Routing is a key step in transport planning and research. Nonetheless, researchers and practitioners often face challenges when performing this task due to long computation times and the cost of licensed software. R⁵ is a multimodal transport network router that offers multiple routing features, such as calculating travel times over a time window and returning multiple itineraries for origin/destination pairs. This paper describes r5r, an open-source R package that leverages R⁵ to efficiently compute travel time matrices and generate detailed itineraries between sets of origins and destinations at no expense using seamless parallel computing.

The advent of ridehailing services such as Uber and Lyft has expanded for-hire vehicle travel. We use data from the 2017 National Household Travel Survey (NHTS) to investigate the extent of this expansion in the United States. We report changes in the for-hire vehicle market since ridehailing services became available and statistically estimate the determinants of ridehailing use. From 2009–2017, the for-hire vehicle market share doubled. While for-hire vehicles still only account for 0.5% of all trips, the percent of all Americans who use ridehailing in any given month is nearly 10%. Within the for-hire vehicle market, this trend of growth has not been uniformly distributed across demographic groups or geographies; it has been greater in mid-sized and large cities, and among younger individuals and wealthier households. This suggests that understanding the equity implications of ridehailing is an important avenue for research. Multivariate analysis provides evidence that both transit and nonmotorized transport use are correlated with ridehailing use, that ridehailing has a negative relationship with vehicle ownership, and that residents of denser areas have higher ridehailing use. Given the rapid growth of ridehailing, it has become important for cities to include for-hire vehicles in their planning going forward. These NHTS data provide a starting point, but more detailed and frequent data collection is needed to fully understand this many-faceted, rapidly-changing market.

Attitudes and habits are extremely resistant to change, but a disruption of the magnitude of the COVID-19 pandemic has the potential to bring long-term, massive societal changes. During the pandemic, people are being compelled to experience new ways of interacting, working, learning, shopping, traveling, and eating meals. Going forward, a critical question is whether these experiences will result in changed behaviors and preferences in the long term. This paper presents initial findings on the likelihood of long-term changes in telework, daily travel, restaurant patronage, and air travel based on survey data collected from adults in the United States in Spring 2020. These data suggest that a sizable fraction of the increase in telework and decreases in both business air travel and restaurant patronage are likely here to stay. As for daily travel modes, public transit may not fully recover its pre-pandemic ridership levels, but many of our respondents are planning to bike and walk more than they used to. These data reflect the responses of a sample that is higher income and more highly educated than the US population. The response of these particular groups to the COVID-19 pandemic is perhaps especially important to understand, however, because their consumption patterns give them a large influence on many sectors of the economy.

Accessibility is increasingly used as a metric when evaluating changes to public transport systems. Transit travel times contain variation depending on when one departs relative to when a transit vehicle arrives, and how well transfers are coordinated given a particular timetable. In addition, there is necessarily uncertainty in the value of the accessibility metric during sketch planning processes, due to scenarios which are underspecified because detailed schedule information is not yet available. This article presents a method to extend the concept of "reliable" accessibility to transit to address the first issue, and create confidence intervals and hypothesis tests to address the second.

There is a need for indicators of transportation–land use system quality that are understandable to a wide range of stakeholders and that can provide immediate feedback on the quality of interactively designed scenarios. Location-based accessibility indicators are promising candidates, but indicator values can vary strongly depending on time of day and transfer wait times. Capturing this variation increases complexity, slowing calculations. This paper presents new methods for rapid yet rigorous computation of accessibility metrics, allowing immediate feedback during early-stage transit planning while being rigorous enough for final analyses. The approach is statistical, characterizing the uncertainty and variability in accessibility metrics related to differences in departure time and headway-based scenario specification. The analysis was carried out on a detailed multimodal network model including both public transportation and streets. Land use data were represented at high resolution. These methods were implemented as open-source software running on a commodity cloud infrastructure. Networks were constructed from standard open data sources, and scenarios were built in a map-based web interface. A case study is presented, describing how these methods were applied in a long-term transportation planning process for an urbanized, polycentric Randstad region in the Netherlands.

This study focuses on the long-term impacts of COVID-19 on telecommuting behavior. We seek to study the future of telecommuting, in the post-pandemic era, by capturing the evolution of observed behavior during the COVID-19 pandemic. To do so, we implemented a comprehensive multi-wave nationwide panel survey (the Future Survey) in the U.S. throughout 2020 and 2021. A panel Generalized Structural Equation Model (GSEM) was used to investigate the effects of two perceptual factors on telecommuting behavior: (1) perceived risk of COVID-19; and (2) perceived telecommuting productivity. The findings of this study reveal significant and positive impacts of productivity and COVID-risk perception on telecommuting behavior. Moreover, the findings indicate a potential shift in preferences toward telecommuting in the post-pandemic era for millennials, employees with long commute times, high-income, and highly educated employees. Overall, a potential increase in telecommuting frequency is expected in the post-pandemic era, with differences across socio-economic groups.