با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Investigating day-to-day variability of transit usage on a multimonth scale with smart card data: A case study in Lyon
بررسی تنوع روزانه استفاده از ترانزیت در مقیاس چند ماهه با داده های کارت هوشمند: یک مطالعه موردی در لیون-2020 To examine the variability of travel behaviour over time, transportation researchers need to collect longitudinal
data. The first studies around day-to-day variability of travel behaviour were based on surveys. Those studies
have shown that there is considerable variation in individual travel behaviour. They have also discussed the
implications of this variability in terms of modelling, policy evaluation or marketing. Recently, the multiplication
of big data has led to an explosion in the number of studies about travel behaviour. This is because
those new data sources collect lots of data, about lots of people over long periods. In the field of public transit,
smart card data is one of those big data sources. They have been used by various authors to conduct longitudinal
analyses of transit usage behaviour. However, researchers working with smart card data mostly rely on clustering
techniques to measure variability, and they often use conceptual framework different from those of
transportation researchers familiar with traditional data sources. In particular, there is no study based on smart
card data that explicitly measure day-to-day intrapersonal variability of transit usage. Therefore, the purpose of
this investigation is to address this gap. To do this, a clustering method and a similarity metric are combined to
explore simultaneously interpersonal and intrapersonal variability of transit usage. The application is done with
a rich dataset covering a 6 months period (181 days) and it contributes to the growing literature on smart card
data. Results of this research confirm previous works based on survey data and show that there is no one size fits
all approach to the problem of day-to-day variability of transit usage. They also prove that combining clustering
algorithm with day-to-day intrapersonal similarity metric is a valuable tool to mine smart card data. The findings
of this study can help in identifying new passenger segmentation and in tailoring information and services. Keywords: Public transit | Travel behaviour | Smart card data | Passenger clustering | Day-to-day variability | User segmentation |
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