# Individual Mobility Prediction via Attentive Marked Temporal Point Processes

arXiv: https://arxiv.org/abs/2109.02715 · HTML: https://mlsanity.com/paper/2109.02715/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 31 references that are in the map.
- First posted on arXiv 6 Sep 2021.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Time2Vec: Learning a Vector Representation of Time](https://mlsanity.com/paper/1907.05321/) (2019)
- [Intensity-Free Learning of Temporal Point Processes](https://mlsanity.com/paper/1909.12127/) (2019)
- [Fully Neural Network based Model for General Temporal Point Processes](https://mlsanity.com/paper/1905.09690/) (2019)
- [A Variational Auto-Encoder Model for Stochastic Point Processes](https://mlsanity.com/paper/1904.03273/) (2019)
- [Understanding metropolitan patterns of daily encounters](https://mlsanity.com/paper/1301.5979/) (2013)
- [Decoupled Learning for Factorial Marked Temporal Point Processes](https://mlsanity.com/paper/1801.06805/) (2018)
- [Incorporating travel behavior regularity into passenger flow forecasting](https://mlsanity.com/paper/2004.00992/) (2020)
- [Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling](https://mlsanity.com/paper/1910.12469/) (2019)
- [Individual Mobility Prediction: An Interpretable Activity-based Hidden Markov Approach](https://mlsanity.com/paper/2101.03996/) (2021)

## Built on by (most cited first, 2 of 2)

- [Spatio-Temporal Graph Neural Point Process for Traffic Congestion Event Prediction](https://mlsanity.com/paper/2311.08635/) (2023)
- [MobEvolve: An Agentic Self-Evolving Heuristic System for Interpretable Human Mobility Generation](https://mlsanity.com/paper/2606.01640/) (2026)

## Abstract (arXiv)

Individual mobility prediction is an essential task for transportation demand management and traffic system operation. There exist a large body of works on modeling location sequence and predicting the next location of users; however, little attention is paid to the prediction of the next trip, which is governed by the strong spatiotemporal dependencies between diverse attributes, including trip start time t, origin o, and destination d. To fill this gap, in this paper we propose a novel point process-based model -- Attentive Marked temporal point processes (AMTPP) -- to model human mobility and predict the whole trip (t,o,d) in a joint manner. To encode the influence of history trips, AMTPP employs the self-attention mechanism with a carefully designed positional embedding to capture the daily/weekly periodicity and regularity in individual travel behavior. Given the unique peaked nature of inter-event time in human behavior, we use an asymmetric log-Laplace mixture distribution to precisely model the distribution of trip start time t. Furthermore, an origin-destination (OD) matrix learning block is developed to model the relationship between every origin and destination pair. Experimental results on two large metro trip datasets demonstrate the superior performance of AMTPP.