# Choose A Table: Tensor Dirichlet Process Multinomial Mixture Model with Graphs for Passenger Trajectory Clustering

arXiv: https://arxiv.org/abs/2310.20224 · HTML: https://mlsanity.com/paper/2310.20224/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 35 references that are in the map.
- First posted on arXiv 31 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Jointly Contrastive Representation Learning on Road Network and Trajectory](https://mlsanity.com/paper/2209.06389/) (2022)
- [Tensor Completion for Weakly-dependent Data on Graph for Metro Passenger Flow Prediction](https://mlsanity.com/paper/1912.05693/) (2019)
- [Long-Short Term Spatiotemporal Tensor Prediction for Passenger Flow Profile](https://mlsanity.com/paper/2004.11022/) (2020)
- [Correlated Time Series Self-Supervised Representation Learning via Spatiotemporal Bootstrapping](https://mlsanity.com/paper/2306.06994/) (2023)
- [Adaptive Hierarchical SpatioTemporal Network for Traffic Forecasting](https://mlsanity.com/paper/2306.09386/) (2023)

## Built on by (most cited first, 4 of 4)

- [Skin Cancer Detection utilizing Deep Learning: Classification of Skin Lesion Images using a Vision Transformer](https://mlsanity.com/paper/2407.18554/) (2024)
- [Deep Causal Learning to Explain and Quantify The Geo-Tension's Impact on Natural Gas Market](https://mlsanity.com/paper/2407.10878/) (2024)
- [Transformer-based Drum-level Prediction in a Boiler Plant with Delayed Relations among Multivariates](https://mlsanity.com/paper/2407.11180/) (2024)
- [Exploring Genre and Success Classification through Song Lyrics using DistilBERT: A Fun NLP Venture](https://mlsanity.com/paper/2407.21068/) (2024)

## Abstract (arXiv)

Passenger clustering based on trajectory records is essential for transportation operators. However, existing methods cannot easily cluster the passengers due to the hierarchical structure of the passenger trip information, including multiple trips within each passenger and multi-dimensional information about each trip. Furthermore, existing approaches rely on an accurate specification of the clustering number to start. Finally, existing methods do not consider spatial semantic graphs such as geographical proximity and functional similarity between the locations. In this paper, we propose a novel tensor Dirichlet Process Multinomial Mixture model with graphs, which can preserve the hierarchical structure of the multi-dimensional trip information and cluster them in a unified one-step manner with the ability to determine the number of clusters automatically. The spatial graphs are utilized in community detection to link the semantic neighbors. We further propose a tensor version of Collapsed Gibbs Sampling method with a minimum cluster size requirement. A case study based on Hong Kong metro passenger data is conducted to demonstrate the automatic process of cluster amount evolution and better cluster quality measured by within-cluster compactness and cross-cluster separateness. The code is available at https://github.com/bonaldli/TensorDPMM-G.