Metadata-Version: 2.1
Name: MapMatching4GMNS
Version: 0.2.2
Summary: 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.
Home-page: https://github.com/asu-trans-ai-lab/MapMatching4GMNS
Author: Xuesong (Simon) Zhou, Kai (Frank) Zhang, Jiawei Lu
Author-email: xzhou74@asu.edu, zhangk2019@seu.edu.cn, jiaweil9@asu.edu
License: UNKNOWN
Description: # MapMatching2GMNS
        
        Please send your comments to <xzhou74@asu.edu> 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.
        
        [GMNS: General Modeling Network
        Specification (GMNS) ](https://github.com/zephyr-data-specs/GMNS)
        
        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. **Data flow**
        
        | **Input files** | **Output files** |
        |-----------------|------------------|
        | node.csv        | agent.csv        |
        | link.csv        |                  |
        | input_agent.csv |                  |
        
        12. **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.
        
        ![](media/22d8257ea35209b83eefefa4eec814c0.png)
        
        **File link.csv** provides essential link information of the underlying
        (subarea) network, including link_id, from_node_id and to_node_id.
        
        ![](media/1f78e34e3e8ff4091a1997e44825a503.png)
        
        **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.
        
        ![](media/5fdd74e09597da19d58779b8aaa7fc60.png)
        
        Another format of trace file is input_agent.csv, which could come from the
        [grid2demand](https://github.com/asu-trans-ai-lab/grid2demand) program. The
        geometry field describes longitude and latitude of each GPS point along the
        trace of each agent. In the following example there are exactly 2 GPS points as
        the origin and destination locations, while other examples can include more than
        2 GPS points along the trace. The geometry field follows the WKT format.
        
        https://en.wikipedia.org/wiki/Well-known_text_representation_of_geometry
        
        ![](media/308de5075f12b12dab40c3309182b047.png)
        
        1.  **Output file description**
        
            **File agent.csv** describes the most-likely path for each agent based on
            input trajectories.
        
        ![](media/caec124ffd9a88d841b924a0dda3d3b7.png)
        
        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.
        
            ![](media/bb2f8e2690c0478afdef7893260e5a16.png)
        
        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.
        
        ![](media/2c3e07d7afef6c519cf7ee331e0bace5.png)
        
        **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
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
