In this project we propose T-REX, a novel crowd-sourced decision-support system for improving public transit riding experience. Realized as a mobile app, T-REX aims to integrate past and current transit data (e.g., locations of the buses/trains, road closures and blocks), both publicly available and rider-generated ones, with rider intent information, to predict travel time, comfort, and cost of various transit options. Furthermore, our proposed system builds behavior models, decision histories, and assesses the impact of the rider’s decisions on public resources and on other riders over time, encouraging collaboration, hence overall better transit experience.
Public transportation is a common good, and riders have collaborative and competitive relationships when consuming it. Selfish behavior negatively affects the community: for instance, if a rider takes public transportation only based on her own schedule and preferences, this might lead to over-crowded buses/trains/subways during rush hour. In contrast, a solution that allows every rider to plan trips by considering other riders’ behavior and preferences, along with context-relevant information, would facilitate the distribution of public transit resources, resulting in an improved experience for the entire community.
To augment customer satisfaction, a smart solution should be able to unpack both public and private dimensions of rider experience and promote collaborative behavior by balancing them. Public dimensions include events that affect public transportation's arrival and travel times, such as emergencies, public gatherings like concerts, marathons, tailgate parties, protests, etc. Private dimensions include reasons for travel (e.g., daily commute, leisure), distance between origin and destination, willingness to wait with or without incentives (e.g., less crowded buses outside rush hour, discounts), alternative route choices, and personal preferences.
In this regard, a decision support system that aggregates public and private dimensions of rider experience at scale, processing them using Artificial Intelligence can be used to augment riders’ context awareness, thus customizing the transit solution and, ultimately, mitigating riders’ frustration. T-REX aims to be such a decision-support system for public riders in the Pittsburgh region. The system, implemented as a mobile app, will support urban and suburban routes of Port Authority bus, light rail, and subway systems. T-REX will improve customer experience by analyzing local riders’ competition on public transit resources and encouraging collaboration. T-REX will allow local riders to share information collaboratively to decide better timing and routing of taking public transit, in both self and public interests. Our system will also mediate the competing relationships when consuming public transportation resources by influencing individuals’ decision-making related to transit timing and routes: we will model the riders’ behavior as evolutionary game with additional information of past interactions of each other and present the equilibrium (best transit option in response to other riders’ decisions) to the riders. More importantly, via incentive design and novel user interfaces, we will also show riders alternative transit options that are not “the best” options but much better in terms of both self and public interest.
The mobile app will also show how transit alternatives affects other riders who have decided not to compete with the user for the transit resource, and how likely other riders will help the user in the future. By providing such options through a user-friendly interface, riders can easily interpret the impact of their decisions: in this regard, they are encouraged to collaborate and push the equilibrium to benefit the public transportation system as a whole. T-REX aims to integrate past and current transit data (e.g., locations of the buses/trains, road closures and road conditions), both publicly available and rider-generated ones, with the current intent of other T-REX users to predict the travel time, comfort, and cost of various transit options.
Such options include both timing and routes of transit. Furthermore, T-REX builds behavior models, decision histories, and assesses impact of the rider’s decisions on public resource and other riders’ experience over time. When presenting transit options to the riders, the impact on riders who have taken altruistic alternatives, as well as the displaying potential future benefit from other riders for each transit option.
Past approaches on decision-making do not consider the “potential riders”, i.e., riders that are just about to decide whether/how they are going to use public transport, but only the riders in the system currently and status of the transit system. This implies that state of the art tools tend to present the same information and suggest the same decisions to all potential riders, which pushes them to make similar decisions ending up with more competition on public resources. T-REX will take each potential rider’s interest and flexibility into account, and suggest different alternatives that fit each individual’s schedule to encourage riders making community-aware decisions.