DE-NOISING SCHEDULED TRANSPORTATION DATA
Embodiments of the disclosure include a method for de-noising data in a scheduled transportation system, the method includes receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system. The method also includes identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes and generating a route map and schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.
1 . A computer system for de-noising data in a scheduled transportation system, the computer system comprising:
a scheduling device having a processor, the processor configured to perform a method comprising:
receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system;
identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes;
identifying a plurality of stops made by the piece of transportation equipment during each of the plurality of journeys;
classifying each of the plurality of identified stops into a type of stop;
identifying a route map comprising at least a portion of the plurality of identified stops;
identifying a schedule for the scheduled transportation system; and
updating the route map and the schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.
2 . The computer system of claim 1 , wherein each of the plurality of digital traces comprises a location, a time-stamp, and an identification of the piece of equipment in the scheduled transportation system.
3 . The computer system of claim 1 , wherein the type of stop comprises at least one of a scheduled stop and a non-scheduled stop.
4 . The computer system of claim 1 , wherein classifying each of the set of potential stops includes calculating a confidence level.
5 . The computer system of claim 1 , wherein the classifying comprises applying a partial ground truth.
6 . The computer system of claim 1 , wherein the identification of the schedule includes the identification of the arrival times of the piece of transportation equipment at scheduled stops.
7 . The computer system of claim 1 , wherein updating the route map and schedule includes removing one or more scheduled stops from the route map and schedule.
8 . The computer system of claim 1 , wherein updating the route map and schedule includes adding one or more scheduled stops to the route map and schedule.
9 . The computer system of claim 1 , wherein updating the route map and schedule includes correcting a characteristic of one or more scheduled stops of the route map and schedule.
10 . The computer system of claim 9 , wherein the characteristics of a scheduled stop include at least one of a location a list of lines serving the scheduled stop, a time of arrival of vehicles at the scheduled stop.
11 . The computer system of claim 1 , wherein classifying each of the plurality of identified stops into the type of stop further comprises:
clustering the plurality of stops along one of the plurality of routes into a set of potential stops;
computing a feature set for each of the set of potential stops; and
classifying each of the set of potential stops into the type of stop based on the feature set and a classification model.
12 . A computer program product for de-noising data in a scheduled transportation system, the computer program product comprising:
a computer readable storage medium having computer readable program code embodied therewith, the computer readable program code comprising:
computer readable program code configured for:
a scheduling device having a processor, the processor configured to perform a method comprising:
receiving a plurality of digital traces that correspond to a piece of equipment in the scheduled transportation system;
identifying a plurality of journeys from the plurality of digital traces, wherein each of the plurality of journeys corresponds to the piece of equipment traversing one of a plurality of routes;
identifying a plurality of stops made by the piece of transportation equipment during each of the plurality of journeys;
classifying each of the plurality of identified stops into a type of stop;
identifying a route map comprising at least a portion of the plurality of identified stops;
identifying a schedule for the scheduled transportation system; and
updating the route map and the schedule for the scheduled transportation system from the plurality of journeys and the plurality of digital traces.
13 . The computer program product of claim 12 , wherein each of the plurality of digital traces comprises a location, a time-stamp, and an identification of the piece of equipment in the scheduled transportation system.
14 . The computer program product of claim 12 , wherein the type of stop comprises at least one of a scheduled stop and a non-scheduled stop.
15 . The computer program product of claim 12 , wherein classifying each of the set of potential stops includes calculating a confidence level.
16 . The computer program product of claim 12 , wherein the classifying comprises applying a partial ground truth.
17 . The computer program product of claim 12 , wherein the identification of the schedule includes the identification of the arrival times of the piece of transportation equipment at scheduled stops.
18 . The computer program product of claim 12 , wherein updating the route map and schedule includes removing one or more scheduled stops from the route map and schedule.
19 . The computer program product of claim 23 , wherein updating the route map and schedule includes adding one or more scheduled stops to the route map and schedule.
20 . The computer program product of claim 12 , wherein updating the route map and schedule includes correcting a characteristic of one or more scheduled stops of the route map and schedule.
21 . The computer program product of claim 20 , wherein the characteristics of a scheduled stop include at least one of a location a list of lines serving the scheduled stop, a time of arrival of vehicles at the scheduled stop.
22 . The computer program product of claim 12 , wherein classifying each of the plurality of identified stops into the type of stop further comprises:
clustering the plurality of stops along one of the plurality of routes into a set of potential stops;
computing a feature set for each of the set of potential stops; and
classifying each of the set of potential stops into the type of stop based on the feature set and a classification model.