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Existing methodology on food accessibility predominately focuses on on-premise services, that is, dine-in and shopping at stores, which assumes a linear distance decay property (the closer, the higher accessibility). Access to delivery services is fundamentally different from that to on-premise stores. Stores with close proximity (within an inner boundary) are less desirable for delivery due to delivery fees, and there is an outer boundary beyond which deliveries are unavailable, both challenging the assumption of increasing impediment with distance. These two boundaries form a donut shape for delivery services. We propose a modified 2-step floating catchment area method that incorporates the donut shape, accounts for both demand and supply, and examines the diversity of food options. Using Seattle as a case study, our results show that delivery services increase restaurant and fast-food accessibility in areas where there is already good accessibility (e.g., downtown Seattle for restaurants and South Seattle for fast-food). Given South Seattle is where low-income and low-access households concentrate, the increase in accessibility to fast-food may not be desired. Interestingly, with delivery services, more low-income or low-access households (those who live far from grocery stores) have better accessibility to fresh produce from grocery stores compared to the rest of the population. And the newly created Supplemental Nutrition Assistance Program (SNAP) online program appears to miss low-access households. These findings have important implications for policymakers and stakeholders seeking to improve food accessibility in urban areas through delivery services. 

As transportation systems grow in complexity, analysts need sophisticated tools to understand travelers’ decision-making and effectively quantify the benefits of the proposed strategies. The transportation community has developed integrated demand–supply models to capture the emerging interactive nature of transportation systems, serve diverse planning needs, and encompass broader solution possibilities. Recently, utilizing advances in Machine Learning (ML) techniques, researchers have also recognized the need for different computational models capable of fusing/analyzing different data sources. Inspired by this momentum, this study proposes a new modeling framework to analytically bridge travel demand components and network assignment models with machine learning algorithms. Specifically, to establish a consistent representation of such aspects between separate system models, we introduce several important mathematical programming reformulation techniques—variable splitting and augmented Lagrangian relaxation—to construct a computationally tractable nonlinear unconstrained optimization program. Furthermore, to find equilibrium states, we apply automatic differentiation (AD) to compute the gradients of decision variables in a layered structure with the proposed model represented based on computational graphs (CGs) and solve the proposed formulation through the alternating direction method of multipliers (ADMM) as a dual decomposition method. Thus, this reformulated model offers a theoretically consistent framework to express the gap between the demand and supply components and lays the computational foundation for utilizing a new generation of numerically reliable optimization solvers. Using a small example network and the Chicago sketch transportation network, we examined the convergency/consistency measures of this new differentiable programming-based optimization structure and demonstrated the computational efficiency of the proposed integrated transportation demand and supply models. 

A sustainable transportation future is one in which people eschew personal car ownership in favor of using autonomous vehicle (AV)-based ridehailing services in a shared mode. However, the traveling public has historically shown a disinclination toward sharing rides and carpooling with strangers. In a future of AV-based ridehailing services, it will be necessary for people to embrace both AVs as well as true ridesharing to fully realize the benefits of automated and shared mobility technologies. This study investigated the factors influencing willingness to use AV-based ridehailing services in the future in a shared mode (i.e., with strangers). This was done through the estimation of a behavioral model system on a comprehensive survey data set that included rich information about attitudes, perceptions, and preferences pertaining to the adoption of AVs and shared mobility modes. The model results showed that current ridehailing experiences strongly influenced the likelihood of being willing to ride AV-based services in a shared mode. Campaigns that provide opportunities for individuals to experience such services firsthand would potentially go a long way to enabling a shared mobility future at scale. In addition, several attitudinal variables were found to strongly influence the adoption of future mobility services; these findings provide insights on the likely early adopters of shared autonomous mobility services and the types of educational awareness campaigns that may effect change in the prospects of such services. 

With work arrangements experiencing dramatic changes over the past three years due to the COVID-19 pandemic, and the possibility that altered work arrangements may persist well into the future, the implications of teleworking on activity-travel behavior are potentially profound. This paper aims to substantially add to the body of knowledge about the present and future of telework in the wake of the pandemic through a rigorous analysis of telework arrangements between two distinct time periods. The paper focuses on three key aspects of telework, including whether to telework or not, frequency of telework, and location of telework. Behavioral data for this study is derived from a workplace location choice survey conducted across Texas in February-March 2022, which included a recall component to obtain workplace location choice information in the pre-pandemic period. The evolution of telework arrangements between the pre-and after-pandemic periods is explored through a joint model system estimated using a joint multivariate methodology. Results show that, After COVID, the population of workers is generally inclined toward a hybrid work arrangement, with an overall tendency to engage in a higher frequency of teleworking than Before COVID. Finally, teleworkers have a higher propensity to work only from home as opposed to working only from a third workplace or from a combination of home and a third workplace. Overall, our results indicate that telework arrangements may remain at an elevated level into the future, with home serving as the dominant telework location. These findings suggest that transportation demand forecasting models need to be updated to reflect higher levels of teleworking, as well as the heterogeneity across individuals in teleworking adoption, frequency, and location. 

This paper presents an examination of the interrelationship between household vehicle ownership and ridehailing use frequency. Both variables constitute important mobility choices with significant implications for the future of transport. Although it is generally known that these two behavioral phenomena are inversely related to one another, the direction of causality is rather ambiguous. Do vehicle ownership levels affect ridehailing use frequency, or does the adoption and use of ridehailing services affect vehicle ownership? If ridehailing services affect vehicle ownership, then it is plausible that a future of mobility-as-a-service would be characterized by lower levels of vehicle ownership. To explore the degree to which these causal relationships are prevalent in the population, a joint latent segmentation model system was formulated and estimated on a survey data set collected in four automobile-oriented metropolitan areas of the United States. The latent segmentation model system recognized that the causal structures driving the mobility choices of individuals were not directly observable. Model estimation results showed that 58% of the survey sample followed the causal structure in which ridehailing use frequency affected vehicle ownership. This finding suggests that there is considerable structural heterogeneity in the population with respect to causal structures and that ridehailing use does indeed hold considerable promise to effect changes in private vehicle ownership in the future. 

This research aims to investigate the well-being implications of changes in activity-travel and time-use patterns brought about by the COVID-19 pandemic. The study uses American Time Use Survey (ATUS) data from 2019 and 2020 to assess changes in activity-travel and time-use patterns. It applies two methods—a well-being scoring method and a time-poverty analysis method—to evaluate the impacts of these changes on society. The results show that individuals experienced diminished well-being during the pandemic even when their time-poverty statistics showed an improvement; this is because the pandemic did not allow individuals to pursue activities in a way that would enhance well-being. In general, well-being is positively associated with the pursuit of discretionary activities in the company of others in favored out-of-home locations. This explains why people have rapidly embraced traveling again in a post-pandemic era. At the same time, people desire more discretionary time (less time poverty); because the elimination of the commute contributes to this, workers are reluctant to return fully to the workplace. Planning processes need to account for a new normal in which activity-travel patterns will be increasingly shaped by the human desire to accumulate positive life experiences.