Scipediacontent: Scipediacontent moved page Draft Content 286445933 to Chen et al 2020f

2021-02-15T11:29:25Z

Scipediacontent moved page Draft Content 286445933 to Chen et al 2020f

Scipediacontent: Created page with " == Abstract == Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on histori..."

2021-02-15T11:29:21Z

Created page with " == Abstract == Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on histori..."

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== Abstract ==

Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on historical taxi trajectory data. In this study, we detected hotspots and proposed three methods to predict the taxi demand in hotspots. Next, we compared the predictive effect of the random forest model (RFM), ridge regression model (RRM), and combination forecasting model (CFM). Thereafter, we considered environmental and meteorological factors to predict the taxi demand in hotspots. Finally, the importance of indicators was analyzed, and the essential elements were the time, temperature, and weather factors. The results indicate that the prediction effect of CFM is better than those of RFM and RRM. The experiment obtains the relationship between taxi demand and environment and is helpful for taxi dispatching by considering additional factors, such as temperature and weather.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_286445933-beopen983-3487-document.pdf</pdf>

== Original document ==

The different versions of the original document can be found in:

* [http://dx.doi.org/10.1155/2020/1302586 http://dx.doi.org/10.1155/2020/1302586] under the license http://creativecommons.org/licenses/by/4.0/

* [https://doi.org/10.1155/2020/1302586 https://doi.org/10.1155/2020/1302586]

* [http://downloads.hindawi.com/journals/jat/2020/1302586.pdf http://downloads.hindawi.com/journals/jat/2020/1302586.pdf],
: [http://downloads.hindawi.com/journals/jat/2020/1302586.xml http://downloads.hindawi.com/journals/jat/2020/1302586.xml],
: [http://dx.doi.org/10.1155/2020/1302586 http://dx.doi.org/10.1155/2020/1302586] under the license cc-by

* [http://dx.doi.org/10.1155/2020/1302586 http://dx.doi.org/10.1155/2020/1302586],
: [https://doaj.org/toc/0197-6729 https://doaj.org/toc/0197-6729],
: [https://doaj.org/toc/2042-3195 https://doaj.org/toc/2042-3195] under the license http://creativecommons.org/licenses/by/4.0/

* [https://www.hindawi.com/journals/jat/2020/1302586 https://www.hindawi.com/journals/jat/2020/1302586],
: [http://downloads.hindawi.com/journals/jat/2020/1302586.pdf http://downloads.hindawi.com/journals/jat/2020/1302586.pdf],
: [https://trid.trb.org/view/1723769 https://trid.trb.org/view/1723769],
: [https://academic.microsoft.com/#/detail/3044440748 https://academic.microsoft.com/#/detail/3044440748]

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Chen et al 2020f - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 286445933 to Chen et al 2020f

Scipediacontent: Created page with " == Abstract == Accurate taxi demand prediction can solve the congestion problem caused by the supply-demand imbalance. However, most taxi demand studies are based on histori..."