Computational models of consumer confidence from large-scale online attention data: crowd-sourcing econometrics

Economies are instances of complex socio-technical systems that are shaped by the interactions of large numbers of individuals. The individual behavior and decision-making of consumer agents is determined by complex psychological dynamics that include their own assessment of present and future economic conditions as well as those of others, potentially leading to feedback loops that affect the macroscopic state of the economic system. We propose that the large-scale interactions of a nation's citizens with its online resources can reveal the complex dynamics of their collective psychology, including their assessment of future system states. Here we introduce a behavioral index of Chinese Consumer Confidence (C3I) that computationally relates large-scale online search behavior recorded by Google Trends data to the macroscopic variable of consumer confidence. Our results indicate that such computational indices may reveal the components and complex dynamics of consumer psychology as a collective socio-economic phenomenon, potentially leading to improved and more refined economic forecasting.Comment: 21 pages, 6 figures, 13 table

Evaluating the Comprehensive Development Level and Coordinated Relationships of Urban Multimodal Transportation: A Case Study of China’s Major Cities

Urban multimodal transportation effectively meets the diversified travel demand of residents. However, it also generates extensive development problems such as traffic congestion, exhaust emissions and low operational efficiency. Therefore, there is an urgent need in urban sustainable development to achieve the coordinated and stable development of various modes of transportation. In this study, we took 36 major cities in China as the research object; measured the comprehensive development level of urban multimodal transportation; used the coupling coordination degree model (CCDM) to research the coordinated development relationship among buses, rail transit, and taxis; and clarified the shortcomings of the coordinated development of multimodal transportation. The results show that the comprehensive development of urban multimodal transportation in China has shown a significant upward trend from 2016 to 2020, with an average annual growth rate of about 7.36%. There are significant differences in the development levels of multimodal transportation in different cities. In addition, the relationship among buses, rail transit, and taxis in the major cities in China presents a state of uncoordinated development. Therefore, the relevant departments of cities should optimize the allocation of transportation resources, in terms of infrastructure construction and operation, according to these development levels and coordination of multimodal transportation

Evaluating the Comprehensive Development Level and Coordinated Relationships of Urban Multimodal Transportation: A Case Study of China’s Major Cities

Urban multimodal transportation effectively meets the diversified travel demand of residents. However, it also generates extensive development problems such as traffic congestion, exhaust emissions and low operational efficiency. Therefore, there is an urgent need in urban sustainable development to achieve the coordinated and stable development of various modes of transportation. In this study, we took 36 major cities in China as the research object; measured the comprehensive development level of urban multimodal transportation; used the coupling coordination degree model (CCDM) to research the coordinated development relationship among buses, rail transit, and taxis; and clarified the shortcomings of the coordinated development of multimodal transportation. The results show that the comprehensive development of urban multimodal transportation in China has shown a significant upward trend from 2016 to 2020, with an average annual growth rate of about 7.36%. There are significant differences in the development levels of multimodal transportation in different cities. In addition, the relationship among buses, rail transit, and taxis in the major cities in China presents a state of uncoordinated development. Therefore, the relevant departments of cities should optimize the allocation of transportation resources, in terms of infrastructure construction and operation, according to these development levels and coordination of multimodal transportation

Matrix <i>C</i>: Parameters of topics’ future effect on C3I.

<p>Matrix <i>C</i>: Parameters of topics’ future effect on C3I.</p

Vector Auto-regression Results.

<p>(1) L1 indicates a 1 month time series lag whereas L2 indicates a 2 month lag. (2) L.CCI and L2.<i>C</i>₃ are significant with a low p-value in the VAR model, so they are added in the model as independent variables.</p><p>Vector Auto-regression Results.</p

The contribution of Google Trends Data to our C3I model plotted over time reveals a downward trend possibly indicating that the public are losing economic confidence as judged from search engine queries.

<p>The contribution of Google Trends Data to our C3I model plotted over time reveals a downward trend possibly indicating that the public are losing economic confidence as judged from search engine queries.</p

Results of Structural Change Tests.

<p>Structural Change Tests indicate that we can reject the null-hypothesis of no structural change in CCI time series. P-values marked <b>**</b> mean rejecting <i>H</i>₀ at the 1% level.</p><p>Results of Structural Change Tests.</p

Topic Effect on C3I according to matrix <i>A</i>, <i>B</i> and <i>C</i>.

<p>Topic Effect on C3I according to matrix <i>A</i>, <i>B</i> and <i>C</i>.</p