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A new fiscal/academic year has started. There may be a bunch of people who have begun a new life, but it is hard to get used to traffic environment in a new place. Especially in a big city, traffic delay happens very often and to find alternative routes in such a complicated traffic system is really exhausting.
Our theme this time is “train delay.” When delay happens, information about it spreads over SNS, showing users being irritated or panicked.
How does SNS world react to train delay? Is there any characteristic from start to calm of SNS reaction? We analyzed relationship between hours of delay and behavior of SNS posts in spring.
In this report, we use SNS analysis tool. Data are analyzed in a comprehensive manner using posts on SNS which are divided into “positive” and “negative.”
We first calculated the ratio of passing time until the number of SNS posts hit a peak to minutes of delay. Then the peak comes at the shortest time when accidents causing injury happen. It comes with moderate speed when delay occurs with other causes of “Other lines’ delay,” “Rail company,” “Natural disaster,” and “Multiple causes.” If passengers cause delay, information about delay spreads very slowly; the peak comes after resumption of operation in some cases.
Here the ratios of passing time the number of SNS posts takes to calm down to minutes of delay according to each cause. In general, SNS takes more than 3 times as minutes of delay to calm down. Among causes above, when it comes to “Natural disaster” or “Passengers,” people keeps posting on SNS for a long time although delay occurs due to such causes; passing time to calm down is 7.9 times and 14.2 times as average minutes of delay of each cause, respectively. When causes are “Other lines delay” or “Rail company” time taken to calm down is on average, and calm of SNS comes most quickly when delay happens because of “Accidents causing injury” or “Multiple causes.”
To do similar analyses by lines, we then see what kind of causes triggers delay for each line.
Causes of delay are biased depending on lines as the picture above shows. Not to let this bias influence on our analyses, we next narrowed causes to “Accidents causing injury” and did the same analyses as we have done from the perspective of causes. For further results and details, do not hesitate to download PDF files for free.