A Visual Analytics Approach for Inferring Passenger Demand in Public Transport System based on Bus Trajectory
The control and monitoring of public transport buses considering the Global Positioning System (GPS) produce a data tsunami for the creation of indicators related to public transportation. The conventional techniques of data analysis for this type of information require programming effort, execution of algorithms with high processing in extensive databases to get at the end the production of the idealized statistical and visualization reports. In this process, the search for new analyzes or visualizations may require a restart of the process, a control of the versioning of the developed programs and repetitive high processing algorithm. This form of acting hinder the cognitive process, the analysis capability and the inference of relevant information. In this context, this paper proposes a methodology based on Visual Analytics to infer passenger demand based on the trajectory of conventional buses for planning new routes served by electric buses at the State University of Campinas - UNICAMP. The methodology includes a space-time stage and another for human interactivity, with easily configurable graphics for evaluation of indicators. Results show the locations and times with the highest use of the transportation service and can be used to identifying new routes to be served by electric buses on campus.