MapMatching4GMNS 0.2.11

An open-source, cross-platform, lightweight, and fast Python MapMatching4GMNS engine for mapping GPS traces to the underlying network using General Modeling Network Specification (GMNS). Its most likely path finding algorithm takes about 0.02 seconds to process one GPS trace with 50 location points in a large-scale network with 10K nodes.

MapMatching2GMNS

Please send your comments to [e-mail] if you have any suggestions and questions.

Based on input network and given GPS trajectory data, the map-matching program of MapMatching4GMNS aims to find the most likely route in terms of node sequence in the underlying network, with the following data flow chart.

Test Python Script: https://github.com/xiaomo123zk/MapMatching4GMNS-0.2/tree/main/MapMatching4GMNS.ipynb

GMNS: General Modeling Network Specification (GMNS)

Installation

MapMatching4GMNS has been released on PyPI, and can be installed using

$ pip install MapMatching4GMNS

If you need a specific version of MapMatching4GMNS, say, 0.2.9,

$ pip install MapMatching4GMNS==0.2.9 --upgrade

Dependency: An environment that needs to be installed in advance for the code to run normally

Windows Users
You do not need to install Microsoft Visual C++ Redistributable for Visual Studio; only copy the missing dependency libraries from the Dependent_libraries_missing_in_windows_system folder to your computer path(C:\Windows\System32).
Note: If your windows system has existed some dependencies in the C:\Windows\System32, you only need to copy the dependency libraries that are not.

Getting Started

Download the Test Data Set

A sample data set with six different networks are provided. You can manually retrieve each individual test network from here.from MapMatching4GMNS import *

# first, check your operation system.

# If you run the code on windows system without installed C++ environment,

# some necessary dependency libraries need to be copied.

# If an error occurs: Permission denied: 'C:/Windows/System32/*.dll',

# you need to manually copy the dependency library from https://github.com/xiaomo123zk/MapMatching4GMNS-0.2/tree/main/Dependent_libraries_missing_in_windows_system

#First, download the input data of the test: node.csv, link.csv and trace.csv from Github.

MapMatching4GMNS.download_sample_data_sets_from_network()

#If the online download fails**,** Please download manually the input data from https://github.com/asu-trans-ai-lab/osm_test_data_set/map_matching/.

#Second, call the mapmatching4gmns library to calculate and output the result in the current directory.

MapMatching4GMNS.map_match()

The calculation process of MapMatching2GMNS

1. Data flow

Input files	Output files
node.csv	intput_agent.csv
link.csv	agent.csv
trace.csv	agent_performance.csv

1. Read standard GMNS network files node and link files

2. Read GPS trace.csv file Note: the M2G program will convert trace.csv to input_agent.csv for visualization in NeXTA.

3. Construct 2d grid system to speed up the indexing of GSP points to the network. For example, a 10x10 grid for a network of 100 K nodes could lead to 1K nodes in each cell.

4. Identify the related subarea for the traversed cells by each GPS trace, so only a small subset of the network will be loaded in the resulting shortest path algorithm.

5. Identify the origin and destination nodes in the grid for each GPS trace, in case, the GPS trace does not start from or end at a node inside the network (in this case, the boundary origin and destination nodes will be identified). The OD node identification is important to run the following shortest path algorithm.

6. Estimate link cost to calculate a generalized weight/cost for each link in the cell, that is, the distance from nearly GPS points to a link inside the cell.

7. Use likely path finding algorithm selects the least cost path with the smallest generalized cumulative cost from the beginning to the end of the GPS trace.

8. Identify matched timestamps of each node in the likely path

9. Output agent file with map-matched node sequence and time sequence

10. Output link performance with estimated link travel time and delay based on free-flow travel time of each link along the GPS matched routes

11. Input file description

    File node.csv gives essential node information of the underlying (subarea) network in GMNS format, including node_id, x_coord and y_coord.

File link.csv provides essential link information of the underlying (subarea) network, including link_id, from_node_id and to_node_id.

Input trace file as

The agent id is GPS trace id, x_coord and y_coord should be consistent to the network coordinate defined in node.csv and link.cvs. Fields hh mm and ss correspond the hour, minute and second for the related GPS timestamp. We use separate columns directly to avoid confusion caused by different time coding formats.

https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry

1. Output file description

File agent.csv describes the most-likely path for each agent based on input trajectories.

File agent_performance.csv.

The first visualization tool NeXTA: the original input_agent.csv and resulting agent.csv can be visualized through NeXTA.

1. Load the network node.csv and click on the following 4 buttons or menu check box.

2. The original GPS trace is shown in green and the map-matched route in the network is displayed in purple. The user can use the scroll wheel of the mouse to zoom in the focused area.

The first visualization tool QGIS: the original input_agent.csv and resulting agent.csv, link_performance.csv can be visualized through QGIS**.**

Note: The QGIS is a free and open source geographic information system. You need to install QGIS on your computer in advance. The official download address is https://qgis.org/en/site/forusers/download.html.

1. Double click the QGIS.qgz in the "release" folder, which is the QGIS project, where the node, link, agent, trace, and background map have been loaded in QGIS.

1. The original GPS trace is shown in red point, the node is shown in purple point, and the agent is shown in continuous red curve.

1. The link_performance.csv is loaded in QGIS as the layer, and the map-matched route in the network is displayed in a continuous red curve, as the topo main road. The user can use the scroll wheel of the mouse to zoom in the focused area.

The process is shown in the figure below:

Reference:

This code is implemented based on a published paper in Journal of Transportation Research Part C:

Estimating the most likely space–time paths, dwell times and path uncertainties from vehicle trajectory data: A time geographic method

https://www.sciencedirect.com/science/article/pii/S0968090X15003150