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Are there clusters of long delays?? Are particular stops affected
One could identify delays that are outliers on the distribution of all delays, say the top 2.5%, and map these to the affected stops. Separate the data by trams and buses. Identify where in the city long delays tend to occur. One could also standardize by the expected travel time between stops, in case the expected travel time between between stops affects the distribution of delay times.
Line 33 delay gains.pdf
The graph is the main result of the today's effort, namely a visualization of delay gains along the line 33. Data shown is averaged over an (arbitrary) week. The delay gains are indicated by the width of the line segment by direction. At the stations the delay gain (for both directions) is indicated by the circle size and color intensity.
How can delays of vehicles and the number of passengers being affected by the delays in the course of a day be visualized?
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