Tiramisu: Live Bus Occupancy and Data Feeds

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Total Dollars:$50,000
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Project Status:Complete
Subject Categories:Public Transportation, Data and information technology
Abstract:The primary source of information for rider safety with respect to dynamic events such as cancelled buses and snow detours is the transit system website and, often, the agency's Twitter feed. However, users require a lot of effort to understand these messages, since at any point in time, a transit system has dozens of problems. A user must read through all the messages to understand if any are relevant. Finally, it is difficult for riders to forecast how this will impact the fullness of alternative routes.
Our project goal is to create a natural language processing system that automatically extracts relevant information from each message. The extracted information can be used in multiple ways to improve the interactive experience of the user and keep them better informed. For example, messages could be surfaced to the user in an interface if it addresses a stop or route that the user typically uses, or for which the user is currently requesting data. This, combined with vehicle fullness data from the Tiramisu system, will allow riders to identify alternate transit trip options that are not too full to board.
We will start with agency Twitter feeds as an input. While not all transit agencies use Twitter, many cities rely on this content channel to transmit real-time safety, detour, and status updates. Processing this information source is not always and easy problem. There is often an inexact match between the official entity names and tweet content. We will also work on the difficult problem of event extraction, that is, attaching time duration to the events extracted.
Processing and distributing information about abnormal transit operations has a direct impact on rider safety and efficient travel. Unnecessary exposure to inclement weather while waiting for a bus that will never arrive can have a significant negative health impact.
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