Volume 39 Issue 3
Jun.  2021
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CUI Hongjun, SUN Wanru, ZHAO Rui, ZHU Minqing, LI Xia. A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity[J]. Journal of Transport Information and Safety, 2021, 39(3): 128-135. doi: 10.3963/j.jssn.1674-4861.2021.03.016
Citation: CUI Hongjun, SUN Wanru, ZHAO Rui, ZHU Minqing, LI Xia. A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity[J]. Journal of Transport Information and Safety, 2021, 39(3): 128-135. doi: 10.3963/j.jssn.1674-4861.2021.03.016

A Quantitative Analysis on Repeatability of Residents'Bus Trip Chain and Travel Regularity

doi: 10.3963/j.jssn.1674-4861.2021.03.016
  • Received Date: 2020-06-16
  • As a hub system of urban transportation, public transportation carries a large number of residents'travel.The IC card data collected by the automatic data collection system contains a large number of passenger-travel information, which can be analyzed to optimize the public transport service.The information entropy and entropy rate are introduced to quantify the repeatability of the trip chain of public transport, with the method of analyzing the law of public transport travel based on the quantitative index studied.The trip chain of passengers is transformed into a discrete travel sequence through the state calibration of the travel place.Information entropy and the entropy rate are used to quantitatively analyze the travel sequence, thus obtaining the relationship between travel repeatability and quantitative index.In other words, the higher the information entropy of the travel sequence, the lower the entropy rate, the higher the passengers'travel repeatability, and the stronger the travel rule.Based on the repeatability of quantitative processing, the work takes the travel data of smart card passengers in Shijiazhuang as a case study to analyze the travel rules of bus passengers from group and individual.The results show that the quantification index of trip-chain repeatability can intuitively judge the strength of travel rules.When the information entropy is higher than the sample mean(2.53 bits)and the entropy rate is lower than the sample mean(1.13 bits/event)with the unobvious travel rules of passengers, the potential travel rules of passengers can be mined through further analysis.

     

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