Phd Dissertation

Commute Mode Choice, Parking Policies, and Social Influence

Publication Date

May 31, 2019

Author(s)

Abstract

This dissertation examines the impact of parking policies and social influence on commute mode choice using discrete choice analysis. A key feature of the dissertation is overcoming the problem of insufficient data by using unique datasets, building unique datasets, or exploring appropriate estimation strategies and assumptions.

Chapter 1 studies the impact of parking prices on the decision to drive to work using the California Household Travel Survey. The chapter tackles estimation challenges posed by insufficient parking information. The first challenge is the estimation of parking prices for those who do not drive, which is addressed by using a sample selection model. The second challenge is to understand the effect of the extent of the prevalence of Employer-Paid parking coupled with incentive programs offered in-lieu of parking. To address this challenge, two extreme scenarios are examined, and a range for the marginal effects of parking prices is estimated; one scenario assumes everyone receives Employer-Paid parking coupled with in-lieu of parking incentives, and the second assumes that no one is offered such incentives. The results suggest that higher parking prices reduce driving, regardless of the followed approach. It is estimated that a 10% increase in parking prices leads to a 1 – 2 percentage point decline in the probability of driving to work. Moreover, there seems to be no evidence of sample selection bias. The evidence suggests that parking pricing can indeed be an effective transportation demand management tool.

Chapter 2 extends the analysis of Chapter 1 to simultaneously estimate the impact of parking pricing, parking availability, and urban form on commute mode choice. The joint role of these three factors is examined using a dataset that is constructed by merging three major different data sources. The California Household Travel Survey data are matched to two unique datasets on parking for Los Angeles County; one for prices and the other availability. Chapter 2 first examines how these three factors affect the binary decision of whether to drive, while controlling for a rich set of covariates. The analysis then becomes more specific and examines how these factors affect particular commute modes in a multinomial context. The results indicate that parking prices have a significant negative impact on the decision to drive to work, where a 10% increase in parking prices is associated with a 1.1% drop in the probability of driving to work. Both on-street and off-street parking availability at home, as well as urban form measures of the workplace tract, are found to significantly affect commute mode choices. These findings have important policy implications in terms of minimum parking requirements, maximum parking standards, employer-paid parking, and parking pricing policies.

Chapter 3, on the other hand, examines the impact of a number of fundamental determinants of commute mode choice on transit use, and introduces the role of social influence. The determinants explored cover socioeconomic characteristics, built environment and neighborhood characteristics, transit accessibility, and trip characteristics. Social interactions have been found to affect many of the decisions of economic agents, and are likely to play a role in the decision to use transit. A unique dataset is built to conduct this analysis across a number of major US cities and examine the effects in both the residence and workplace neighborhoods, where a neighborhood is defined as a census tract. Social influence is explored along three different dimensions: space (neighborhood), income, and race. A novel instrumental variable is constructed in order to identify spatial social influence, and an alternative identification strategy is devised to identify income-group and racial social influence. The evidence suggests that spatial social influence exists among both coworkers and residential neighbors, and that peer effects among coworkers are larger than those among residential neighbors. Moreover, income-group social influence, among both coworkers and residential neighbors, plays a significant role in the rich commuter’s decision to use transit. However, racial social influence does not affect a commuter’s decision to use transit, regardless of race.

published journal article

Integrating Autonomous Vehicles in Multimodal Peer-to-peer Shared Mobility Systems and its Network Impacts

Abstract

As public perception of sharing economy in transportation has changed, mobilephone-hailed ridesharing is gaining prominence. The key aspect of capitalizing and promoting better shared-mobility systems depends on the matching rate between the supply and demand for rides. Peer-to-peer (P2P) ridesharing systems devise higher matching rate than pure ridesharing systems by attracting more drivers. Even relaxing the spatiotemporal constraints for participants could increase the chances to be matched. However, we notice that sole P2P ridesharing systems still do not guarantee matching when the number of drivers is limited. We propose the utilization of a fleet service to cover the unmatched riders in P2P ridesharing. While it can be any type of fleet services such as taxis, Uber/Lyft, or paratransit, we explore the idea of utilizing shared autonomous vehicles as a fleet, as they can be dispatched without labor. We model an integrated system for P2P ridesharing and shared autonomous fleet vehicles (SAFVs). The proposed algorithm is designed to maximize matching ratio while optimizing the number of required SAFVs. Based on a simulated study on the northern Los Angeles, the integrated shared-mobility system is shown to have high potential to serve a high fraction of riders.

policy brief

Compact, Accessible, and Walkable Communities Help Support Gender Equality

Abstract

In California, Senate Bill 375 mandates regional planning organizations align their transportation plans with sustainable land use and development strategies to achieve reductions in greenhouse gas emissions. In response, the Southern California Association of Governments’ 2016 Regional Transportation Plan/Sustainable Community Strategy directs nearly 50% of housing and employment growth between 2010 and 2040 into walkable and compact neighborhoods within a one-half mile walking distance from well-serviced transit stops. This approach to land use development can encourage shorter driving trips, greater transit usage, and increased walking and cycling as a result of daily activity destinations being clustered near residential and work locations.1Another bi-product and benefit of compact and accessible communities may be improving gender equality related to travel and activity patterns. Prior research shows segregated and dispersed land uses (i.e., suburban sprawl) can exacerbate gender disparities in daily household travel by separating the public and private realms, and can also constrain women to their immediate neighborhoods.2,3 In contrast, neighborhoods with pedestrian accessible mixes-use centers have been shown to help counter social isolation of women in suburbia.4In addition, compact communities with denser land use and better transit service has been shown to reduce the disproportionate amount of chauffeuring women conduct on behalf of the household.

policy brief

Transit Investments are Having an Impact on Land Use Beyond the Half-Mile Mark

Abstract

Recent years have witnessed a growing interest in transit-
oriented development (TOD) and other transit-centered
initiatives. It has been widely presumed that transit investment
can significantly contribute to curbing sprawl and creating
a more compact (and thus more sustainable) pattern of
urban land use, while providing a broader range of travel
options. However, little is known about how investments in
the public transit system modify urban land use patterns and
the geographical extent of impacts. Prior research tends to
assume transit lines and stations are homogeneous and have
similar impacts without careful consideration of development
history, service quality, or other variations. In addition, prior
research and current practice often assume transit impacts
are concentrated within a half-mile, which has limited the
understanding of how transit investments impact the broader
vicinity.

Phd Dissertation

Real Options Models for Better Investment Decisions in Road Infrastructure under Demand Uncertainty

Abstract

An efficient transportation system requires adequate and well-maintained infrastructure to relieve congestion, reduce accidents, and promote economic competitiveness. However, there is a growing gap between public financial commitments and the cost of maintaining, let alone expanding the U.S. road transportation infrastructure. Moreover, the tools used to evaluate transportation infrastructure investments are typically deterministic and rely on present value calculations, even though it is well-known that this approach is likely to result in sub-optimal decisions in the presence of uncertainty, which is pervasive in transportation infrastructure decisions. In this context, the purpose of this dissertation is to propose a framework based on real options and advanced numerical methods to make better road infrastructure decisions in the presence of demand uncertainty. I first develop a real options framework to find the optimal investment timing, endogenous toll rate, and road capacity of a private inter-city highway under demand uncertainty. Traffic congestion is represented by a BPR function, competition with an existing road is captured by user equilibrium, and travel demand between the two cities follows a geometric Brownian motion with a reflecting upper barrier. I derive semi-analytical solutions for the investment threshold, the dynamic toll rates and the optimum capacity. The result shows the importance of modeling congestion and an upper demand barrier — features that are missing from previous studies. I then extend this real options framework to study two additional ways of funding an inter-city highway project: with public funds or via a Public-Private Partnership (PPP). Using Monte Carlo simulation, I investigate the value of a non-compete clause for both a local government and for private firms involved in the PPP. Since road infrastructure investments are rarely made in isolation, I also extend my real options framework to the multi-period Continuous Network Design Problem (CNDP), to analyze the investment timing and capacity of multiple links under demand uncertainty. No algorithm is currently available to solve the multi-period CNDP under uncertainty in a reasonable time. I propose and test a new algorithm called “Approximate Least Square Monte Carlo simulation” that dramatically reduces the computing time to solve the CNDP while generating accurate solutions

research report

Evaluating the Impacts of Start-Up and Clearance Behaviors in a Signalized Network: A Network Fundamental Diagram Approach

MS Thesis

The Effects of VMT on Travel Demand and Implied Equity Issues

Abstract

The purpose of this thesis was to analyze the California Household Travel Survey to examine any differences in travel behavior and demographics between two of California’s multi-county Metropolitan Planning Organization (MPO) areas, the Southern California Association of Governments (SCAG) and the Metropolitan Transportation Commission (MTC). As these regions continue to grow, they have witnessed significant gentrification affecting marginalized communities that are already struggling against increasing costs of living. There were significant differences in both travel times and distance traveled with the SCAG region having values slightly higher than MTC. However, within each region there were significant differences in income and racial demographics at the county level. In SCAG, Orange County had the highest Average HH level incomes and San Bernardino and Imperial Counties having the lowest average HH level incomes. Within the MTC area African Americans and Native Americans were found to more likely walk and use public transit more than other group due to these groups having the lowest incomes out of other groups. Also, these groups tend to reside in Contra Costa and Alameda Counties which have the lowest housing costs in the MTC region.

policy brief

Evaluating the Impacts of Start-Up and Clearance Behaviors in a Signalized Network: A Network Fundamental Diagram Approach

published journal article

How do they get by without cars? An analysis of travel characteristics of carless households in California

Abstract

In spite of their substantial number in the U.S., our understanding of the travel behavior of households who do not own motor vehicles (labeled “carless” herein) is sketchy. The goal of this paper is to start filling this gap for California. We perform parametric and non-parametric tests to analyze trip data from the 2012 California Household Travel Survey (CHTS) after classifying carless households as voluntarily carless, involuntarily carless, or unclassifiable based on a CHTS question that inquires why a carless household does not own any motor vehicle. We find substantial differences between our different categories of carless households. Compared to their voluntarily carless peers, involuntarily carless households travel less frequently, their trips are longer and they take more time, partly because their environment is not as well adapted to their needs. They also walk/bike less, depend more on transit, and when they travel by motor vehicle, occupancy is typically higher. Their median travel time is longer, but remarkably, it is similar for voluntarily carless and motorized households. Overall, involuntarily carless households are less mobile, which may contribute to a more isolated lifestyle with a lower degree of well-being. Compared to motorized households, carless households rely a lot less on motor vehicles and much more on transit, walking, and biking. They also take less than half as many trips and their median trip distance is less than half as short. This study is a first step toward better understanding the transportation patterns of carless households.