IRTX JSprit to COPERT connector

Introduction

This model is a connector between the upstream JSprit model and the downstream COPERT model for emissions analysis.

The purpose of this connector is to process a given JSprit scenario and its solution, aggregated the obtained vehicle traces to COPERT-compatible vehicle type information and prepare the input file for COPERT.

Requirements

Software requirements

The converter is packaged in a Python script. All dependencies to run the model have been collected in a conda environment, which is available in the LEAD repository as environment.yml.

Input / Output

Input

To run the model, a downstream JSprit scenario (scenario.json) and its JSprit solution (solution.json) must be available.

Output

The output of the model is an Excel sheet that can be transmitted for emissions calculation to the COPERT model (copert.xlsx).

Configuration

The conversion process needs to be configured using a configuration file (configuration.json) in JSON format. It is structured as follows:

{
  "peak_share": 0.3,
  "peak_speed_factor": 0.7,
  "temperature": {
    "min": [
      1.1, 1.4, 4.2, 7.2, 11.2, 15.0, 17.0, 16.6, 12.8, 9.6, 4.9, 2.0
    ],
    "max": [
      7.1, 9.0, 13.8, 17.4, 21.5, 25.6, 28.2, 28.0, 23.1, 17.7, 11.4, 7.7
    ]
  },
  "humidity": [
    84.0, 80.0, 74.0, 71.0, 72.0, 70.0, 65.0, 70.0, 76.0, 82.0, 84.0, 86.0
  ],
  "vehicle_type_mapping": {}
}

Since the JSprit model does not explicity take into account the time of day (but the time elapsed from the first movements of a vehicle), the share of movements during peak hour needs to be defined manually as peak_share. Furthermore, JSprit solutions are based on optimal travel times. Therefore, the peak_speed_factor can be defined to modulate the vehicle-specific speeds. After, maximum and minimum temperatures for the twelve months of the year (starting January) are given, as well as the mean humidity in percentage points. Finally, mappings from the optimized JSprit vehicle types to the COPERT emission classes are given as follows:

{
  "vehicle_type_mapping": {
    "van": {
      "category": "Light Commercial Vehicles",
      "fuel": "Diesel",
      "segment": "N1-III",
      "euro_standard": "Euro 5",
      "fuel_tank_size": 100
    }
  }
}

Here, an example for the van JSprit vehicle type is given. It is mapped to a light vehicle emissions class for Diesel with a specified fuel tank size.

Running the model

The model is defined in a Jupyter notebook called Convert.ipynb. To run it, call it through the papermill command line utility (which is installed as a dependency in the conda environment) as described below:

papermill "Convert.ipynb" /dev/null \
  -pconfiguration_path /path/to/configuration.json \
  -pscenario_path /path/to/irtx-jsprit/scenario.json \
  -psolution_path /path/to/irtx-jsprit/solution.json \
  -poutput_path /path/to/output/copert.xlsx \
  -pyear 2022

The mandatory parameters are detailed in the following table:

Parameter	Values	Description
configuration_path	String	Path to the conversion configuration file (see above)
scenario_path	String	Path to the JSprit scenario definition file
solution_path	String	Path to the JSprit solution file
output_path	String	Path where the COPERT data will be saved

The following optional parameter is available:

Parameter	Values	Description
year	Integer (default 2022)	Year for which the information is written in the COPERT file

Standard scenarios

For the Lyon living lab, a configuration file has already been prepared in data/configuration_lyon.json. It can be used to prepare COPERT data for the three main scenarios (Baseline 2022, UCC 2022, UCC 2030) as follows:

papermill "Convert.ipynb" /dev/null \
  -pconfiguration_path data/configuration_lyon.json \
  -pscenario_path /path/to/irtx-jsprit/output/scenario_{scenario}.json \
  -psolution_path /path/to/irtx-jsprit/output/solution_{scenario}.json \
  -poutput_path /path/to/irtx-jsprit-copert-connector/output/copert_{scenario}.xlsx \
  -pyear {year}

Here, {scenario} = baseline_2022 | ucc_2022 | ucc_2030 and year = 2022 | 2030 according to the respective scenario.