|Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks|
Unpredictability is one of the top reasons that prevent people from using public transportation. To improve the on-time performance of transit systems, prior work focuses on updating schedule periodically in the long-term and providing arrival delay prediction in real-time. But when no real-time transit and traffic feed is available (e.g., one day ahead), there is a lack of effective contextual prediction mechanism that can give alerts of possible delay to commuters.
|Year of Publication||
2018 IEEE International Conference on Smart Computing (SMARTCOMP)
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