Neural bus networks
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/119711 |
| _version_ | 1826214706390499328 |
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| author | Sloan, Cooper Stokes |
| author2 | Alan Edelman. |
| author_facet | Alan Edelman. Sloan, Cooper Stokes |
| author_sort | Sloan, Cooper Stokes |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. |
| first_indexed | 2024-09-23T16:09:45Z |
| format | Thesis |
| id | mit-1721.1/119711 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T16:09:45Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1197112019-04-12T19:28:48Z Neural bus networks Sloan, Cooper Stokes Alan Edelman. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 65-68). Bus schedules are unreliable, leaving passengers waiting and increasing commute times. This problem can be solved by modeling the traffic network, and delivering predicted arrival times to passengers. Research attempts to model traffic networks use historical, statistical and learning based models, with learning based models achieving the best results. This research compares several neural network architectures trained on historical data from Boston buses. Three models are trained: multilayer perceptron, convolutional neural network and recurrent neural network. Recurrent neural networks show the best performance when compared to feed forward models. This indicates that neural time series models are effective at modeling bus networks. The large amount of data available for training bus network models and the effectiveness of large neural networks at modeling this data show that great progress can be made in improving commutes for passengers. by Cooper Stokes Sloan. M. Eng. 2018-12-18T19:46:48Z 2018-12-18T19:46:48Z 2018 2018 Thesis http://hdl.handle.net/1721.1/119711 1078621654 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 68 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Sloan, Cooper Stokes Neural bus networks |
| title | Neural bus networks |
| title_full | Neural bus networks |
| title_fullStr | Neural bus networks |
| title_full_unstemmed | Neural bus networks |
| title_short | Neural bus networks |
| title_sort | neural bus networks |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/119711 |
| work_keys_str_mv | AT sloancooperstokes neuralbusnetworks |