Graph-based Representation for Identifying Individual Travel Activities with Spatiotemporal Trajectories and POI Data
This repository provides the implementation of the following paper:
Graph-based Representation for Identifying Individual Travel Activities with Spatiotemporal Trajectories and POI Data
Xinyi Liu (Data Science, Zoox. Inc.), Meiliu Wu (Spatial Computing and Data Mining Lab, UW-Madison),
Bo Peng (Ping An Labs, PAII), and Qunying Huang* (Spatial Computing and Data Mining Lab, UW-Madison)
(* indicates corresponding author.)
Abstract: Individual daily travel activities (e.g., work, eating) are identified with various machine learning models (e.g., Bayesian Network, Random Forest) for understanding people’s frequent travel purposes. However, labor-intensive engineering work is often required to extract effective features. Additionally, features and models are mostly calibrated for individual trajectories with regular daily travel routines and patterns, and therefore suffer from poor generalizability when applied to new trajectories with more irregular patterns. Meanwhile, most existing models cannot extract features to explicitly represent regular travel activity sequences. Therefore, this paper proposes a graph-based representation of spatiotemporal trajectories and point-of-interest (POI) data for travel activity type identification, defined as Gstp2Vec. Specifically, a weighted directed graph is constructed by connecting regular activity areas (i.e., zones) detected via clustering individual daily travel trajectories as graph nodes, with edges denoting trips between pairs of zones. Statistics of trajectories (e.g., visit frequency, activity duration) and POI distributions (e.g., percentage of restaurants) at each activity zone are encoded as node features. Next, trip frequency, average trip duration, and average trip distance are encoded as edge weights. Then a series of feedforward neural networks are trained to generate low-dimensional embeddings for activity nodes through sampling and aggregating spatiotemporal and POI features from their multihop neighborhoods. Activity type labels collected via travel surveys are used as ground truth for backpropagation. The experiment results with real-world GPS trajectories show that Gstp2Vec significantly reduces feature engineering efforts by automatically learning feature embeddings from raw trajectories with minimal prepossessing efforts. It not only enhances model generalizability to receive higher identification accuracy on test individual trajectories with diverse travel patterns, but also obtains better efficiency and robustness. In particular, our identification of the most common daily travel activities (e.g., Dwelling and Work) for people with diverse travel patterns outperforms state-of-the-art classification models.
Clone this repository.
git clone https://github.com/XinyiHolly/Gstp2Vec-TravelActivityClassification.git
cd Gstp2Vec-TravelActivityClassification
pip install -r requirements.txt
We used two datasets in our paper:
- OSM landuse and POI datasets
- GPS trajectory data (available from the corresponding author on a reasonable request)
- processing.py generates:
- activityzone.csv, which contains activity zones with their spatiotemporal and POI properties
- graph.csv, which contains graph edges with their origin and destination nodes
- Gstp2VecDemo.ipynb demostrates:
- generation of input representations (i.e., t, s, p)
- creation of graph-based representation model based on weighted bi-directional GraphSAGE
- supervised learning:
- build and run the representation learning model under a two-split setup (i.e., training-test split)
- build and run the representation learning model under a three-split setup (i.e., training-validation-test split)
- semi-supervised learning:
- build and run the representation learning model under a three-split setup (i.e., training-validation-test split)