Learning Citywide Patterns of Life from Trajectory Monitoring

TL;DR

This paper introduces a biologically-inspired neural network approach to analyze and extract patterns of human mobility from GPS data streams, enabling anomaly detection and insights into citywide transportation behaviors.

Contribution

It adapts Grow When Required (GWR) neural network for real-time, unsupervised pattern learning and anomaly detection in geospatial mobility data, extending its application to urban transportation analysis.

Findings

Identified new transportation anomalies like festivals and concerts.

Successfully applied to Porto taxi dataset for pattern discovery.

Demonstrated incremental learning of normal and abnormal mobility behaviors.

Abstract

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.