# MapMatching4GMNS
Please send your comments to <xzhou74@asu.edu> if you have any suggestions and
questions.
## 1. Introduction
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.
GMNS: General Modeling Network Specification (GMNS)
(<https://github.com/zephyr-data-specs/GMNS>)
## 2. Data flow
| | **files** | **Data Source** | **Visualization** |
|------------------------------|--------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------|
| GMNS network input | node.csv, link.csv | [Osm4GMNS](https://osm2gmns.readthedocs.io/en/latest/) | [QGIS](https://www.qgis.org/en/site/), [web interface for GMNS](https://asu-trans-ai-lab.github.io/index.html#/) |
| Location sequence data input | trace.csv | GPS traces downloaded from OpenStreetMap, e.g., using the script at <https://github.com/asu-trans-ai-lab/MapMatching4GMNS/blob/master/release/get_gps_trace.py> | QGIS |
| Map-matched output | route.csv | | QGIS |
The M4G program can be executed using one of the following 2 different modes:
The Python package mode is mainly used in an effective integration with other
packages such as OSM2GMNS and Path2GMNS. The windows executable mode aims to
help users generate the results directly without replying on the python
environment.
**Mode 1: Python package and test script**:
<https://github.com/asu-trans-ai-lab/MapMatching4GMNS/blob/master/MapMatching4GMNS.ipynb>
**Mode 2: Windows Executable: M4G.exe** can be found from
<https://github.com/asu-trans-ai-lab/MapMatching4GMNS/tree/master/release>
## 3. File description
> **File node.csv** gives essential node information of the underlying network
> in GMNS format, including node_id, x_coord and y_coord.

**File link.csv** should include essential link information of the underlying
(subarea) network, including from_node_id, to_node_id, length and geometry.

**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.

**Output file description**
> **File route.csv** describes the most-likely path for each agent based on
> input trajectories.

## 4. Visualization
### Step 1: Load GMNS files in QGIS
Install and open QGIS and click on menu Layer-\>Add-\>Add Delimited Text Layer.
In the following dialogue box, load GMNS node.csv and link.csv, and ensure
“point coordinates” is selected as geometry definition for node.csv wit x_coord
and y_coord for “Geometry field”, and WKT is selected as geometry definition for
link.csv.


### Step 2: Load XYZ Tiles in QGIS with background maps
Find XYZ Tiles and double-click OpenStreetMap on Browser panel. Please move the
background layer to the bottom to show the GMNS network.
Refence:
<https://gis.stackexchange.com/questions/20191/adding-basemaps-from-google-or-bing-in-qgis>
### Step 3. Visualize input trace and output route files in QGIS
The 'geometry' field can be obtained from link.csv file. Then open this file in
the same way as above. (Menu Layer-\>Add-\>Add Delimited Text Layer)



## 5. Algorithm
1. **Read standard GMNS network files** node and link files, **Read GPS
trace.csv** file
Note: the M4G program will convert trace.csv to input_agent.csv for
visualization in NeXTA.
2. **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.
3. **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.
4. **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.
5. **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.
6. 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.
7. **Identify matched timestamps** of each node in the likely path
8. **Output route.csv** with **estimated link travel time and delay** based on
free-flow travel time of each link along the GPS matched routes
## Reference
This code is implemented partially based on a published paper in Transportation
Research Part C:
Tang J, Song Y, Miller HJ, Zhou X (2015) “Estimating the most likely space–time
paths, dwell times and path uncertainties from vehicle trajectory data: A time
geographic method,” *Transportation Research Part C*,
<http://dx.doi.org/10.1016/j.trc.2015.08.014>