New method clusters travel behavior data from 1990-2017.
problem Challenges in analyzing large-scale travel data.
method Divide and Combine K-means clustering on time series data.
result Activity-travel patterns can be grouped into three clusters.
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48 results for “Travel behavior”
Deriving the Traveler Behavior Information from Social Media: A Case Study in Manhattan with Twittercs.SI
This study uses Twitter to analyze traveler behavior in Manhattan.
problem Analyzing traveler behavior using social media data.
method Systematic method to extract displacement information from geo-tagged tweets.
result Twitter reveals unique demographics and travel behavior patterns.
Study highlights fairness issues in travel behavior prediction models.
problem Ethical challenges in travel behavior analysis using machine learning.
method Operationalized computational fairness by equality of opportunity; compared DNN and DCM; introduced absolute correlation regularization.
result Both DNN and DCM predict disparities across social groups, with DNN outperforming DCM in prediction disparities.
This paper distills a complex travel mode choice model into simpler, interpretable models.
problem Lack of interpretability in complex machine learning models for travel behavior.
method Model distillation combined with market segmentation.
result Generated interpretable models that closely match the predictions of the original complex model.
This paper compares machine learning and logit models for predicting travel mode choice.
problem Comparing interpretability of machine learning and logit models for travel mode choice.
method Empirical evaluation of machine learning and logit models on stated preference survey data.
result Machine learning produces significantly higher predictive accuracy than logit models.
Paper uses machine learning to model travel mode switching under a new transit system.
problem Modeling individual travel mode preferences and response to new mobility options.
method Interpretable machine learning approach to predict and interpret mode-switching behavior.
result Machine learning captures individual heterogeneity in travel mode choice.
Study shows curves converge to traveling waves under specific conditions.
problem Global stability of traveling waves for area-preserving curvature flow.
method Area-preserving curvature flow with contact angle condition.
result Moving curves converge to traveling waves starting from embedded convex curves.
Bayesian calibration improves ABMs for predicting travel patterns.
problem Calibrating ABMs for accurate travel pattern predictions.
method Gaussian Process emulator with deep learning dimensionality reduction for high-dimensional, non-stationary data.
result Improved accuracy in predicting travel patterns using traffic flow data.
Bayesian framework predicts post-disruption travel times in metro networks.
problem Uncertainty in post-disruption travel times in metro networks.
method Bayesian spatiotemporal modeling framework capturing train interactions and non-Gaussian distributional characteristics.
result The proposed models consistently outperform baseline specifications in point prediction and uncertainty quantification.
Generative models help analyze complex travel behavior data.
problem Analyzing large scale multiple discrete-continuous travel behavior data.
method Proposed a bi-partite generative model framework using restricted Boltzmann machines.
result The model accurately represents the joint distribution of MDC data and performs better than discriminative methods.
Bayesian model identifies three types of travelers adapting to feedback.
problem Capturing adaptive, feedback-driven travel behavior in heterogeneous individuals.
method Latent Class Reinforcement Learning (LCRL) model with Variational Bayes estimation.
result Three distinct traveler classes identified: context-dependent, persistent exploitative, and exploratory.
Information processing constraints in travel behaviour modelling: A generative learning approachecon.EM
Generative model captures how individuals process travel information under uncertainty.
problem Travel decisions are sensitive to uncertainty and information processing constraints.
method Data-driven generative learning model based on rational inattention theory.
result Generative model demonstrates strong correlation with rational inattention theory.
DeepIST uses CNNs to estimate travel time from path images.
problem Accurately estimating travel time for a path in urban transportation systems.
method Proposes DeepIST, a neural network framework that converts paths into generalized images and uses PathCNN and 1D CNN to capture spatial and temporal patterns.
result DeepIST significantly outperforms existing models in travel time estimation.
Alt-GNNs improve travel mode choice modeling by integrating graph neural networks with GEV models.
problem Capturing alternative dependence in discrete choice models with predefined, symmetric, and uniform dependence.
method Introducing Alternative Graph Neural Networks (Alt-GNNs) that embed alternative dependence within a unified framework.
result Alt-GNNs significantly improve predictive performance over benchmark models in travel mode choice datasets.
Model for corporate bond pricing with credit rating migration, solving a double free boundary problem.
problem Corporate bond pricing with credit rating migration risks.
method Established a pricing model as a double free boundary problem, proving existence, uniqueness, and regularity of the solution.
result Two free boundaries are shown to be smooth and converge to a traveling wave solution as time goes to infinity.
Composite Travel Generative Adversarial Networks for Tabular and Sequential Population Synthesiscs.LG
CTGAN synthesizes population data for travel behavior simulation.
problem Synthesizing population data for agent-based transportation modeling.
method Composite Travel Generative Adversarial Network (CTGAN).
result Consistent and accurate generation of synthetic populations with tabular and sequential mobility data.
Uncertainty Quantification of Spatiotemporal Travel Demand with Probabilistic Graph Neural Networkscs.LG
Prob-GNN quantifies travel demand uncertainty with deep learning.
problem Uncertainty in travel demand prediction.
method Probabilistic Graph Neural Networks (Prob-GNN) framework.
result Probabilistic assumptions significantly impact uncertainty prediction.
Deep RL for dynamic pricing of express lanes considers multiple origins, destinations, and access locations.
problem Dynamic pricing of express lanes with multiple access points and traveler heterogeneity.
method Formulated as a POMDP, uses policy gradient methods and neural networks to determine stochastic tolls.
result Deep RL outperforms traditional methods in maximizing revenue and minimizing travel time.
Model predicts missing boarding stops in smart card data.
problem Missing boarding stop information in public transport datasets.
method Supervised machine learning with ordinal classification.
result Proposed method significantly outperforms existing imputation methods.
Study traveling waves in hyperbolic space for Fisher-KPP equations.
problem Understanding wave behavior in hyperbolic space for Fisher-KPP equations.
method Analyzes the Cauchy problem in hyperbolic space for heat equation with Fisher-KPP forcing term.
result Proves new results on the dichotomy of solution propagation or vanishing based on diffusion and reaction strength.
The Euclidean traveller explores various geometric spaces, seeing different places in each.
problem Understanding the Euclidean traveller's itinerary in different geometric spaces.
method Analyzing the geometric spaces and the traveller's paths.
result The traveller sees a variety of places in different geometric spaces, including tori, closed hyperbolic surfaces, and manifolds.
Survey visual analytics methods for detecting anomalous user behaviors.
problem Understanding and detecting anomalous user behaviors in various domains.
method Survey and classification of visual analytics methods in four categories.
result Discussion of findings and potential research directions.
Agent-based simulation assesses tradable credit schemes for congestion reduction.
problem Simplistic modeling of TCS impacts in transportation research.
method Agent- and activity-based simulation framework within SimMobility.
result TCS stabilizes network and market performance over time, reducing congestion.
Improved risk assessment for UBI using telematics data and AdaBoost.
problem Class imbalance in predicting claims frequency for UBI.
method Cost-sensitive multi-class AdaBoost (SAMME.C2) algorithm.
result SAMME.C2 outperforms other models in handling class imbalances.
Method estimates travel times on urban roads using Uber data.
problem Estimating travel times on urban roads where data is scarce.
method Graph representation, trip sampling, least-squares optimization.
result Estimates travel times on arterial roads using aggregated Uber data.
Smartphone data shows promise but accuracy issues remain.
problem Inaccurate travel surveys from smartphone data.
method Training algorithms on data quality and validating labels.
result Machine learning methods are limited by data quality.
New proofs confirm travel time data determine simple metrics on a disc.
problem Determining a simple Riemannian metric from travel time data.
method Proofs based on Myers-Steenrod theorem, Lipschitz-type stability estimate.
result Travel time data determine a simple Riemannian metric on a disc up to natural gauge.
Method recovers obstacles from travel times on curved surfaces.
problem Recovering obstacles from travel times on curved surfaces.
method Extending Noakes and Stoyanov's method to Riemannian surfaces with curvature constraints.
result Obstacles can be recovered from travel times on Riemannian surfaces under certain curvature conditions.
Paper addresses travel time tomography stability and statistical inversion.
problem Determining conformal factors of metrics from geodesic lengths.
method Established forward and inverse stability estimates; applied to Bayesian statistical inversion.
result Consistency of statistical inversion technique for travel time tomography.
Financial derivatives based on road travel times for hedging and pricing.
problem Market risk in crypto and banking sectors.
method Modeling travel time data with CARMA models and applying risk-neutral pricing.
result Derivatives pricing based on travel time and its volatility.
Recover simple irreversible Finsler geometry from travel time data
problem Stable recovery of a simple irreversible Finsler geometry
method Use a Gromov-Hausdorff distance adapted to irreversible metric spaces
result Unique and Lipschitz-stable recovery
This paper presents a bus travel time prediction system using deep neural networks.
problem Accurate travel time predictions for urban buses to compete with other modes of transport.
method Multi-output, multi-time-step deep neural network combining convolutional and LSTM layers.
result The proposed model significantly outperforms other methods and detects small irregular peaks quickly.
Billiard trajectories in curved spaces have predictable travel times.
problem Understanding travel times in billiard trajectories on curved surfaces.
method Analyzing geodesic flows and sectional curvature to prove time-preserving conjugacy.
result Billiard trajectories with almost identical obstacles have identical shapes.
Same travelling times imply identical obstacles in Riemannian manifolds.
problem Determining if two sets of obstacles are identical based on travel times.
method Analyzing curvature conditions and geodesic intersections.
result Disjoint convex obstacles with identical travel times are identical.
Study uses neural networks to predict travel times for public transportation.
problem Inaccurate travel time predictions due to road traffic irregularities.
method Developed two neural network models (MLP and LSTM) using OD travel time matrix.
result Both models can make near-accurate predictions, but LSTM is more susceptible to noise.
We address the problem of inverse reinforcement learning in Markov decision processes where the agent is risk-sensitive. In particular, we model risk-sensitivity in a reinforcement learning framework by making use of models of human decision-making having their origins in behavioral psychology, behavioral economics, an…
In building intelligent transportation systems such as taxi or rideshare services, accurate prediction of travel time and distance is crucial for customer experience and resource management. Using the NYC taxi dataset, which contains taxi trips data collected from GPS-enabled taxis [23], this paper investigates the use…
Study improves choice model accuracy and heterogeneity representation using mixture models.
problem Improving prediction accuracy and heterogeneity representation in choice models.
method Semi-nonparametric Latent Class Choice Model with mixture models and EM algorithm.
result Mixture models enhance prediction accuracy and heterogeneity representation without sacrificing interpretability.
Study examines active travel in Chicago communities, revealing mixed perceptions.
problem Transport disadvantage and lack of active mobility in underserved communities.
method Focus groups, qualitative discourse analysis, quantitative text-mining (topic modeling, sentiment analysis).
result Residents view active travel as both necessity and symbol of privilege, influenced by local culture.
Study shows stability of travel time data reconstruction from closed subsets.
problem Reconstruction of length spaces from travel time data on a closed subset.
method Lipschitz stability proof for certain types of length spaces.
result Reconstruction of length spaces is Lipschitz stable from travel time data on a closed subset.
Study analyzes Airbnb booking lead times during global crises using a new metric.
problem Disruptions in booking behaviors during global crises affect forecasting accuracy.
method Normalized L1 (Manhattan) distance to assess lead time divergences.
result Identified two-phase disruption: abrupt change at pandemic onset followed by partial recovery.
TRIPDECODER: Study Travel Time Attributes and Route Preferences of Metro Systems from Smart Card Dataphysics.soc-ph
TripDecoder recovers metro routes and travel times from smart card data.
problem Recover unknown routes and travel times in metro systems.
method Decouples two inference tasks: travel time and route preference.
result TripDecoder improves accuracy and efficiency compared to competitors.
STAD improves travel time estimation by learning from real traffic data.
problem Travel time estimation using GPS traces is inaccurate and requires offline optimization.
method STAD uses machine learning and real-time trip data to adjust travel time estimates.
result STAD reduces median absolute errors by 14% in Doha and New York City, and 29% in Porto.
Microlocal Methods for The Elastic Travel Time Tomography Problem for Transversely Isotropic Mediamath.AP
Study travel time tomography for transversely isotropic media using modified pseudodifferential calculus.
problem Travel time tomography problem for transversely isotropic media.
method Modified scattering pseudodifferential calculus to solve the tomography problem.
result Construction and use of modified pseudodifferential calculus to solve the tomography problem.
Semi-supervised GANs improve travel mode inference from GPS data.
problem Travel mode inference from GPS trajectories.
method Developed semi-supervised GANs and compared them with CNNs on a large-scale smartphone dataset.
result Best semi-supervised GAN model achieved 83.4% prediction accuracy.
We review and organize some results describing the behavior of a Teichmüller geodesic and draw several applications: 1) We show that Teichmüller geodesics do not back track. 2) We show that a Teichmüller geodesic segment whose endpoints are in the thick part has the fellow travelling property. This fails when the endpo…
Model predicts travel time under rare conditions using a vector-space model.
problem Predicting travel time under rare temporal conditions (e.g., holidays, school vacations) is challenging due to limited historical data and other temporal changes.
method Presented a vector-space model for encoding rare temporal conditions, allowing coherent representation learning across different conditions.
result Increased performance for travel time prediction over different baselines when using the vector-space encoding for representing the temporal setting.
Enhanced travel time prediction using deep neural networks and road network information.
problem Improving travel time estimation using deep learning models.
method Proposes incorporating road network information into deep learning models for travel time prediction.
result Improved travel time prediction, especially with limited training data.