derekwtian / LHMM

LHMM: A Learning Enhanced HMM Model for Cellular Trajectory Map Matching

This is a partial implementation for paper LHMM: A Learning Enhanced HMM Model for Cellular Trajectory Map-matching

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Overview

1. Requirements
2. Execution
3. Map-matching-Example
4. Dataset
5. License
6. Contact

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1. Requirements

The following modules are required.

• Ubuntu 16.04
• Python >=3.5 (Anaconda3 recommended)
• PyTorch 0.4 (virtualenv recommended)
• Cuda 9.0

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2. Execution

2.1 HMM Map-matching

Due to privacy protocols, we cannot render the Map-matching part. We try our best to provide the interface of the learned observation and transition probabilities. Moreover, a simple HMM framework is also provided in src/pymatch.py.

2.2 Training

$ cd pygcn
$ python gcn_train.py
$ cd trans
$ python trans_train.py

2.3 Serve

$ python gcnServer_old.py
$ python transServer.py

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3. Map-matching-Example

We provide some figures to illustrate the matching of LHMM in director Mapmatching-Figures.

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4. Dataset

We used real trajectory data. Unfortunately, due to privacy protection, we cannot provide the dataset for testing.

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5. License

The code is developed under the MPL-02.0 license.

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6. Contact

If you have any questions or require further clarification, please do not hesitate to send an email to us (E-mail address：[e-mail])