Mining motif periodic frequent travel patterns of individual metro passengers considering uncertain disturbances

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Abstract

Periodic pattern mining is of great significance for understanding passenger travel behavior, but the previous works mainly focused on the trajectory data and the dimension of the spot/point. Besides, many uncertain factors (severe weather, traffic accident, etc.) may interfere with discovering original and accurate periodic travel patterns. This paper proposes a novel type of travel pattern called motif periodic frequent pattern (MPFP), which captures the periodicity of network temporal motifs of individual metro passengers with higher-order spatio-temporal characteristics, considering, uncertain disturbances. We also propose a new complete mining algorithm MPFP-growth to extract MPFP from smart card data (SCD), and apply the real long-time-span experimental data from a large-scale metro system is applied. Results show that frequent-travel metro passengers usually have some typical MPFPs with the temporal periodic characteristic of “week”. Only the top 10 types of all 4 624 types account for about 95% of all motifs and the top 5 types constitute about 90%, and the MPFP of the top 3 types of motifs account for nearly 80% of all periodic patterns, in which Mono-MPFP and 2-MPFP are the main ones. The relatively stable time range of MPFP is three months, and the threshold for the optimal uncertain disturbance factor should be set at 5%. Additionally, several interesting typical MPFPs of individual metro commuting passengers and their proportions are introduced to further understand the multifarious variants of MPFP.
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考虑不确定干扰的地铁个体乘客母题周期频繁出行模式挖掘
周期性模式挖掘对于理解乘客的出行行为具有重要意义,但以往的工作主要集中在轨迹数据和点/点的维度上。此外,许多不确定因素(恶劣天气、交通事故等)可能会干扰发现原始、准确的周期性出行模式。本文提出了一种新型的出行模式--周期性频繁模式(Motif periodic frequent pattern,MPFP),它能捕捉到具有高阶时空特征的地铁乘客个体的网络时空模式的周期性,并考虑了不确定的干扰因素。我们还提出了一种新的完整挖掘算法 MPFP-growth,用于从智能卡数据(SCD)中提取 MPFP,并应用了大规模地铁系统的真实长时跨实验数据。结果表明,经常乘坐地铁的乘客通常会有一些典型的 MPFP,这些 MPFP 具有 "周 "的时间周期特征。在全部 4 624 种类型中,只有前 10 种类型的主题词约占全部主题词的 95%,前 5 种类型的主题词约占 90%,前 3 种类型的主题词 MPFP 占全部周期性模式的近 80%,其中以 Mono-MPFP 和 2-MPFP 为主。MPFP 相对稳定的时间范围为 3 个月,最佳不确定干扰因子的阈值应设定为 5%。此外,还介绍了几种有趣的地铁乘客个人典型 MPFP 及其比例,以进一步了解 MPFP 的多种变体。
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来源期刊
International Journal of Transportation Science and Technology
International Journal of Transportation Science and Technology Engineering-Civil and Structural Engineering
CiteScore
7.20
自引率
0.00%
发文量
105
审稿时长
88 days
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