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.
This paper distills a complex travel mode choice model into simpler, interpretable models.
Alt-GNNs improve travel mode choice modeling by integrating graph neural networks with GEV models.
Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional …
Recent years have witnessed an increased focus on interpretability and the use of machine learning to inform policy analysis and decision making. This paper applies machine learning to examine travel behavior and, in particular, on modeling changes in travel modes when individuals are presented with a novel (on-demand)…
In this paper, we implement an information-theoretic approach to travel behaviour analysis by introducing a generative modelling framework to identify informative latent characteristics in travel decision making. It involves developing a joint tri-partite Bayesian graphical network model using a Restricted Boltzmann Ma…
Study improves choice model accuracy and heterogeneity representation using mixture models.
Over the last few years, traffic data has been exploding and the transportation discipline has entered the era of big data. It brings out new opportunities for doing data-driven analysis, but it also challenges traditional analytic methods. This paper proposes a new Divide and Combine based approach to do K means clust…
Graph neural nets improve discrete choice modeling with network effects.
Simultaneously estimates travel times and route choice model parameters.
Semi-supervised Generative Adversarial Networks (GANs) are developed in the context of travel mode inference with uni-dimensional smartphone trajectory data. We use data from a large-scale smartphone travel survey in Montreal, Canada. We convert GPS trajectories into fixed-sized segments with five channels (variables).…
Accurate and reliable travel time predictions in public transport networks are essential for delivering an attractive service that is able to compete with other modes of transport in urban areas. The traditional application of this information, where arrival and departure predictions are displayed on digital boards, is…
Study highlights fairness issues in travel behavior prediction models.
Study improves cross-modal bike-share and transit demand prediction.
We address two shortcomings in online travel time estimation methods for congested urban traffic. The first shortcoming is related to the determination of the number of mixture modes, which can change dynamically, within day and from day to day. The second shortcoming is the wide-spread use of Gaussian probability dens…
We develop ensemble Convolutional Neural Networks (CNNs) to classify the transportation mode of trip data collected as part of a large-scale smartphone travel survey in Montreal, Canada. Our proposed ensemble library is composed of a series of CNN models with different hyper-parameter values and CNN architectures. In o…
Identifying the distribution of users' transportation modes is an essential part of travel demand analysis and transportation planning. With the advent of ubiquitous GPS-enabled devices (e.g., a smartphone), a cost-effective approach for inferring commuters' mobility mode(s) is to leverage their GPS trajectories. A maj…
Travel decisions tend to exhibit sensitivity to uncertainty and information processing constraints. These behavioural conditions can be characterized by a generative learning process. We propose a data-driven generative model version of rational inattention theory to emulate these behavioural representations. We outlin…
Revisits PPO design choices, exposing failure modes and proposing alternatives.
This study develops methods to coordinate travel routes to reduce congestion.
CTGAN synthesizes population data for travel behavior simulation.
Whereas deep neural network (DNN) is increasingly applied to choice analysis, it is challenging to reconcile domain-specific behavioral knowledge with generic-purpose DNN, to improve DNN's interpretability and predictive power, and to identify effective regularization methods for specific tasks. This study designs a pa…
A large GPS dataset reveals that a single route often covers 60% of travel observations.
New model combines neural networks and embeddings for better choice modeling interpretability.
The emergence of data-driven demand analysis has led to the increased use of generative modelling to learn the probabilistic dependencies between random variables. Although their apparent use has mostly been limited to image recognition and classification in recent years, generative machine learning algorithms can be a…
This work proposes a new feature for transportation mode classification using GPS trajectories.
Study traveling waves in hyperbolic space for Fisher-KPP equations.
This paper analyzes consumer choices over lunchtime restaurants using data from a sample of several thousand anonymous mobile phone users in the San Francisco Bay Area. The data is used to identify users' approximate typical morning location, as well as their choices of lunchtime restaurants. We build a model where res…
New methods show quasinormal modes can be defined using various stationary Killing vectors.
Travel providers such as airlines and on-line travel agents are becoming more and more interested in understanding how passengers choose among alternative itineraries when searching for flights. This knowledge helps them better display and adapt their offer, taking into account market conditions and customer needs. Som…
Encouraging sustainable mobility patterns is at the forefront of policymaking at all scales of governance as the collective consciousness surrounding climate change continues to expand. Not every community, however, possesses the necessary economic or socio-cultural capital to encourage modal shifts away from private m…
The Euclidean traveller explores various geometric spaces, seeing different places in each.
Kernel Dynamic Mode Decomposition reconstructs dynamical systems using Laplacian kernel.
Route Choice Models predict the route choices of travelers traversing an urban area. Most of the route choice models link route characteristics of alternative routes to those chosen by the drivers. The models play an important role in prediction of traffic levels on different routes and thus assist in development of ef…
Bayesian framework predicts post-disruption travel times in metro networks.
The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in machine learning and statistics. Since HMC uses the gradient information of the target distribution, it can explore the state space much mor…
Empirical mode modeling improves state-space analysis of noisy data.
This study uses Twitter to analyze traveler behavior in Manhattan.
Method estimates travel times on urban roads using Uber data.
New proofs confirm travel time data determine simple metrics on a disc.
Smartphone data shows promise but accuracy issues remain.
The paper provides robustness guarantees for mode estimation in bandits.
Method recovers obstacles from travel times on curved surfaces.
Paper addresses travel time tomography stability and statistical inversion.
Financial derivatives based on road travel times for hedging and pricing.
Recover simple irreversible Finsler geometry from travel time data
Billiard trajectories in curved spaces have predictable travel times.
Same travelling times imply identical obstacles in Riemannian manifolds.
Study uses neural networks to predict travel times for public transportation.