Modeling human mobility under uncertain conditions and individual preferences remains a difficult and unsolved problem. Data-driven deep learning approaches require extensive trajectory data for training, while more traditional methods often assume deterministic conditions or simple minimum-cost paths. We propose an implicit neural representation (INR) to learn continuous, latent fields of stochastic traffic properties over space and time. We successfully impute speeds on a road network with hundreds of thousands of edges from only a few hundred vehicles, then illustrate the quality of these representations on a trajectory generation task. A near-shortest-path algorithm weighted by the INR’s predictions produces plausible real-world routing choices, showing potential for applications in route planning and anomaly detection.

Human mobility research has significantly benefited from recent advances in machine learning, as have numerous other industries. Aided by the ever-increasing availability of geospatial and mobility data, machine learning models have enabled large-scale systems for simulating city-wide macro and micro mobility behaviors, urban planning, transportation management, and disaster relief optimization. However, while many fields have invested significant effort in solving the model transferability and generalization problem, the inability of machine learning-based human mobility models to generalize to new locations has come to be implicitly accepted in most geospatial research. In this vision paper, we focus on this geospatial generalization problem, its root causes, and how it is restricting the applications of otherwise-promising research. Most importantly, we argue for several data- and modeling-driven innovations which could help remedy this problem, spanning mega-scale simulations, large foundation models, and multi-task, transfer, and meta-learning. We also spotlight a handful of promising ideas which have recently emerged from the community. We hope that these proposals take root and help develop more capable, flexible, and generalizable models in research and industry.

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.

A need to understand and predict vehicles' behavior underlies both public and private goals in the transportation domain, including urban planning and management, ride-sharing services, and intelligent transportation systems. Individuals' preferences and intended destinations vary throughout the day, week, and year: for example, bars are most popular in the evenings, and beaches are most popular in the summer. Despite this principle, we note that recent studies on a popular benchmark dataset from Porto, Portugal have found, at best, only marginal improvements in predictive performance from incorporating temporal information. We propose an approach based on hypernetworks, a variant of meta-learning ("learning to learn") in which a neural network learns to change its own weights in response to an input. In our case, the weights responsible for destination prediction vary with the metadata, in particular the time, of the input trajectory. The time-conditioned weights notably improve the model's error relative to ablation studies and comparable prior work, and we confirm our hypothesis that knowledge of time should improve prediction of a vehicle's intended destination.