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Conventional four-step travel demand models, used by most metropolitan planning organizations (MPOs), state departments of transportation, and local planning agencies, are the basis for long-range transportation planning in the United States. Trip distribution—whether the trip is intrazonal (internal) or interzonal (external)—is one of the essential steps in travel demand forecasting. However, the current intrazonal forecasts based on a gravity model involve flawed assumptions, primarily due to a lack of considerations on differences in zone size, land use, and street network patterns. In this study, we first survey 25 MPOs about how they model intrazonal travel and find the state of the practice to be dominated by the gravity model. Using travel data from 31 diverse regions in the U.S., we develop an approach to enhance the conventional model by including more built environment D variables and by using multilevel logistic regression. The models' predictive capability is confirmed using k-fold cross-validation. The study results provide practical implications for state and local planning and transportation agencies with better accuracy and generalizability. [ABSTRACT FROM AUTHOR]

Conventional corridor management has focused on travel speed and efficiency while ignoring interactions between highway corridors and surrounding areas. This study investigates the interaction between the corridor highway system and the built environment around highways by estimating sustainable mode choices with respect to the land use characteristics of surrounding corridor highways throughout 29 regions in the U.S. The results indicate that the D variables at the corridor level matter. D variables at the corridor level, such as activity density, job-population balance, percentage of accessible jobs within 10 min by car, intersection density, percentage of 4-way intersections, and transit stop density, were highly associated with internal capture, walking trips, and transit use. The neighborhoods with higher activity density, job-population balance, intersection density, and better employment accessibility by transit experienced reduced VMT. The likelihood of internal trips, walking trips, and transit use decreased with household size and income; the opposite signs were found with VMT. The regional gasoline price had significant effects on all trips. In conclusion, this study provides planning and transportation researchers and practitioners with new evidence on the importance of built environmental characteristics (D variables) to encourage sustainable mode choices in the proximate neighborhoods of corridor highways. • The D variables at the highway corridor level matter for sustainable travel. • Neighborhoods around highway corridors with richer D variables generate less VMT. • Neighborhoods with richer D variables captured more trips internally. [ABSTRACT FROM AUTHOR]

With years in the market, ride-hailing services have generated broad impacts on almost every aspect of transportation, from the system level – such as congestion, vehicle miles traveled (VMT), transit ridership, etc. – to the household/individual level – such as vehicle ownership, mode choice, destination choice, etc. This study aims to explore the influences of ride-hailing services on VMT and transit usage at the household level by controlling for both socioeconomic and built environmental characteristics by using pre-COVID-19 data from California. While spatial distributions, descriptive statistics, and statistical tests reveal some connections between ride-hailing usage, household VMT generation, and transit usage, a few of these relationships lose statistical significance in multilevel two-stage regressions once the built environment where people reside is controlled for. The two remaining significant relationships include: 1) occasional ride-hailing usage (1–3 times per month) correlates with increased household VMT generation, and 2) frequent ride-hailing usage (4 or more times per month) is linked to a higher likelihood of utilizing any transit services. Together with previous literature, this study suggests that the associations between ride-hailing services and household VMT generation and transit usage may vary at the household level and depend on the frequency of usage, local contexts, household characteristics, etc., such as the built environment of where people live, employment status, household income, etc. Policymakers and planners should consider all factors to create efficient, sustainable and equitable transportation systems for all. • Influences of ride-hailing are less significant after controlling built environment. • Occasional ride-hailing usage is associated with higher VMT generation. • Frequent ride-hailing usage increases the probability of taking any transit. • Influences of ride-hailing services vary at the individual and local level. [ABSTRACT FROM AUTHOR]

This study pools household travel and built environment data from 15 diverse US regions to produce travel models with more external validity than any to date. It uses a large number of consistently defined built environmental variables to predict five household travel outcomes – car trips, walk trips, bike trips, transit trips and vehicle miles travelled (VMT). It employs multilevel modelling to account for the dependence of households in the same region on shared regional characteristics and estimates ‘hurdle’ models to account for the excess number of zero values in the distributions of dependent variables such as household transit trips. It tests built environment variables for three different buffer widths around household locations to see which scale best explains travel behaviour. The resulting models are appropriate for post-processing outputs of conventional travel demand models, and for sketch planning applications in traffic impact analysis, climate action planning and health impact assessment. [ABSTRACT FROM AUTHOR]

• Four-step modeling is still the state-of-the-practice. • More than half of the surveyed MPOs do not model vehicle ownership. • Vehicle ownership increases with household size, income, and number of workers. • Vehicle ownership decreases with almost all of the built environmental variables. • Count regression models have better predictive accuracy than discrete choice models. [ABSTRACT FROM AUTHOR]

• 622 MXDs were identified in 31 metropolitan regions across the United States. • Well-designed MXDs have walk shares of more than 50 percent on internal trips. • MXDs with diverse activities and good transit service capture more internal trips. • Certain MXDs generate significant walk, bike, and transit shares on external trips. • A k-fold cross-validation supports the robustness of the analyses. This study aims to improve a previously-developed methodology for predicting the traffic impacts of mixed-use developments (MXDs). In 31 diverse metropolitan regions across the United States, we collected consistent regional household travel survey data and computed built environment characteristics—D variables—of MXDs. Multilevel modeling (MLM) was employed to predict the probability of trips captured internally within MXDs, walking on internal trips, and travel mode choice on external trips, by trip purpose. Larger, denser, mixed-use, and more walkable MXDs show a larger share of trips internally, compared with conventional suburban developments. Those MXDs with good access to transit, employment, and destinations also show higher levels of walking, biking, and transit shares on external trips, thus helping to reduce traffic impacts on the external road network. Perhaps the most impressive finding is that well-designed MXDs have walk shares of more than 50 percent on internal trips. A k-fold cross-validation supports the robustness of our analyses. [ABSTRACT FROM AUTHOR]

• The built environment has an influence on Uber demand beyond simple socioeconomics. • More Uber demand is expected as density, diversity, and distance to transit increase. • Lower Uber demand is expected as accessibility and intersection density increase. Ride-sourcing services have made significant changes to the transportation system, essentially creating a new mode of transport, arguably with its own relative utility compared to the other standard modes. As ride-sourcing services have become more popular each year and their markets have grown, so have the publications related to the emergence of these services. One question that has not been addressed yet is how the built environment, the so-called D variables (i.e., density, diversity, design, distance to transit, and destination accessibility), affect demand for ride-sourcing services. By having unique access to Uber trip data in 24 diverse U.S. regions, we provide a robust data-driven understanding of how ride-sourcing demand is affected by the built environment, after controlling for socioeconomic factors. Our results show that Uber demand is positively correlated with total population and employment, activity density, land use mix or entropy, and transit stop density of a census block group. In contrast, Uber demand is negatively correlated with intersection density and destination accessibility (both by auto and transit) variables. This result might be attributed to the relative advantages of other modes – driving, taking transit, walking, or biking – in areas with denser street networks and better regional job access. The findings of this paper have important implications for policy, planning, and travel demand modeling, where decision-makers seek solutions to shape the built environment in order to reduce automobile dependence and promote walking, biking, and transit use. [ABSTRACT FROM AUTHOR]