Results

Results#

RouteE-Transit Energy predictions are stored within the energy_predictions attribute of GTFSEnergyPredictor. If save_results=True in GTFSEnergyPredictor.run(), they are written to CSV files as well. This includes the following files:

  • trip_energy_predictions.csv: The main intended output of the RouteE-Transit pipeline, which includes energy predictions at the GTFS trip level, for each vehicle model and weather scenario analyzed.

  • link_energy_predictions.csv: More granular energy predictions at the road link level. These can be used, for example, to map individual bus trips and see which segments are most energy intensive.

trip_energy_predictions.csv#

The RouteE-Transit results file (trip_energy_predictions.csv) includes the following columns:

  • trip_id: GTFS trip ID

  • miles: distance of trip in miles (calculated from GTFS shapes)

  • vehicle: name of the vehicle model used to generate energy prediction (e.g., Transit_Bus_Battery_Electric)

  • energy_used: estimated total energy consumed during this trip, including HVAC energy for electric vehicles

  • energy_unit: unit of energy consumption for this trip

  • route_id: GTFS route ID

  • service_id: GTFS service ID

  • block_id: GTFS block ID

  • shape_id: GTFS shape ID

  • route_short_name: GTFS short name of route served on this trip (the one typically used for displaying to riders, e.g., route number)

  • agency_id: GTFS agency_id of the agency that operates this trip

  • trip_type: the type of trip: service for passenger service trips defined in GTFS, pull-out for deadhead trips from the depot to first trip of the day, pull-in for deadhead trips from last trip of the day to depo, or mid_block_deadhead for deadhead connecting two service trips

  • start_time: start time of the trip, merged in from GTFS stop_times.txt

  • end_time: end time of the trip, merged in from GTFS stop_times.txt

  • trip_duration_minutes: duration of the trip in minutes (based on start_time and end_time). not yet implemented for deadhead trips

  • trip_count: the number of days in the service period described by this GTFS data that the trip operates (a positive integer)

  • from_trip: for deadhead trips, the trip ID of the service trip before this deadhead trip

  • to_trip: for deadhead trips, the trip ID of the service trip after this deadhead trip

  • scenario: weather scenario used to estimate HVAC energy demand. Current options are “winter”, “summer”, or “median"

  • hvac_energy_kWh: estimated HVAC energy consumption for electric buses, in kWh. Note this has already been added to energy_used in those cases.