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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. 

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.