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The slow rebound of public transit ridership since the pandemic and major upcoming budget shortfalls have created a perfect storm in which American cities and transit agencies must make difficult decisions regarding operations and service design. Among the many challenges, perceived rider safety has emerged as a key concern. However, implementing effective safety interventions is complicated by the mixed rider experiences with, and perceptions of, crime and law enforcement. Transit agencies can design more effective policy interventions if they understand what shapes riders’ reactions to different safety strategies, and how those strategies can promote rider satisfaction. Using a 2023 survey of 2292 transit riders in the Chicago region, we estimate a Bayesian Structural Equation Model to investigate the connections between rider experiences and demographics, receptiveness to safety measures, and overall satisfaction. We find that enforcement-related strategies are most strongly associated with higher overall rider satisfaction, but they also come with the notable downside of 10%–20% of riders feeling less safe. On the other hand, improvements to various facets of service quality are less strongly related to satisfaction, but they come with little to no downside in terms of negative rider perceptions. Rider experience also plays a role, with more severe crime and nuisance experience directly impacting satisfaction. In contrast, indirect knowledge of transit safety issues obtained from media and hearsay primarily affects riders’ support for safety interventions rather than their overall satisfaction. 

Building community resilience is vital due to climate change and more frequent extreme weather events, which often force people to choose between evacuating or sheltering in place. The prevalence of stay-at-home orders and quarantine practices emerging from the COVID-19 pandemic highlights the need to understand how households access resources when mobility is restricted. This research investigates peer-to-peer resource-exchanging behavior during a shelterin- place response to a flooding event amid the pandemic through an online stated response survey (n=600). Latent class analysis reveals six distinct segments based on respondents’ resource sharing and accepting behaviors. Several household and social context variables help explain these behavioral clusters. Younger individuals and individuals with lower household income are generally more reluctant to accept resources from neighbors, while larger households are more inclined to share essential items. Additionally, social resources, trust in neighbors, and preparedness level can significantly influence individuals’ resource-exchanging behaviors. The findings highlight gaps for governmental agencies and nonprofit organizations to help address, emphasizing the need to ensure sufficient allocation of resources, especially for private items such as backup power sources, communication devices, and shelter, which respondents are least willing to share. This research offers valuable insights for future disaster preparedness programs and resource allocation strategies, aiming to improve community resilience and minimize negative impacts during shelter-in-place responses. 

Unplanned disaster events can greatly disrupt access to essential resources, with calamitous outcomes for already vulnerable households. This is particularly challenging when concurrent extreme events affect both the ability of households to travel and the functioning of traditional transportation networks that supply resources. This paper examines the use of volunteer-based crowdsourced food delivery as a community resilience tactic to improve food accessibility during overlapping disruptions with lasting effects, such as the COVID-19 pandemic and climate disasters. The study uses large-scale spatio-temporal data (n = 28,512) on crowdsourced food deliveries in Houston, TX, spanning from 2020 through 2022, merged with data on community demographics and significant disruptive events occurring in the two-year timespan. Three research lenses are applied to understand the effectiveness of crowdsourced food delivery programs for food access recovery: 1) geographic analysis illustrates hot spots of demand and impacts of disasters on requests for food assistance within the study area; 2) linear spatio-temporal modeling identifies a distinction between shelter-in-place emergencies and evacuation emergencies regarding demand for food assistance; 3) structural equation modeling identifies socially vulnerable identity clusters that impact requests for food assistance. The findings from the study suggest that volunteerbased crowdsourced food delivery adds to the resilience of food insecure communities, supporting its effectiveness in serving its intended populations. The paper contributes to the literature by illustrating how resilience is a function of time and space, and that similarly, there is value in a dynamic representation of community vulnerability. The results point to a new approach to resource recovery following disaster events by shifting the burden of transportation from resource-seekers and traditional transportation systems to home delivery by a crowdsourced volunteer network. 

Public transit in the U.S. has an unsettled future. The onset of the COVID-19 pandemic saw a dramatic decline in transit ridership, with agency operations, and user perceptions of safety changing significantly. However, one new factor beyond the control of agencies is playing an outsized role in transit ridership: the shifting employment patterns in the hybrid work era. Indeed, a lasting and widespread adoption of telework has emerged as a key determinant of individual transit behaviors. This study investigates the impact of teleworking on public transit ridership changes across the different transit services in the Chicago area during the pandemic, employing a random forest machine learning approach applied to large-scale survey data (n = 5637). The use of ensemble machine learning enables a data-driven investigation that is tailored for each of the three main transit service operators in Chicago (Chicago Transit Authority, Metra, and Pace). The analysis reveals that the number of teleworking days per week is a highly significant predictor of lapsed ridership. As a result, commuter-centric transit modes—such as Metra—saw the greatest declines in ridership during the pandemic. The study's findings highlight the need for transit agencies to adapt to the enduring trend of teleworking, considering its implications for future ridership and transportation equity. Policy recommendations include promoting non-commute transit use and addressing the needs of demographic groups less likely to telework. The study contributes to the understanding of how telework trends influence public transit usage and offers insights for transit agencies navigating the post-pandemic world. 

Abstract The logistics and delivery industry is undergoing a technology-driven transformation, with robotics, drones, and autonomous vehicles expected to play a key role in meeting the growing challenges of last-mile delivery. To understand the public acceptability of automated parcel delivery options, this U.S. study explores customer preferences for four innovations: autonomous vehicles, aerial drones, sidewalk robots, and bipedal robots. We use an Integrated Nested Choice and Correlated Latent Variable (INCLV) model to reveal substitution effects among automated delivery modes in a sample of U.S. respondents. The study finds that acceptance of automated delivery modes is strongly tied to shipment price and time, underscoring the importance of careful planning and incentives to maximize the trialability of innovative logistics options. Older individuals and those with concerns about package handling exhibit a lower preference for automated modes, while individuals with higher education and technology affinity exhibit greater acceptance. These findings provide valuable insights for logistics companies and retailers looking to introduce automation technologies in their last-mile delivery operations, emphasizing the need to tailor marketing and communication strategies to meet customer preferences. Additionally, providing information about appropriate package handling by automated technologies may alleviate concerns and increase the acceptance of these modes among all customer groups. 

Abstract Customer preference modelling has been widely used to aid engineering design decisions on the selection and configuration of design attributes. Recently, network analysis approaches, such as the exponential random graph model (ERGM), have been increasingly used in this field. While the ERGM-based approach has the new capability of modelling the effects of interactions and interdependencies (e.g., social relationships among customers) on customers’ decisions via network structures (e.g., using triangles to model peer influence), existing research can only model customers’ consideration decisions, and it cannot predict individual customer’s choices, as what the traditional utility-based discrete choice models (DCMs) do. However, the ability to make choice predictions is essential to predicting market demand, which forms the basis of decision-based design (DBD). This paper fills this gap by developing a novel ERGM-based approach for choice prediction. This is the first time that a network-based model can explicitly compute the probability of an alternative being chosen from a choice set. Using a large-scale customer-revealed choice database, this research studies the customer preferences estimated from the ERGM-based choice models with and without network structures and evaluates their predictive performance of market demand, benchmarking the multinomial logit (MNL) model, a traditional DCM. The results show that the proposed ERGM-based choice modelling achieves higher accuracy in predicting both individual choice behaviours and market share ranking than the MNL model, which is mathematically equivalent to ERGM when no network structures are included. The insights obtained from this study further extend the DBD framework by allowing explicit modelling of interactions among entities (i.e., customers and products) using network representations.