Spatial-Temporal Analysis on Spring Festival Travel Rush in China Based on Multisource Big Data

Abstract

Spring Festival travel rush is a phenomenon in China that population travel intensively surges in a short time around Chinese Spring Festival. This phenomenon, which is a special one in the urbanization process of China, brings a large traffic burden and various kinds of social problems, thereby causing widespread public concern. This study investigates the spatial-temporal characteristics of Spring Festival travel rush in 2015 through time series analysis and complex network analysis based on multisource big travel data derived from Baidu, Tencent, and Qihoo. The main results are as follows: First, big travel data of Baidu and Tencent obtained from location-based services might be more accurate and scientific than that of Qihoo. Second, two travel peaks appeared at five days before and six days after the Spring Festival, respectively, and the travel valley appeared on the Spring Festival. The Spring Festival travel network at the provincial scale did not have small-world and scale-free characteristics. Instead, the travel network showed a multicenter characteristic and a significant geographic clustering characteristic. Moreover, some travel path chains played a leading role in the network. Third, economic and social factors had more influence on the travel network than geographical location factors. The problem of Spring Festival travel rush will not be effectively improved in a short time because of the unbalanced urban-rural development and the unbalanced regional development. However, the development of the modern high-speed transport system and the modern information and communication technology can alleviate problems brought by Spring Festival travel rush. We suggest that a unified real-time traffic platform for Spring Festival travel rush should be established through the government's integration of mobile big data and the official authority data of the transportation department.

Social Sensing of the Imbalance of Urban and Regional Development in China Through the Population Migration Network around Spring Festival

“…In addition to the traditional decennial census data, limited population flow data have been made publicly available by several location-based services (LBS) platforms, allowing a data-driven social sensing approach to observe population migration around Spring Festival. Three mainstream LBS platforms (i.e., Baidu, Tencent, and Qihoo) were compared in reference [23]. Qihoo does not provide city-level population flow data, while Baidu and Tencent provide only the top 10 inflows and top 10 outflows for each city.…”

“…The current research based on SFT is based mainly on LBS data provided by Baidu and Tencent in 2015 and 2016 [23,29,30]. Therefore, we use the Sina Weibo data obtained through the API provided by the Sina Weibo platform in 2015 to conduct research, which is convenient for comparison with other related research, published urban economic statistics, and China's 13th Five-Year Development Plan.…”

Exploring Spatial and Temporal Connection Patterns among the Districts in Chongqing Based on Highway Passenger Flow

“…However, the actual traffic flow data between cities is often difficult to obtain. Therefore, identification of alternative parameters to represent traffic flow between cities has become the research direction of scholars at home and abroad [4,5]. In many studies, traffic flow is usually replaced by actual shift information.…”

Method for the Analysis and Visualization of Similar Flow Hotspot Patterns between Different Regional Groups

“…However, the spatial dependence of interactive nodes in these methods is limited. Some of these nodes apply complex network methods to discover spatial interaction patterns [17][18][19][20]. Models for regional interaction based on the concept of complex networks have been proposed [21].…”