@conference {8421344,
	title = {Short-Term Transit Decision Support System Using Multi-task Deep Neural Networks},
	booktitle = {2018 IEEE International Conference on Smart Computing (SMARTCOMP)},
	year = {2018},
	month = {07/2018},
	pages = {155-162},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Taormina, Italy},
	abstract = {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. In this paper, we propose a generic tool-chain that takes standard General Transit Feed Specification (GTFS) transit feeds and contextual information (recurring delay patterns before and after big events in the city and the contextual information such as scheduled events and forecasted weather conditions) as inputs and provides service alerts as output. Particularly, we utilize shared route segment networks and multi-task deep neural networks to solve the data sparsity and generalization issues. Experimental evaluation shows that the proposed toolchain is effective at predicting severe delay with a relatively high recall of 76\% and F1 score of 55\%.},
	keywords = {arrival delay prediction, contextual prediction mechanism, Data models, data sparsity, decision support systems, deep learning, delay prediction, Delays, Games, general transit feed specification, GTFS, learning (artificial intelligence), multi-task learning, multitask deep neural networks, neural nets, neural networks, Predictive models, public transport, public transportation, real-time systems, route segment networks, short-term transit decision support system, traffic information systems, Urban areas},
	isbn = {978-1-5386-4705-9},
	doi = {10.1109/SMARTCOMP.2018.00086},
	attachments = {https://archive.isis.vanderbilt.edu/sites/default/files/short_term_transit_dnn.pdf},
	author = {F. Sun and A. Dubey and C. Samal and H. Baroud and C. Kulkarni}
}
