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edges_commercial_speed table:
This table holds data by each edge or street segment. Data is stored by each line, branch, direction and hour. How many GPS segments where use to compute that speed is stored in the weights attribute in order to compute weighted averages at any aggregated level (branch, line, segment). Aggregated stats will be in the same table with day = 'weekday' or hour = 'all day'.
line_commercial_speed table
This table holds aggregated commercial speed stats by line, computed from the gps_commercial_speed table at a service level.
- line_id
- branch_id
- direction
- day
- hour
- commercial_speed_h3_avg
- commercial_speed_h3_std
- commercial_speed_g_avg
- commercial_speed_g_std
- weights
* Use pandana to compute distances
* Computes travel time for trips and legs #141
* Improvement of basic KPI
* Add a feature to compute an OD Matrix for a given line #129 (#132)
* Gender and ticket price feature
* Compute destination gps by day
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Co-authored-by: Sebastian Anapolsky <[email protected]>
Main objective
Requirements
Steps
Data schema
gps_commercial_speed
table:This table holds data by each GPS point from the previous point
edges_commercial_speed
table:This table holds data by each edge or street segment. Data is stored by each line, branch, direction and hour. How many GPS segments where use to compute that speed is stored in the
weights
attribute in order to compute weighted averages at any aggregated level (branch, line, segment). Aggregated stats will be in the same table withday = 'weekday'
orhour = 'all day'
.line_commercial_speed
tableThis table holds aggregated commercial speed stats by line, computed from the
gps_commercial_speed
table at a service level.travel_time
tableThe text was updated successfully, but these errors were encountered: