Predicting commuter flows in spatial networks using a radiation model based on temporal ranges
Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of th...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Nature Publishing Group
2016
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| Online Access: | http://hdl.handle.net/1721.1/101415 https://orcid.org/0000-0002-8482-0318 |
| _version_ | 1826216753074536448 |
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| author | Ren, Yihui Ercsey-Ravasz, Maria Wang, Pu Gonzalez, Marta C. Toroczkai, Zoltan |
| author2 | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Ren, Yihui Ercsey-Ravasz, Maria Wang, Pu Gonzalez, Marta C. Toroczkai, Zoltan |
| author_sort | Ren, Yihui |
| collection | MIT |
| description | Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. |
| first_indexed | 2024-09-23T16:52:33Z |
| format | Article |
| id | mit-1721.1/101415 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:52:33Z |
| publishDate | 2016 |
| publisher | Nature Publishing Group |
| record_format | dspace |
| spelling | mit-1721.1/1014152022-09-29T22:08:03Z Predicting commuter flows in spatial networks using a radiation model based on temporal ranges Ren, Yihui Ercsey-Ravasz, Maria Wang, Pu Gonzalez, Marta C. Toroczkai, Zoltan Massachusetts Institute of Technology. Department of Civil and Environmental Engineering Gonzalez, Marta C. Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. 2016-03-02T22:48:07Z 2016-03-02T22:48:07Z 2014-11 2013-08 Article http://purl.org/eprint/type/JournalArticle 2041-1723 http://hdl.handle.net/1721.1/101415 Ren, Yihui, Maria Ercsey-Ravasz, Pu Wang, Marta C. Gonzalez, and Zoltan Toroczkai. “Predicting Commuter Flows in Spatial Networks Using a Radiation Model Based on Temporal Ranges.” Nat Comms 5 (November 6, 2014): 5347. https://orcid.org/0000-0002-8482-0318 en_US http://dx.doi.org/10.1038/ncomms6347 Nature Communications Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Nature Publishing Group arXiv |
| spellingShingle | Ren, Yihui Ercsey-Ravasz, Maria Wang, Pu Gonzalez, Marta C. Toroczkai, Zoltan Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title | Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title_full | Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title_fullStr | Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title_full_unstemmed | Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title_short | Predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| title_sort | predicting commuter flows in spatial networks using a radiation model based on temporal ranges |
| url | http://hdl.handle.net/1721.1/101415 https://orcid.org/0000-0002-8482-0318 |
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