Fast map matching (FMM)

This project is an implementation of the fast map matching (FMM) algorithm introduced in this paper Fast map matching, an algorithm integrating hidden Markov model with precomputation, which acelerates the performance by precomputation. A post-print version of the paper can be downloaded at my home page.

Features of fmm

Highly optimized code in C++ using Boost libraries (Graph and Rtree index)
Map matching speed of 25,000-45,000 points/second (single processor)
Customized output mode (matched point,matched line,offset,edge ID)
Tested on city level road network and millions of GPS records
🎉 Parallel map matching with OpenMP ( 5-6 times of the single processor speed).

Install
Run map matching
Input and output
Configuration
Example
Performance measurement
Contact and citation

Install

Requirements

Linux/Unix environment (tested on Ubuntu 14.04)
gcc >= 4.4 (gnu++11 used)
GDAL >= 1.11.2: IO with ESRI shapefile, Geometry data type
Boost Graph >= 1.54.0: routing algorithms used in UBODT Generator
Boost Geometry >= 1.54.0: Rtree, Geometry computation
Boost Serialization >= 1.54.0: Serialization of UBODT in binary format

The required libraries can be installed with

sudo apt-get install gdal-bin libgdal-dev libboost-dev

Installation

Change to the project directory, open a terminal and run

make

It will build executable files under the dist folder:

ubodt_gen: the Upper bounded origin destination table (UBODT) generator (precomputation) program
fmm: the map matching program (single processor)
fmm_omp: parallel map matching implemented with OpenMP.

Then run

make install

It will copy these executable files into the ~/bin path, which should be added to the PATH variable by default.

To manually add the ~/bin path to $PATH variable, open a new terminal and run:

echo 'export PATH=$PATH:$HOME/bin' >> ~/.bashrc
source ~/.bashrc

Verfication of installation

Open a new terminal and type fmm, you should see the following output:

------------ Fast map matching (FMM) ------------
------------     Author: Can Yang    ------------
------------   Version: 2017.11.11   ------------
------------     Applicaton: fmm     ------------
No configuration file supplied
A configuration file is given in the example folder
Run `fmm config.xml`

Run map matching

The programs take configuration in xml format as input

# Run UBODT precomputation 
ubodt_gen ubodt_config.xml 
# Run map matching (Single processor)
fmm fmm_config.xml
# Run map matching parallely (omp stands for OpenMP) 
fmm_omp fmm_config.xml

Check the example for configuration file format.

Input and output

Input

Two files should be prepared for the map matching program:

GPS trajectory file: an ESRI shapefile (LineString) with an ID field. Each row stores a trajectory.
Network file: an ESRI shapefile (LineString), each row stores a network edge with ID, source and target fields, which defines the topology of network graph.

For more details, please to refer to the ubodt configuration and fmm configuration.

Useful resources

If you already have a road network file in GDAL supported formats, e.g., ESRI shp, GeoJSON and CSV, you may encounter a problem of creating topology of the network, namely, defining id, source and target fields. Spatial database PostGIS and its extension pgRouting can solve the problem:

Output

The output of program ubodt_gen is a CSV file or a Binary file, which is automatically detected from the file extension csv or bin. Binary file can be used to save space in case of a large road network.

The CSV file contains the following information:

source: source (origin) node
target: target (destination) node
next_n: the next node visited after source in the shortest path
prev_n: the previous node visited before target in the shortest path
next_e: the next edge index visited after source in the shortest path
distance: the shortest path distance

The output of program fmm is a CSV file containing the following information:

id: id of trajectory
o_path: optimal path, edges matched for each point in a trajectory
c_path: complete path, edges traversed by the trajectory
geom: geometry of the complete path

Note: In UBODT, the edge index is stored in next_e. However, in the final output, the element is exported as the id attribute of edge specified by the configuration fmm_config>network>id, which is a string value.

Configuration

Two example configuration files are given in the example folder.

ubodt_config.xml: configuration file for the ubodt_gen program.
fmm_config.xml: configuration file for the fmm program

Configuration of ubodt_gen

ubodt_config
- input
  - network
    - file: network file in ESRI shapefile format
    - id: column name storing id
    - source: column name storing source
    - target: column name storing target
- parameters
  - delta: Upper bound of shortest path distance
- output
  - file: output file in CSV format or Binary format, detected from the file extension (csv or bin) automatically.

⚠️ Delta should be specified in the same spatial unit as the network file. If the reference system is WGS84 (in degree), then 1 degree of latitude or longitude equals to about 111km. It is suggested to try some small values first (e.g., 0.01 degree).

Configuration of fmm

fmm_config
- input
  - ubodt
    - file: ubodt file path, CSV or Binary format detected from the file extension (csv or bin) automatically.
    - nhash: hashtable bucket size, recommended to be a prime number
    - multipler: used to get a unique key as n_o*multiplier+n_d in hash table, recommended to be the number of nodes in network file
  - network
    - file: network file path, in ESRI shapefile format
    - id: column name storing id
    - source: column name storing source
    - target: column name storing target
  - gps
    - file: GPS trajectory file path, in ESRI shapefile format
    - id: column name storing id
- parameters
  - k: number of candidates in MM
  - r: search radius r
  - pf: penalty factor for reversed movement
  - gps_error: gps error used in emission probability calculation
- output
  - mode: output mode
    - 0: id + o_path + c_path (Default mode)
    - 1: id + o_path + c_path + geom(wkb)
    - 2: id + o_path + c_path + geom(wkt) // consumes a lot of storage, for small data set
    - 3: id + o_path + (L-offset) + c_path
  - file: the matched file

Note that search radius and gps error should be specified in the same unit as the network file.

Example

Check the example folder.

Performance measurement

Map matching

A case study is reported in the original paper with real world datasets in Stockholm:

GPS: 644,695 trajectories containing 6,812,720 points
Road network: 23,921 nodes and 57,928 directed edges
UBODT size: 4,305,012 rows
k = 8 (candidate set size), r = 300 meters (search radius)

As reported in the paper, on a desktop with Intel(R) Core(TM) 2 Quad CPU Q9650 @ 3.00GHz (4 processors), the speed of map matching (single processor) is about:

25000 points/s (WKB Geometry output, mode 1)
45000 points/s (No geometry output, mode 0)

On Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz (8 processors), the speed are reported as:

Program	WKB speed	WKT speed	No geom speed
fmm	52,890	17,905	58,830
fmm_omp	221,797	77,910	289,031

UBODT precomputation

Statistics of ubodt construction on the network of Netherland (700k nodes and 1 million edges) http://geodata.nationaalgeoregister.nl/nwbwegen/extract/nwbwegen.zip

delta (m)	running time	rows	csv size
1000	3min27.5s	19,022,620	766 MB
3000	4min32.9s	79,998,367	3,2GB

Contact and citation

Can Yang, Ph.D. student at KTH, Royal Institute of Technology in Sweden

Email: cyang(at)kth.se

Homepage: https://people.kth.se/~cyang/

Please cite fmm in your publications if it helps your research:

Can Yang & Győző Gidófalvi (2018) Fast map matching, an algorithm
integrating hidden Markov model with precomputation, International Journal of Geographical Information Science, 32:3, 547-570, DOI: 10.1080/13658816.2017.1400548

Bibtex file

@article{doi:10.1080/13658816.2017.1400548,
author = {Can Yang and Győző Gidófalvi},
title = {Fast map matching, an algorithm integrating hidden Markov model with precomputation},
journal = {International Journal of Geographical Information Science},
volume = {32},
number = {3},
pages = {547-570},
year  = {2018},
publisher = {Taylor & Francis},
doi = {10.1080/13658816.2017.1400548},
URL = { 
        https://doi.org/10.1080/13658816.2017.1400548
},
eprint = { 
        https://doi.org/10.1080/13658816.2017.1400548   
}
}

zhuang-hao-ming / fmm Goto Github PK

fmm's Introduction