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This research examines the influence of backers' social networks on their backing behavior using data from a large social networking site and a reward-based crowdfunding platform. We distinguish the roles of nodes and ties in a backer's social network and assess the combined and differential effects of these two types of social relationship on the backer's pledge decisions. As backers have different motives for engaging in different crowdfunding campaigns, which range from commercially oriented technological innovation to community-based social development, we further examine how these effects differ between technology-oriented campaigns and social-oriented campaigns. We find that node-level factors (e.g., centrality) have a greater influence on technology-oriented campaigns than on social-oriented campaigns, while tie-level factors (e.g., embeddedness) have a stronger impact on social-oriented campaigns. Considering the two forms of embeddedness in tandem, we find that the effects of relational embeddedness on backers' pledge decisions are not only moderated by structural embeddedness but also contingent on campaign type. These results offer important theoretical insights into the drivers of contribution, which should be considered by crowdfunding operators and campaign proponents seeking to stimulate contribution., Author(s): Yuho Chung [sup.1] , Yiwei Li [sup.1] , Jianmin Jia [sup.2] [sup.3] Author Affiliations: (1) grid.411382.d, 0000 0004 1770 0716, Faculty of Business, Lingnan University, , Tuen Mun, Hong [...]

Sudden, large-scale and diffuse human migration can amplify localized outbreaks of disease into widespread epidemics.sup.1-4. Rapid and accurate tracking of aggregate population flows may therefore be epidemiologically informative. Here we use 11,478,484 counts of mobile phone data from individuals leaving or transiting through the prefecture of Wuhan between 1 January and 24 January 2020 as they moved to 296 prefectures throughout mainland China. First, we document the efficacy of quarantine in ceasing movement. Second, we show that the distribution of population outflow from Wuhan accurately predicts the relative frequency and geographical distribution of infections with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) until 19 February 2020, across mainland China. Third, we develop a spatio-temporal 'risk source' model that leverages population flow data (which operationalize the risk that emanates from epidemic epicentres) not only to forecast the distribution of confirmed cases, but also to identify regions that have a high risk of transmission at an early stage. Fourth, we use this risk source model to statistically derive the geographical spread of COVID-19 and the growth pattern based on the population outflow from Wuhan; the model yields a benchmark trend and an index for assessing the risk of community transmission of COVID-19 over time for different locations. This approach can be used by policy-makers in any nation with available data to make rapid and accurate risk assessments and to plan the allocation of limited resources ahead of ongoing outbreaks. Modelling of population flows in China enables the forecasting of the distribution of confirmed cases of COVID-19 and the identification of areas at high risk of SARS-CoV-2 transmission at an early stage., Author(s): Jayson S. Jia [sup.1] , Xin Lu [sup.2] [sup.3] , Yun Yuan [sup.4] , Ge Xu [sup.5] , Jianmin Jia [sup.6] [sup.7] , Nicholas A. Christakis [sup.8] Author Affiliations: [...]

Understanding traveler mobility in cities is significant for urban planning and traffic management. However, most traditional studies have focused on travel mobility in a single traffic mode. Only limited studies have focused on the travel mobility associated with multimodal transportation. Subways are considered a green travel mode with large capacity, while taxis are an energy-consuming travel mode that provides a personalized service. Exploring the relationship between subway mobility and taxi mobility is conducive to building a sustainable multimodal transportation system, such as one with mobility as a service (MaaS). In this study, we propose a framework for comparatively analyzing the travel mobilities associated with subways and taxis. Firstly, we divided taxi trips into three groups: competitive, cooperative, and complementary. Voronoi diagrams based on subway stations were introduced to divide regions. An entropy index was adopted to measure the mix of taxi trips. Secondly, subway and taxi trip networks were constructed based on the divided regions. The framework was tested based on the automatic fare collection (AFC) data and global positioning system (GPS) data of a subway in Beijing, China. The results showed that the proportions of taxi competition, taxi cooperation, and taxi complements were 9.1%, 35.6%, and 55.3%, respectively. The entropy was large in the central city and small in the suburbs. Moreover, it was found that the subway trip network was connected more closely than the taxi network. However, the unbalanced condition of taxis is more serious than that of the subway. [ABSTRACT FROM AUTHOR]

Dockless bike-sharing (DBS) is a green and flexible travel mode, which has been considered as an effective way to address the first-and-last mile problem. A two-level process is developed to identify the integrated DBS–metro trips. Then, DBS trip data, metro passenger data, socioeconomic data, and built environment data in Shanghai are used to analyze the spatiotemporal characteristics of integrated trips and the correlations between the integrated trips and the explanatory variables. Next, multicollinearity tests and autocorrelation tests are conducted to select the best explanatory variables. Finally, a geographically and temporally weighted regression (GTWR) model is adopted to examine the determinants of integrated trips over space and time. The results show that the integrated trips account for 16.8% of total DBS trips and that departure-transfer trips are greater than arrival-transfer trips. Moreover, the integrated trips are concentrated in the central area of the city. In terms of impact factors, it is found that GDP, government count, and restaurant count are negatively correlated with the number of integrated trips, while house price, entropy of land use, transfer accessibility index, and metro passenger flow show positive relationships. In addition, the results show that the GTWR model outperforms the OLS model and the GWR model. [ABSTRACT FROM AUTHOR]

Operational efficiency and stability are two critical aspects to measure bus systems. Influenced by many stochastic factors, buses always suffer from delay and bunching. Traditional studies focus on a single route and lack research on the systematic evaluation of bus network. In this paper, we propose a data-driven framework to analyze the efficiency and stability based on small granularity GPS trajectory data from the perspective of entire bus network. The IC card data and route data are used to extract the boarding passenger number and topological structure, respectively. The results show that the average headway of stations follows a lognormal distribution. Moreover, the distribution of arrival efficiency of stations is inhomogeneous and a small number of stations have large values. In addition, the relationships among average headway of stations, boarding passenger number, bus number, and complex network indicators are revealed. It is found that the average headway of station is negatively correlated with other indicators, which implies that complex network connections and more passenger flows could weaken the efficiency of bus operations. This paper provides a way to evaluate the operational performance of bus networks and could give help for monitoring and optimizing the daily operation of bus systems., Author(s): Hui Zhang [1]; Yanjun Liu [1]; Baiying Shi (corresponding author) [1]; Jianmin Jia [1]; Wei Wang [2]; Xiang Zhao [3] 1. Introduction Nowadays, public transport plays a growing role [...]

The digital divide first emerged as an important social and human rights issue in the 1990s. With the rapid development of wireless network technology (e.g., Wi-Fi and cellular networks) and mobile terminal devices (e.g., mobile phones, laptops, and tablets), this issue has shifted to concern mobile Internet access. The three main Chinese telecom operators (China Mobile Communications Corporation, China Unicom Communications Corporation, and China Telecom Communications Corporation) have recently promoted unlimited data plans (UDPs) to reduce information usage costs and increase mobile data use. However, assessments of the impact of these policy changes on the mobile digital divide are limited. This study therefore offers insights into how a reduction in information costs can eliminate this divide. Using a difference-in-differences (DID) approach based on a monthly panel of system-generated mobile app data for subscribers, we find that the UDPs have various effects on promoting the use of mobile Internet and on the digital divide, depending on whether subscribers are of high or low socioeconomic status (i.e., urban versus rural and rich versus poor). We show that the relative differences are alleviated, but the absolute difference further increases after the implementation of a UDP subscription. In addition, a UDP subscription has heterogeneous effects on lifestyles. For the poor or those in rural areas, only promoting UDP does not effectively narrow the mobile digital divide because of insufficient cognition about and persistent weaknesses in the use of information technology. [ABSTRACT FROM AUTHOR]

Travel time reliability plays a key role in bus scheduling and service quality. Owing to various stochastic factors, buses often suffer from traffic congestion, delay and bunching, which leads to disturbances of travel time. Automatic vehicle location (AVL) could record the spatiotemporal information of buses, making it possible to understand the status of bus service. In this paper, we specifically analyze the statistical characteristics of travel time based on historic AVL data. Moreover, a Kalman filter-LSTM deep learning is proposed to estimate bus travel time. Numerical tests indicate that the travel time of bus routes shows a left-skewed and right-tail pattern with a good fit of the lognormal distribution. The bus service reliability fluctuates largely in the peak hours, especially the morning peak. Bus bunching and large bus time headway easily occur, and once it occurs, it will continue until destination. The Kalman filter-LSTM model outperforms the ensemble learning methods to predict travel time. This study could provide implications for transit schedule optimization to improve the bus service quality. [ABSTRACT FROM AUTHOR]