ECVS density at multiple spatial scales using machine learning algorithms and compare their predictive accuracy to identify the most optimal spatial resolution for our predictions. Finally, we compare the most accurately predicted EVCS placement density with a spatial inequity indicator to quantify how equitably these placements would be for Orange County, California. Our method achieved the highest predictive accuracy (94.9%) of EVCS placement density at a spatial resolution of 3 km using Random Forests. Our results indicate that a total of 11.04% of predicted EVCS placements in Orange County will lie within a high spatial inequity zone – indicating populations with the lowest accessibility may require greater investments in EVCS placements. 69.52% of the study area experience moderate accessibility issues and the remaining 19.11% face the least accessibility issues w.r.t EV charging stations. Within the least accessible areas, 7.8% of the area will require a low density of predicted EVCS placements, 3.4% will require a medium density of predicted EVCS placements and 0.55% will require a high density of EVCS placements. The moderately accessible areas would require the highest placements of EVCS but mostly with low-density placements covering 54.42% of the area. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all. The findings from this study highlight a generalizable framework to quantify inequities in EVCS placements that will enable policymakers to identify underserved communities and facilitate targeted infrastructure investments for widespread EV usage and adoption for all.

Related Publications

other | August 26, 2023

Story Map: Examining Spatial Disparities in Electric Vehicle Charging Station Placements Using Machine Learning

published journal article | August 8, 2023

Examining Spatial Disparities in Electric Vehicle Charging Station Placements in Orange County

book/book chapter

The Impacts of Bus Use on COVID-19 Dispersion

Publication Date

July 23, 2022

Associated Project

Exploring the Role of Transit in COVID-19 Transmission

Author(s)

Henry Bernal, David Brownstone

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Safety, Public Health, & Mobility Justice

View book/book chapter

Abstract

This research examines how bus use impacts the transmission of the COVID-19 virus in urban areas, focusing on the evolution of the COVID-19 pandemic in Los Angeles County. Using data from the Los Angeles County Metropolitan Transportation Authority on station-level ridership in October 2019, April 2020, and October 2020, we impute station-level ridership for other months in our data and map these to 231 Countywide Statistical Areas (CSAs) in Los Angeles County, which are used by the Los Angeles Department of Public Health to report community COVID-19 transmission. We obtain CSA-specific COVID-19 case counts between March 16, 2020, and January 31, 2021, to create a monthly panel of bus ridership and COVID-19 cases. After using a dynamic panel regression, our findings provide no evidence that increased ridership levels or trip lengths are associated with a higher incidence of COVID-19 at the CSA level in Los Angeles County in the period between June 2020 and January 2021.

Related Publications

policy brief | April 1, 2021

Higher Bus Ridership Unlikely to Increase Community COVID-19 Transmission

book/book chapter

Workers and the Post-COVID Transportation Gig Economy

Publication Date

July 22, 2022

Associated Project

Evaluating the Labor, Environmental, and Safety Implications of Home Delivery Services and Ride-hailing Companies

Author(s)

Amelia Regan

Areas of Expertise

Freight, Logistics, & Supply Chain Public Transit, Shared Mobility, & Active Transportation

View book/book chapter

Abstract

The COVID-19 pandemic significantly reduced the demand for ride-hailing services but saw a sharp increase in e-commerce, grocery, and restaurant delivery services. As the economy recovers and demand increases, several issues are emerging. The tension between companies that wish to keep drivers as independent contractors, but which hope that large enough numbers of them return to the industry, and drivers who increasingly demand to be considered as employees will likely lead to more attractive labor contracts, and perhaps even unionization in the future. Prices for ride-hailing and delivery services are increasing rapidly, rendering the savings relative to the now mostly defunct taxi industry and traditional package delivery industries near zero. While that will lead to a reduction in demand, no one knows how much that reduction will be and how long it will last. This chapter addresses three overarching themes dominating analyses of these industries. The first is labor, the second is safety, and the third is environmental impacts.

research report

Factors Affecting Development Decisions and Construction Delay of Housing in Transit-Accessible and Jobs-Rich Areas in California

Publication Date

July 18, 2022

Associated Project

Factors Affecting Development Decisions and Construction Delay of Housing in Transit-Accessible and Jobs-Rich Areas in California

Author(s)

Doug Houston

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View research report

Abstract

Recent state legislation addresses California’s housing affordability crisis by encouraging new development in transit-accessible and/or jobs-rich areas. But policymakers lack key information about the effects of laws and plans on developers’ decisions about whether and where to build housing, and factors contributing to delays in receiving government development approvals in target areas. Drawing on a unique dataset detailing all residential projects of five units or more that were approved from 2014 through 2017 in selected California jurisdictions, this project analyzes how project attributes and transportation-related factors affected infill housing construction. The research team finds that in cities with extensive transit infrastructure, new projects were generally located in parts of the city with high proximity to transit, but that proximity to rail stops or high-frequency bus stops was not associated with extreme delays in project approval compared to all projects in general. The only factors related to extreme delay are the percentage of land within a half-mile radius of dedicated single-family housing and whether a multiunit project required a rezoning or general plan amendment, the latter of which is associated with a 326% increase in the odds of a project being extremely delayed. The paper’s findings suggest that cities could expedite transit-accessible housing development by ensuring that general plans and zoning accommodate multifamily development near transit.

Related Publications

policy brief | July 18, 2022

What Can Be Done to Speed Up Building Approval for Multifamily Housing in Transit-Accessible Locations?

policy brief

What Can Be Done to Speed Up Building Approval for Multifamily Housing in Transit-Accessible Locations?

Publication Date

July 18, 2022

Associated Project

Factors Affecting Development Decisions and Construction Delay of Housing in Transit-Accessible and Jobs-Rich Areas in California

Author(s)

Doug Houston, Jae Hong Kim

Areas of Expertise

Travel Behavior, Land Use, & the Built Environment

View policy brief

Abstract

California’s legislature has attempted to address the state’s housing affordability crisis in recent years by adopting numerous laws encouraging new development in transit-accessible and/or jobs-rich areas, but the evidence concerning the impacts of these laws on housing development remains largely anecdotal. In particular, policymakers lack adequate information concerning: (1) the types of neighborhoods where developers are more likely to build; and (2) the causes of delays in approvals for proposed projects in jobs-rich and transit-accessible areas. In new research, scholars from UC Irvine and UC Berkeley address this problem by drawing on a unique project-level dataset, the Comprehensive Assessment of Land Use Entitlements (CALES), to analyze development projects including five or more residential units that were approved for development from 2014 through 2017 in six cities: Inglewood, Long Beach, Los Angeles, Pasadena, Redondo Beach, and Santa Monica.

Related Publications

research report | July 18, 2022

Factors Affecting Development Decisions and Construction Delay of Housing in Transit-Accessible and Jobs-Rich Areas in California

Phd Dissertation

Modeling the interactions of new price-cost-ownership paradigms with traveler usage patterns and system performance in new shared autonomous mobility systems

Publication Date

June 17, 2022

Author(s)

Eduardo Marino Fernandez

Areas of Expertise

Public Transit, Shared Mobility, & Active Transportation Travel Behavior, Land Use, & the Built Environment

View Phd Dissertation

Abstract

Mobility systems are undergoing a major transformation due to emerging autonomous and shared mobility technologies. A primary aspect of such technologies is improving the mobility system inefficiencies via a reduction in the number of vehicles needed to fulfill the transportation needs. This would impact the use of vehicles and their expected lifetime. This dissertation is focused on the importance of the increased usage of vehicles and how the system can benefit from an optimization with a vehicle point of view. The improvements come from mainly two aspects of shared mobility – carsharing and ridesharing – which are both implemented in the modeling and optimization framework. An analysis of the current vehicle ownership and trip distributions is presented. A vehicle usage cost function is designed to incorporate the changed relative importance of fixed and usage-based variable costs. It presents a framework that analyzes the interactions between all the elements, including a pricing scheme for benefit-cost analysis and optimizations from a service provider perspective. With shared mobility, ownership paradigms can also change to subscription-based use of vehicles from fleet service providers, as included implicitly in the interaction framework. Modeling is carried out for idealized networks, as well as a real-world network of a reasonable size from the city of Irvine, CA. The results capture the increased use of shared and/or autonomous vehicles and the benefits of optimizing the system with properly updated costs. Results and conclusions are provided on the viability of service provider plans as well as on system benefits in terms of the replacement ratio indicating how many personal vehicles can be removed using autonomous fleets.

Phd Dissertation

Diffusion and Management of Disruptive Technology in Cities: The Case of Drones

Publication Date

June 30, 2022

Author(s)

Xiangyu Li

Areas of Expertise

Intelligent Transportation Systems, Emerging Technologies, & Big Data Other

View Phd Dissertation

Abstract

While the industry of civilian unmanned aerial vehicles (UAV) or drones has seen rapid expansion in the past decade, few studies have systematically examined the dynamics between this disruptive technology and various aspects of cities. Employing quantitative methods, this dissertation explores 1) the diffusion and adoption patterns of civilian drones; 2) how cities manage the challenges of increasing drone activities; and 3) the supply-side opportunities and constraints associated with the deployment of Urban Air Mobility (UAM) in built-out metropolitan areas. The results of the first county level study might suggest (Chapter 2) that the digital divide has magnified the uneven and nonlinear diffusion of drones across time and space. Furthermore, the strength of state-level interventions correlates with the intensity of local drone adoption, even though the regulatory effects are different among drone user groups. People living in neighborhoods with a higher adoption rate of drones are on average younger, more affluent, and Whiter. An extension of the first study at the zip code level (Chapter 3) has retested the key results and provided additional insights into the spatial dependence effects that affect the drone adoption patterns. Furthermore, the results of the second study (Chapter 4) indicate that local drone policy adoption among communities of color trails behind that of other communities. Although drone policy adoption at the local level has been shaped by both motivation and capacity factors, the desire to protect public facilities appears to motivate localities to adopt regulatory measures. In particular, policy adoption is influenced by what nearby cities do, suggesting that strategic interaction is at play among local governments. In the third study (Chapter 5), I evaluate the supply-side opportunities and constraints associated with UAM adoption through a systematic scenario analysis. The results of the third study indicate that current supply-side infrastructure opportunities in Southern California, like helipads and elevated parking structures, are widely available to accommodate the regional deployment of UAM service although current spatial constraints can significantly limit the location choice of UAM landing sites (vertiports) for electric vertical take-off and landing (eVTOL) aircraft. Moreover, the low-income and young populations tend to live relatively farther away from the supply-side opportunities compared to the general population. The third study also proposes a network of UAM stations in Southern California based on the joint considerations of available infrastructure and home-workplace commuting flows.