Conference proceeding
Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, Vol.33(1), pp.5199-5206
AAAI Conference on Artificial Intelligence
07/17/2019
DOI: 10.1609/aaai.v33i01.33015199
Abstract
Urban dispersal events are processes where an unusually large number of people leave the same area in a short period. Early prediction of dispersal events is important in mitigating congestion and safety risks and making better dispatching decisions for taxi and ride-sharing fleets. Existing work mostly focuses on predicting taxi demand in the near future by learning patterns from historical data. However, they fail in case of abnormality because dispersal events with abnormally high demand are non-repetitive and violate common assumptions such as smoothness in demand change over time. Instead, in this paper we argue that dispersal events follow a complex pattern of trips and other related features in the past, which can be used to predict such events. Therefore, we formulate the dispersal event prediction problem as a survival analysis problem. We propose a two-stage framework (DILSA), where a deep learning model combined with survival analysis is developed to predict the probability of a dispersal event and its demand volume. We conduct extensive case studies and experiments on the NYC Yellow taxi dataset from 2014-2016. Results show that DILSA can predict events in the next 5 hours with F1-score of 0:7 and with average time error of 18 minutes. It is orders of magnitude better than the state-of-the-art deep learning approaches for taxi demand prediction.
Details
- Title: Subtitle
- Predicting Urban Dispersal Events: A Two-Stage Framework through Deep Survival Analysis on Mobility Data
- Creators
- Amin Vahedian - University of IowaXun Zhou - University of IowaLing Tong - University of IowaW. Nick Street - University of IowaYanhua Li - Worcester Polytechnic Institute
- Resource Type
- Conference proceeding
- Publication Details
- THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE, Vol.33(1), pp.5199-5206
- Publisher
- Assoc Advancement Artificial Intelligence
- Series
- AAAI Conference on Artificial Intelligence
- DOI
- 10.1609/aaai.v33i01.33015199
- ISSN
- 2159-5399
- eISSN
- 2374-3468
- Number of pages
- 8
- Grant note
- DiDiChuxing Research CNS-1657350; CMMI-1831140; IIS-1566386 / NSF; National Science Foundation (NSF) NVIDIA Corporation
- Language
- English
- Date published
- 07/17/2019
- Academic Unit
- Bus Admin College; Business Analytics; Computer Science; Nursing
- Record Identifier
- 9984380420202771
Metrics
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