A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Paper uses DeePC to improve urban traffic lights, reducing congestion and emissions.
Understanding urban dynamics, i.e., how the types and intensity of urban residents' activities in the city change along with time, is of urgent demand for building an efficient and livable city. Nonetheless, this is challenging due to the expanding urban population and the complicated spatial distribution of residents.…
Existing inefficient traffic light control causes numerous problems, such as long delay and waste of energy. To improve efficiency, taking real-time traffic information as an input and dynamically adjusting the traffic light duration accordingly is a must. In terms of how to dynamically adjust traffic signals' duration…
Model predicts urban population using mobile data traffic.
RFN models urban mobility demand by separating temporal and spatial variability.
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
Which area in NYC is the most similar to Lower East Side? What about the NoHo Arts District in Los Angeles? Traditionally this task utilizes information about the type of places located within the areas and some popularity/quality metric. We take a different approach. In particular, urban dwellers' time-variant mobilit…
Mobile and ubiquitous sensing of urban air quality has received increased attention as an economically and operationally viable means to survey atmospheric environment with high spatial-temporal resolution. This paper proposes a machine learning based mobile air pollution sensing framework, called Deep-MAPS, and demons…
Paper infers human mobility from sparse trajectories.
Proposes FairDRL-ST for fair spatio-temporal mobility prediction.
This is a brief survey of the research performed by Grandata Labs in collaboration with numerous academic groups around the world on the topic of human mobility. A driving theme in these projects is to use and improve Data Science techniques to understand mobility, as it can be observed through the lens of mobile phone…
New models predict mobility flows as well as complex machine learning but are simpler and interpretable.
VisitHGNN predicts visit probabilities between neighborhoods and POIs using graph neural networks.
In this study, we present a machine learning approach to infer the worker and student mobility flows on daily basis from static censuses. The rapid urbanization has made the estimation of the human mobility flows a critical task for transportation and urban planners. The primary objective of this paper is to complete i…
Generative model learns vehicle trajectory distributions for better data generalization.
NCPF model improves traffic data imputation with neural and tensor methods.
Graph embeddings from commute networks identify socioeconomic disparities in urban areas.
Urban spatial-temporal flows prediction is of great importance to traffic management, land use, public safety, etc. Urban flows are affected by several complex and dynamic factors, such as patterns of human activities, weather, events and holidays. Datasets evaluated the flows come from various sources in different dom…
We introduce the first unified theory for target tracking using Multiple Hypothesis Tracking, Topological Data Analysis, and machine learning. Our string of innovations are 1) robust topological features are used to encode behavioral information, 2) statistical models are fitted to distributions over these topological …
Recent years have witnessed the world-wide emergence of mega-metropolises with incredibly huge populations. Understanding residents mobility patterns, or urban dynamics, thus becomes crucial for building modern smart cities. In this paper, we propose a Neighbor-Regularized and context-aware Non-negative Tensor Factoriz…
Understanding the spatiotemporal distribution of people within a city is crucial to many planning applications. Obtaining data to create required knowledge, currently involves costly survey methods. At the same time ubiquitous mobile sensors from personal GPS devices to mobile phones are collecting massive amounts of d…
Methodology to analyze traffic accidents using microscopic models.
Human mobility has a significant impact on several layers of society, from infrastructural planning and economics to the spread of diseases and crime. Representing the system as a complex network, in which nodes are assigned to regions (e.g., a city) and links indicate the flow of people between two of them, physics-in…
Predicts user next location using CDR data.
The paper predicts travel times using tree-based ensembles.
Paper uses DMD to embed time in spatiotemporal forecasting.
Cultural activity is an inherent aspect of urban life and the success of a modern city is largely determined by its capacity to offer generous cultural entertainment to its citizens. To this end, the optimal allocation of cultural establishments and related resources across urban regions becomes of vital importance, as…
Graphs model human mobility patterns, reducing errors in data matching.
Agent-based simulation assesses tradable credit schemes for congestion reduction.
Deep learning agent improves pedestrian navigation in urban environments.
DETECT clusters mobility behaviors from trajectories using deep learning.
City2City translates place representations across cities using language translation techniques.
This paper introduces a new Urban Point Cloud Dataset for Automatic Segmentation and Classification acquired by Mobile Laser Scanning (MLS). We describe how the dataset is obtained from acquisition to post-processing and labeling. This dataset can be used to learn classification algorithm, however, given that a great a…
In the last decade, the digital age has sharply redefined the way we study human behavior. With the advancement of data storage and sensing technologies, electronic records now encompass a diverse spectrum of human activity, ranging from location data, phone and email communication to Twitter activity and open-source c…
Paper optimizes urban navigation with deep learning models.
Study examines ridesourcing patterns in Chicago using K-prototypes segmentation.
Active learning reduces simulation needs for high-fidelity mobility maps.
Paper presents a simple method for accurate user localization in urban areas.
New method interprets complex models for music and urban simulations.
This paper presents a novel context-based approach for pedestrian motion prediction in crowded, urban intersections, with the additional flexibility of prediction in similar, but new, environments. Previously, Chen et. al. combined Markovian-based and clustering-based approaches to learn motion primitives in a grid-bas…
With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of compu…
Deep Recurrent Q-Network improves autonomous driving in urban areas with pedestrians.
Identifies directed graphs from node measurements using polynomial filters.
Mobility datasets are fundamental for evaluating algorithms pertaining to geographic information systems and facilitating experimental reproducibility. But privacy implications restrict sharing such datasets, as even aggregated location-data is vulnerable to membership inference attacks. Current synthetic mobility data…
MIP framework improves urban flow prediction by adapting to distribution shifts.
SUMO provides unbiased log marginal likelihood estimation for latent variable models.
Automated road infrastructure mapping using connected vehicle data and deep learning.
Model predicts parking area states up to 60 mins ahead.