Welcome to GoTrackIt’s documentation!

Note

The English document has not yet been fully translated…

Read UserManual in ：Chinese

Author: Tang Kai

Email：tangkai@zhechengdata.com、794568794@qq.com

Project source code：GitHub — Current version v0.3.10

GoTrackIt is a Map-Matching python package based on the Hidden Markov Model.

It matches the vehicle’s GPS trajectory data to the road network through probabilistic graph modeling, and obtains the standardized spatiotemporal trajectory of the vehicle.It can effectively support travel navigation, traffic monitoring, traffic management, carbon emission accounting, traffic modeling and other directions.

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The application areas of Map-Matching are as follows：

• Travel navigation

  General Navigation、Lane Level Navigation、Traffic behavior warning

• Traffic monitoring

  Real-time traffic calculation、Intersection delay calculation、Real-time public transportation、Traffic monitoring

• Traffic management

  Traffic tracing analysis、Travel behavior analysis、Traffic congestion analysis

• Carbon emission accounting

  Emissions accounting、Road network emission characteristics

• Traffic modeling and other directions

  Road network integration、Bus/road network matching

Note

This project is in a period of frequent upgrades and iterations. The current version is v0.3.10. Please update in time.

Note

Due to the differences in computers of different users, it is difficult to unify the CRS output format in Geopandas. Starting from v0.3.5, the CRS check for geographic vector files has been completely removed. Users need to ensure that the CRS of the input geometry vector layer is EPSG:4326.

Document Content

• 💡Introduction
  • 1. About gotrackit
  • 2. Map matching problem
  • 3. Related video tutorials
• 🚀 How to use
  • 1. Install gotrackit
    • 1.1. Pre-dependency libraries
    • 1.2. Install gotrackit
  • 2. Features Overview of gotrackit
    • 2.1. Overview of modules
    • 2.2. Data requirements
      • 2.2.1. Road network data
        • (1) Road network-node layer
        • (2) Road network - link layer
        • (3) Relationship between node layer and link layer
      • 2.2.2. GPS positioning data
  • 3. Road network module
    • 3.1. Road network optimization
      • 3.1.4. id remapping
      • 3.1.5. Reshaping of road network sections and nodes
      • 3.1.6. Handling loops
  • 4. GPS data production
    • 4.1. TripGeneration initialization parameters
    • 4.2. generate_rand_trips related parameters
  • 5. Trajectory data preprocessing
    • 5.1. Trip segmentation
      • 5.1.1. How to understand the main process and sub-process?
      • 5.1.2. Initialize GpsPreProcess related parameters
      • 5.1.3. Class method trip_segmentations related parameters
    • 5.2. Track data cleaning
      • 5.2.2. Track point densification
      • 5.2.3. Track point frequency reduction
      • 5.2.4. Sliding window smoothing
      • 5.2.5. Offline Kalman filter smoothing
      • 5.2.7. Trajectory cleaning and visualization
  • 6. Offline map matching
    • 6.1. Required data
    • 6.2. Matching interface parameter explanation - parameters for building Net
      • 6.2.1. Road network parameters
      • 6.2.2. Truncation search parameters
      • 6.2.3. Path pre-calculation parameters
      • 6.2.4. Hierarchical spatial index parameters
      • 6.2.5. Plane projection system parameters
    • 6.3. Matching interface parameter explanation - MapMatch parameter explanation
      • 6.3.2. Basic parameters (must be specified)
      • 6.3.3. Time column construction parameters
      • 6.3.4. Candidate range parameters
      • 6.3.5. Emission probability and state transition probability parameters
      • 6.3.6. GPS preprocessing parameters - Dwell point processing
      • 6.3.7. GPS preprocessing parameters - point frequency reduction
      • 6.3.8. GPS preprocessing parameters - sliding window average
      • 6.3.9. GPS preprocessing parameters - point density
      • 6.3.10. Emission Probability Correction - Heading Angle Correction
      • 6.3.11. Result output setting parameters
      • 6.3.12. Visualization output parameters
      • 6.3.13. Grid parameter search settings
      • 6.3.14. execute - Execute matching parameters
    • 6.4. Conventional matching code example
      • 6.4.1. Match result table (match_res) field meaning
      • 6.4.2. Meaning of warning information and error information
    • 6.5. Accelerate matching - enable pre-calculation
    • 6.6. Accelerated Matching - Enable Multi-core for Parallel Matching
    • 6.7. Accelerated matching - simplified road network line-geo
    • 6.8. Accelerate matching - use hierarchical index to accelerate spatial association efficiency
    • 6.9. Use grid parameters to determine reasonable matching parameters
    • 6.10. Visualization of matching results
      • 6.10.1 HTML animation visualization
      • 6.10.2 geojson vector file visualization
    • 6.11. Parameter adjustment method for incorrect matching results
      • 6.11.1. Program prompts - less than two GPS points after preprocessing, unable to match
      • 6.11.2. The matching path is not continuous in the html visualization result
  • 7. Real-time map matching
    • 7.1. The difference between real-time and offline
    • 7.2. Real-time Kalman filter
    • 7.3. Real-time matching interface
• ⚡️ QuickStart
  • 1. Map Matching Example
    • 1.1. install gotrackit
    • 1.2. data download
    • 1.3. Match code 1 - non-sparse gps data
    • 1.4. Match code 2 - sparse gps data
    • 1.5. Result output and visualization
      • 1.5.1. Comparison and visualization of trajectory before and after preprocessing
      • 1.5.2. Matching result visualization
• 🔎ClassMethod
  • 1. NetReverse类
    • 1.1. NetReverse类初始化
      • NetReverse
      • (1) 第一组（总体信息）
      • (2) 第二组（路径解析、拆分参数）
      • (3) 第三组（拓扑关联参数）
      • (4) 第四组（拓扑优化参数）
      • (5) 第五组（重叠路段处理参数）
      • (6) 第六组（联通性修复参数）
      • (7) 第七组（分区逆向参数）
    • 1.2. 初始化中三个并行参数的区别
      • (1) multi_core_parse
      • (2) multi_core_merge
      • (3) multi_core_reverse
    • 1.3. NetReverse类方法
      • (1) generate_net_from_request
        • NetReverse.generate_net_from_request
      • (2) generate_net_from_pickle
        • NetReverse.generate_net_from_pickle
      • (3) create_node_from_link
        • NetReverse.create_node_from_link
      • (4) topology_optimization
        • NetReverse.topology_optimization
      • (5) modify_conn
        • NetReverse.modify_conn
      • (4) redivide_link_node
        • NetReverse.redivide_link_node
• 🖥️Iteration record
  • 1. v0.1.1
  • 2. v0.1.3
  • 3. v0.1.5
  • 4. v0.1.6
  • 5. v0.1.7
  • 6. v0.1.8
  • 7. v0.1.9
  • 8. v0.2.0
  • 9. v0.2.1
  • 10. v0.2.2
  • 11. v0.2.3
  • 12. v0.2.4
  • 13. v0.2.7
  • 14. v0.2.9
  • 15. v0.3.0
  • 16. v0.3.1
  • 17. v0.3.3
  • 18. v0.3.5
  • 19. v0.3.6
  • 19. v0.3.7
  • 20. v0.3.8
  • 21. v0.3.10