conference paper
published journal article
Our knowledge of complex travel behavior associated with transit commuting is limited. While chaining activities into tours has been a frequent behavior of travelers in general, and in some cases of work commuters, it is unclear how successful public transit has been in providing the accessibility that is needed to link multiple activities, especially on work tours. To address this knowledge gap, a structural model was developed for transit commuters, which allows for the characterization of commuters based on the complexity of work tours and an assessment of the influence of household and person-level socio-demographic characteristics, built environment variables, and activity-travel demand on tour complexity. Using data from the 2017 National Household Travel Survey, this study’s results suggested that married men with no children and high vehicle ownership living in low-density areas tend to make simple work tours while single women with children who live in high-density neighborhoods were more likely to make complex work tours. Also, white men with higher incomes and higher education living in denser areas were more likely to make complex tours with work-based sub-tours. Denser residential neighborhoods, flexible work schedules, and vehicle availability on work tours were observed to increase the propensity of making complex tours. The findings of this study can assist transit agencies or planning organizations in identifying transit commuters who have complex travel needs or whose circumstances hold potential to yield greater benefits from transit usage in work tours and thus to formulate policies directed at better work and non-work travel and activity linkages.
policy brief |
Apr 2021
research report |
Apr 2021
policy brief
published journal article |
Jun 2022
research report |
Apr 2021
policy brief
working paper
The Orange County Transit District (OCTD) has operated a community service transit program in the City of Orange, California since May of 1975. Because of an adverse court ruling and a subsequent successful appeal, this service underwent four modal changes. These four modes provide a unique opportunity for comparison. In order of implementation, they were: a demand-responsive Dial-A-Bus, a three-loop fixed route bus system, a two-loop fixed route bus system, and a demand-responsive Dial-A-Taxi system. Five performance indicators were used for the comparison, and the two demand-responsive systems were found more efficient and effective than the fixed route systems. The Dial-A-Taxi system, during its first three months of operation, compared very favorably to Dial-A-Bus. In addition, the Dial A-Taxi system continues to show monthly improvements on each indicator. There may be limited transferability of the information gained in this study, but the data suggests that Dial-A-Taxi can be very efficient and effective in serving cities or suburban areas with population densities of 5000 or less per square mile.
working paper
We estimate the rebound effect for motor vehicles, by which improved fuel efficiency causes additional travel, using a panel of US states for 1966-2001. Our model accounts for endogenous changes in fuel efficiency, distinguishes between autocorrelation and lagged effects, includes a measure of the stringency of fuel-economy standards, and interacts the rebound effect with income. At sample averages of variables, our 3SLS estimates of the short- and long-run rebound effect are 4.7% and 22.0%. But they decline substantially with income: with variables at 1997-2001 levels they become 2.6% and 12.1%, considerably smaller than typically assumed for policy analysis.
working paper
This paper develops an analytical framework for assessing the second-best optimal level of gasoline taxation taking into account unpriced pollution, congestion, and accident externalities, and interactions with the broader fiscal system. We provide calculations of the optimal taxes for the US and the UK under a wide variety of parameter scenarios. Under our central parameter values, and with the gasoline tax substituting for a distorting tax on labor income, the second-best optimal gasoline tax is $0.95/gal for the US and $1.29/gal for the UK. These values are moderately sensitive to alternative plausible parameter assumptions. The congestion externality is the largest component in both nations, and the higher optimal tax for the UK is due almost entirely to a higher assumed value for marginal congestion cost. Revenue-raising needs, incorporated in a “Ramsey” component, also play a significant role, as do accident externalities and local air pollution. However, we also find that a shift in taxation off gasoline and onto vehicle miles can produce much larger welfare gains than those from implementing second-best optimal gasoline taxes.
published journal article
This paper aims to quantify the extent to which transport and other factors impact on residential decisions using Oxfordshire, UK, as a case study. It investigates the impacts of the current dwelling, household characteristics and alternative properties on the probability of moving. It also highlights the trade-off between access, space and other attributes in residential location choice. Particular emphasis is placed on assessing the impact of transport and locationembedded amenities. A nested logit model is applied to estimate the indirect random utility functions of the intention to move and residential location choice based on stated preference data. The estimation results of the intentions to move model illustrate the impact of housing and household characteristics on the probability of moving. The estimation results of the residential location choice model quantify the trade-offs between transport, amenities and other factors.
Phd Dissertation
Freight forecasting models are data intensive and may require many explanatory variables to achieve prediction accuracy. One problem, particularly in the United States, is that public data sources are usually available only at highly aggregate geographic levels, while models with more disaggregate geographic levels are required for regional freight transportation planning. A second problem is that supply chain effects are often ignored or modeled with economic input-output models which lack explanatory power. This study addresses these challenges by considering a Structural Equation Modeling approach, that is not confined to a specific spatial structure as spatial regression models would be, and allows for correlations between industries. The goal of the proposed methodology is to design a reliable and policy sensitive modeling framework for long term commodity flow forecasting that makes the best use of public available data sources. Practicality and improvement over previously available freight generation and distribution models are the highlights of this approach. There are two primary developed in this study. The first one is a structural commodity generation model. The second model is the Structural Equations for Multi-Commodity OD Distribution (SEMCOD) model. The proposed framework is implemented as a primary module in California Statewide Freight Forecasting Model (CSFFM) and will be used to update the California Transportation Plan (CTP 2015). The models are specified and estimated based on FAF3 data. It is shown that the proposed modeling framework provides a better fit to the data than independent regression models for each commodity. The three components of the models are: direct and indirect effects, supply chain elasticities at zone level and at origin-destination level, and intra-zonal supply-demand interactions. A validation of the geographic scalability of the model is conducted using a zoning system consisting of 97 county or sub-county zones in California.
published journal article
Emerging mobility technologies, like ride-hailing, are underrepresented in travel behavior surveys. Consequently, travel demand modelers have often resorted to simplistic solo-traveler assumptions or relied on aggregate trip size factors within activity- and agent-based models. To address this gap and help modelers understand ride-hail party size, this study analyzed data collected from a 2021 to 2022 household travel survey in the greater Twin Cities (Minneapolis–St. Paul, MN) region. Ride-hail trips were paired with person-, household-, and trip-level attributes to provide context for both single-party and multiparty ride-hailing. Binary logistic regression models indicated that shared micromobility users, households with more children, renters in large multifamily buildings, and individuals with at most a high school education were significantly more likely to use ride-hailing for group travel than for solo trips. A hurdle regression for party size count revealed that lower-income households, while less likely to use ride-hailing, were more likely to opt for group travel and form larger parties than other income cohorts. These findings offer insights into who takes ride-hailing for group travel, enabling policy makers to devise strategies targeting solo, unshared ride-hailing without negatively affecting large groups.