utdf2gmns 0.2.2

This open-source package is a tool to convert utdf file to GMNS format.

utdf2gmns: Introduction

This open-source package is a tool to convert utdf file to GMNS format.

Required Data Input Files:

• UTDF.csv
• node.csv (GMNS format)
• movement.csv (GMNS format)

Produced outputs

If input folder have UTDF.csv only, outputs are:

• A dictionary store utdf data with keys: Networks, Node, Links, Timeplans, Lanes, and utdf_intersection_geo
• A file named utdf2gmns.pickle to store dictionary object.

If input folder have extra node.csv and movement.csv, outputs are:

• Two files named movement_utdf.csv and intersection_utdf.csv
• A file named utdf2gmns.pickle to store dictionary object.

Package dependencies:

• geocoder==1.38.1
• numpy==1.23.3
• openpyxl==3.0.10
• pandas==1.4.4

Data Conversion Steps:

Step 1: Read UTDF.csv file and perform geocoding, then produce utdf_geo, utdf_lane, and utdf_phase_timeplans.

Step 2: Match four files (utdf_geo, node, utdf_lane, utdf_pahse_timeplans, movement) to produce movement_utdf

Installation

pip install UTDF2GMNS

Example

import utdf2gmns as ug
import pandas as pd
if__name__=="main":
    city =" Bullhead City, AZ"
    # option= 1, generate movement_utdf.csv directly
    # option= 2, generate movement_utdf.csv step by step (more flexible)

    option =1

    if option ==1:
        # NOTE: Option 1, generate movement_utdf.csv directly
        path =r"C:\Users\roche\Desktop\coding\data_bullhead_seg4"  # the fold contain UTDF.csv, node.csv and movement.csv
        res = ug.generate_movement_utdf(path, city,isSave2csv=True)

    if option ==2:
        # NOTE: Option 2, generate movement_utdf.csv step by step (more flexible)
        path_utdf =r"C:\Users\roche\Desktop\coding\data_bullhead_seg4\UTDF.csv"
        path_node =r"C:\Users\roche\Desktop\coding\data_bullhead_seg4\node.csv"
        path_movement =r"C:\Users\roche\Desktop\coding\data_bullhead_seg4\movement.csv"

        # Step 1: read UTDF.csv
        utdf_dict_data = ug.generate_utdf_dataframes(path_utdf, city)

        # Step 1.1: get intersection data from UTDF.csv
        df_intersection = utdf_dict_data["utdf_intersection"]

        # Step 1.2: geocoding intersection data
        df_intersection_geo = ug.generate_coordinates_from_intersection(df_intersection)

        # Step 2: read node.csv and movement.csv
        df_node = pd.read_csv(path_node)
        df_movement = pd.read_csv(path_movement)

        # Step 3: match intersection_geo and node
        df_intersection_node = ug.match_intersection_node(df_intersection_geo, df_node)

        # Step 4: match movement and intersection_node
        df_movement_intersection = ug.match_movement_and_intersection_node(df_movement, df_intersection_node)

        # Step 5: match movement and utdf_lane
        df_movement_utdf_lane = ug.match_movement_utdf_lane(df_movement_intersection, utdf_dict_data)

        # Step 6: match movement and utdf_phase_timeplans
        df_movement_utdf_phase = ug.match_movement_utdf_phase_timeplans(df_movement_utdf_lane, utdf_dict_data)

TODO LIST

• Print out how many intersections being geocoded.
• Print out how many movements being matched or not matched for signalized intersecton nodes in osm2gmns files.
• Add cycle length and green time for each movement.
• Check reasonable capacity.
• Check each movement is reasonable (like 15s of green time...). other attributes.
• Check number of lanes correctness between osm2gmns file and synchro file per movements.
• Print out check log.
• Number of lanes of the movements from synchro file.
• Add signal info to micre-link.csv
• Add function to verify whether geocoded for utdf_geo