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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.
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This content will become publicly available on January 6, 2026
Prosociality in Microtransit
We study (public) microtransit, a type of transportation service wherein a municipality offers point-to-point rides to residents, for a fixed, nominal fare. Microtransit exemplifies practical resource allocation problems that are often over-constrained in that not all ride requests (pickup and dropoff locations at specified times) can be satisfied or satisfied only by violating soft goals such as sustainability, and where economic signals (e.g., surge pricing) are not applicable—they would lead to unethical outcomes by effectively coercing poor people.We posit that instead of taking rider preferences as fixed, shaping them prosocially will lead to improved societal outcomes. Prosociality refers to an attitude or behavior that is intended to benefit others. This paper demonstrates a computational approach to prosociality in the context of a (public) microtransit service for disadvantaged riders. Prosociality appears as a willingness to adjust one’s pickup and dropoff times and locations to accommodate the schedules of others and to enable sharing rides (which increases the number of riders served with the same resources).This paper describes an interdisciplinary study of prosociality in microtransit between a transportation researcher, psychologists, a social scientist, and AI researchers. Our contributions are these: (1) empirical support for the viability of prosociality in microtransit (and constraints on it) through interviews with drivers and focus groups of riders; (2) a prototype mobile app demonstrating how our prosocial intervention can be combined with the transportation backend; (3) a reinforcement learning approach to model a rider and determine the best interventions to persuade that rider toward prosociality; and (4) a cognitive model of rider personas to enable evaluation of alternative interventions.
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- PAR ID:
- 10638026
- Publisher / Repository:
- Journal of Artificial Intelligence Research
- Date Published:
- Journal Name:
- Journal of Artificial Intelligence Research
- Volume:
- 82
- ISSN:
- 1076-9757
- Page Range / eLocation ID:
- 77 to 110
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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