The DOI matches its Crossref record; 32 of the 32 references it cites were found in a registry or an index.
Predicting Taxi–Passenger Demand Using Streaming Data
Open accessAbstract
Informed driving is increasingly becoming a key feature for increasing the sustainability of taxi companies. The sensors that are installed in each vehicle are providing new opportunities for automatically discovering knowledge, which, in return, delivers information for real-time decision making. Intelligent transportation systems for taxi dispatching and for finding time-saving routes are already exploring these sensing data. This paper introduces a novel methodology for predicting the spatial distribution of taxi-passengers for a short-term time horizon using streaming data. First, the information was aggregated into a histogram time series. Then, three time-series forecasting techniques were combined to originate a prediction. Experimental tests were conducted using the online data that are transmitted by 441 vehicles of a fleet running in the city of Porto, Portugal. The results demonstrated that the proposed framework can provide effective insight into the spatiotemporal distribution of taxi-passenger demand for a 30-min horizon.
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A survey on deep learning and its applications
Dong, S., Wang, P., & Abbas, K. (2021) · Computer Science Review · doi:10.1016/j.cosrev.2021.100379 · Cited by 1,544
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Real-time bus arrival prediction and bus bunching prevention using a multi-agent LSTM approach
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Metadata
- Authors
- Moreira-Matias, L., Gama, J., Ferreira, M., Mendes-Moreira, J., & Damas, L.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2013-09
- Volume
- 14
- Issue
- 3
- Pages
- 1393-1402
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W1988580225
- Cited by
- 721
- References
- 32
- FWCI
- 24.83
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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