Passenger Volume Prediction by a Combined Input-Output and Distributed Lag Model and Data Analytics of Industrial Investment


== Abstract ==

In order to sketch the transport infrastructure construction in an economy or a region, the government has to predict the passenger volume, under the local policy of industrial investment. In this paper, we propose a combined input-output and distributed lag prediction model of passenger volume in a province in P. R. China, under a certain policy of industrial investment called Silk Road Economic Belt. Specifically, the relationships between the passenger volume, GDP (gross domestic product), gross output, and transportation consumption are analyzed, and then the industrial development speed analysis and classification are used to calculate the average development speeds and the GDP contributions of 42 industries. Combining the input-output table, the provincial transportation consumption under the Silk Road Economic Belt policy is predicted, and the passenger volumes of the cities and the province in the future are predicted by the distributed lag models. Considering the uncertainty of the investment, the elastic ranges of the cities and the province’s passenger volumes are determined. The results show that the correlation between the passenger volume and transportation consumption is the highest, and it is equal to 0.975. In 2020, the passenger volume in Shaanxi is 1,641,305 thousands, and the error between the predicted value and the value obtained by summing the cities’ passenger volumes is smaller than 0.002%.

Document type: Article

== Full document ==
<pdf>Media:Draft_Content_885777510-beopen813-3339-document.pdf</pdf>


== Original document ==

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

* [http://dx.doi.org/10.1155/2020/6675042 http://dx.doi.org/10.1155/2020/6675042] under the license https://creativecommons.org/licenses/by/4.0/

* [http://downloads.hindawi.com/journals/jat/2020/6675042.pdf http://downloads.hindawi.com/journals/jat/2020/6675042.pdf],
: [http://downloads.hindawi.com/journals/jat/2020/6675042.xml http://downloads.hindawi.com/journals/jat/2020/6675042.xml],
: [http://dx.doi.org/10.1155/2020/6675042 http://dx.doi.org/10.1155/2020/6675042]

 under the license https://creativecommons.org/licenses/by/4.0/