# Learning Citywide Patterns of Life from Trajectory Monitoring

arXiv: https://arxiv.org/abs/2206.15352 · HTML: https://mlsanity.com/paper/2206.15352/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 102 references that are in the map.
- First posted on arXiv 30 Jun 2022.

## Builds on (most cited first, 18 of 18)

- [Overcoming catastrophic forgetting in neural networks](https://mlsanity.com/paper/1612.00796/) (2016)
- [Learning without Forgetting](https://mlsanity.com/paper/1606.09282/) (2016)
- [Progressive Neural Networks](https://mlsanity.com/paper/1606.04671/) (2016)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [Lifelong Learning with Dynamically Expandable Networks](https://mlsanity.com/paper/1708.01547/) (2017)
- [Selective Experience Replay for Lifelong Learning](https://mlsanity.com/paper/1802.10269/) (2018)
- [Imbalanced Continual Learning with Partitioning Reservoir Sampling](https://mlsanity.com/paper/2009.03632/) (2020)
- [Lifelong Learning of Spatiotemporal Representations with Dual-Memory Recurrent Self-Organization](https://mlsanity.com/paper/1805.10966/) (2018)
- [Scalable Recollections for Continual Lifelong Learning](https://mlsanity.com/paper/1711.06761/) (2017)
- [Neurogenesis Deep Learning](https://mlsanity.com/paper/1612.03770/) (2016)
- [Artificial Neural Networks Applied to Taxi Destination Prediction](https://mlsanity.com/paper/1508.00021/) (2015)
- [DeepIST: Deep Image-based Spatio-Temporal Network for Travel Time Estimation](https://mlsanity.com/paper/1909.05637/) (2019)
- [Travel Time Estimation without Road Networks: An Urban Morphological Layout Representation Approach](https://mlsanity.com/paper/1907.03381/) (2019)
- [Destination Prediction Based on Partial Trajectory Data](https://mlsanity.com/paper/2004.07473/) (2020)
- [STAD: Spatio-Temporal Adjustment of Traffic-Oblivious Travel-Time Estimation](https://mlsanity.com/paper/2006.09892/) (2020)
- [Map Enhanced Route Travel Time Prediction using Deep Neural Networks](https://mlsanity.com/paper/1911.02623/) (2019)
- [Continual Learning from Synthetic Data for a Humanoid Exercise Robot](https://mlsanity.com/paper/2102.10034/) (2021)
- [A self-organizing neural network architecture for learning human-object interactions](https://mlsanity.com/paper/1710.01916/) (2017)

## Built on by (most cited first, 1 of 1)

- [Continual Learning for Smart City: A Survey](https://mlsanity.com/paper/2404.00983/) (2024)

## Abstract (arXiv)

The recent proliferation of real-world human mobility datasets has catalyzed geospatial and transportation research in trajectory prediction, demand forecasting, travel time estimation, and anomaly detection. However, these datasets also enable, more broadly, a descriptive analysis of intricate systems of human mobility. We formally define patterns of life analysis as a natural, explainable extension of online unsupervised anomaly detection, where we not only monitor a data stream for anomalies but also explicitly extract normal patterns over time. To learn patterns of life, we adapt Grow When Required (GWR) episodic memory from research in computational biology and neurorobotics to a new domain of geospatial analysis. This biologically-inspired neural network, related to self-organizing maps (SOM), constructs a set of "memories" or prototype traffic patterns incrementally as it iterates over the GPS stream. It then compares each new observation to its prior experiences, inducing an online, unsupervised clustering and anomaly detection on the data. We mine patterns-of-interest from the Porto taxi dataset, including both major public holidays and newly-discovered transportation anomalies, such as festivals and concerts which, to our knowledge, have not been previously acknowledged or reported in prior work. We anticipate that the capability to incrementally learn normal and abnormal road transportation behavior will be useful in many domains, including smart cities, autonomous vehicles, and urban planning and management.