Then, the memory value can be obtained by calculating their first-order autocorrelation function.
#BUS PRESENTS SERIES#
First, the time series of these activities is calculated.
For an individual user, the similarity between his two consecutive activities is called memory. Goh proposed to analyze the “burstiness” feature and the “memory” feature of human behavior time series.īurstiness refers to the intermittent increase and decrease of activity or event frequency, calculated by the variation coefficient to get the result.
#BUS PRESENTS MOVIE#
Through many empirical statistics, such as email communication, web browsing, and online movie on demand, in these people’s behaviors, scientists found that the inter-event time presents a power-law curve. The time features of human behavior refer to the statistical law of time shown by people engaged in a specific event many times. Barabási published a paper in Nature and proposed that human behavior’s inter-event time distribution presents the power-law feature, which made many scholars start to study human behavior. The earliest analysis of human behavior can be traced back to 2005. Mining and analyzing the travel behavior of bus passengers, demonstrating the travel behavior of bus passengers, and finding the evolution of bus passenger flow can provide decision support for the transportation bureau, bus operation companies, and other departments to plan bus operation routes and improve operation efficiency, which has substantial practical value. The booking records of numerous passengers on the website provide feasibility for analyzing the mobility behaviors of passengers who travel by intercity bus. An increasing number of passengers use smart travel apps to book bus tickets.
#BUS PRESENTS SOFTWARE#
In the “Internet +” era, smart travel software has been widely used. More and more medium and short-distance passengers choose to travel by intercity bus instead of self-driving. In recent years, green, low-carbon travel modes have been vigorously promoted in China.
The research conclusions are helpful to deeply understand the features of human mobility behaviors in theory, and can assist the transportation department in traffic planning in the application. Last, holidays have a significant influence on passengers’ travel behaviors, which leads to more trips. Furthermore, the difference in cyclotron radius between these two groups’ travelling distances is quite significant roundtrips from Shanghai are frequent. Also, travel distance displays a scale-free property, and it is more likely to have an exponential distribution. In each group, the passengers’ travelling interval time presents a power-law with a cutoff index, and the passengers’ travelling behaviors have negative memory and low burstiness. Based on passengers’ intercity bus ticket reservation records (roundtrips from Shanghai or Chongqing city) from a smart tourism app, the travel behaviors of these two groups of bus passengers are analyzed and compared. The features of intercity bus passenger group mobility behaviors have important guiding significance for the transportation department.
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