The study infers passenger types from commute data.
problem Classifying commuters based on travel patterns.
method Constructing eigentravel matrices and using gradient boosting for prediction.
result Gradient boosting method achieves 76% accuracy in passenger type prediction.
Neural networks predict airport passenger behavior using WiFi traces.
problem Predicting airport passenger activity choices inside the terminal.
method Three neural network architectures: FNN, LSTM, and their combination. Inputs include static and dynamic passenger data. Real-world case study at Bologna Airport.
result LSTM approach, especially with short prediction horizons, outperforms FNN.
STG2Seq predicts multi-step passenger demand with graph and hierarchical structure.
problem Predicting passenger demand over multiple time horizons is challenging due to nonlinear and dynamic spatial-temporal dependencies.
method Proposes a graph-based model with a hierarchical graph convolutional structure to capture spatial and temporal correlations.
result Consistently outperforms baseline and state-of-the-art models on real-world datasets.
Proposes a new model for clustering passenger trajectories with graphs.
problem Hierarchical trip structure, inaccurate clustering number, and lack of spatial semantic graphs.
method Tensor Dirichlet Process Multinomial Mixture model with graphs and a tensor version of Collapsed Gibbs Sampling.
result Automatic determination of the number of clusters and better cluster quality.
Proposes a new model for clustering passenger trips considering hierarchical and multi-dimensional data.
problem Clustering passenger trips with hierarchical and multi-dimensional data, especially in large-scale transportation systems.
method Tensor Dirichlet Process Multinomial Mixture (Tensor-DPMM) model, incorporating Dirichlet Process for automatic cluster number determination and tensor representation for multi-mode data.
result Automatic determination of the number of clusters and improved clustering quality.
Paper predicts in-situ metro passenger density using smart card data.
problem Crowd management in metro systems.
method Statistical models and EM algorithm for time-dependent OD matrix and travel time cost estimation.
result Accurate prediction of in-situ passenger density for future time points.
Study uses Markov models to predict train delays in India's largest rail network.
problem Systemic delays in train arrivals causing passenger inconvenience.
method Two regression-based models applied to two years of train running-status data.
result Efficient algorithm for estimating delays with near accurate results.
Optimizes e-hailing drivers' passenger seeking to reduce congestion and pollution.
problem Reduces congestion and pollution by optimizing e-hailing drivers' passenger seeking.
method Uses Markov Decision Process (MDP) and imitation learning to model and optimize drivers' decisions.
result Achieves a 17.5% improvement in passenger return rate over a heuristic strategy.
Generative Adversarial Networks create synthetic PNRs for airline industry.
problem Creating realistic synthetic Passenger Name Records without infringing data ownership laws.
method Cramér GANs, categorical feature embedding, Cross-Net architecture.
result Generated PNRs match real data well and can be used for business applications.
Paper proposes a method for predicting future passenger flow in urban transportation.
problem Predicting future passenger flow in urban transportation development.
method Multi-view localized correlation learning method with adaptive-weight.
result Our method achieves excellent performance compared with other baselines.
Model predicts passenger origin-destination for online taxi-hailing systems.
problem Predicting passenger origin-destination for efficient transportation planning.
method K-means clustering, non-negative matrix factorization, stacked recurrent neural network.
result Proposed model reduces MAPE by 5-7% for 1-hour windows and 14% for 30-minute windows.
New model predicts airline passenger itinerary choices using Pointer Networks.
problem Modeling air passenger choices of flight itineraries.
method Pointer Networks combining Recurrent Neural Networks and Attention Mechanism.
result Pointer Networks model outperforms traditional MNL model on metrics.
Trans-Sense uses smartphones to predict public transit wait times and schedules.
problem Traffic congestion and lack of public transportation in developing countries.
method Crowdsourced mobile phones to estimate waiting times and transit schedules.
result Achieves high accuracy in predicting passenger arrival times and station dimensions.
Improved car-hailing service by analyzing POI effects.
problem Minimize passenger waiting time and optimize vehicle utilization.
method Analyzed POI effects on supply-demand gap and proposed a POI selection scheme integrated with XGBoost.
result More accurate and stable estimation results.
New method improves tensor completion for weakly-dependent spatiotemporal data.
problem Improving tensor completion for weakly-dependent data on graphs.
method Introducing L1-norm and Graph Laplacian penalties for low-rank tensor decomposition and completion. result Improved performance in metro passenger flow prediction.
Model trains passing events on a bridge using multilevel Gaussian process.
problem Represent aggregate train-passing events from a bridge monitoring system.
method Formulate a combined model with low-rank approximation hierarchical Gaussian process, incorporating domain expertise as constraints.
result Allow for simulation of previously unobserved train types.
Develops a method to predict system behavior under disruptions.
problem Predicting changes in system behavior due to external perturbations.
method Counterfactual distribution regression for structured inference.
result Generalizes behavior predictions from natural to disrupted states.
A new method improves ridesharing efficiency using QMIX.
problem Improving ridesharing dispatch efficiency with complex environments.
method QMIX for centralized training with decentralized execution.
result QMIX outperforms IDQN in various scenarios.
This research optimizes plate structures to reduce vibrations in vehicles and aircraft.
problem Minimizing structural vibrations in engineering systems for improved passenger comfort.
method Guided flow matching design optimization integrating generative flow matching and surrogate model.
result Generated plate designs with reduced vibrations compared to random search and other methods.
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.
Gaussian processes are rich distributions over functions, which provide a Bayesian nonparametric approach to smoothing and interpolation. We introduce simple closed form kernels that can be used with Gaussian processes to discover patterns and enable extrapolation. These kernels are derived by modelling a spectral dens…
Paper compares RNNs for short-term traffic flow prediction.
problem Accurate short-term traffic flow prediction for smart cities.
method Used three types of RNNs (simple RNN, GRU, LSTM) and compared with other models.
result Simple RNNs and GRU outperform LSTM in accuracy and training time.
MOVI learns optimal vehicle dispatch policies without models, reducing unserviced requests.
problem Optimizing vehicle dispatch to minimize passenger wait times in dynamic fleets.
method Model-free approach using Deep Q-network (DQN) for decentralized learning and centralized receding-horizon control comparison.
result DQN dispatch policy reduces unserviced requests by 76% compared to no dispatch and 20% compared to RHC.
This paper optimizes driver repositioning using MARL and reward design for better service and traffic management.
problem Unserved passenger requests due to drivers' cruising behavior during passenger seeking.
method Mean field multi-agent reinforcement learning (MARL) with a reward design scheme and Bayesian optimization (BO) to solve bilevel optimization problems.
result Optimal toll charges and service charges can improve platform and city planner objectives by significant margins, leading to better traffic conditions.
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.
Proposes a compensation mechanism for improving individual forecast confidence.
problem Difficult to assess the quality of individual probabilistic forecasts and their utilities.
method Compensation mechanism based on fair bets and online learning.
result The proposed mechanism cannot be exploited and ensures forecasted utility matches actual utility.
This paper evaluates various bus arrival time prediction models.
problem Improving prediction accuracy of bus arrival times.
method General evaluation framework for various models, including raw data pre-processing.
result Preliminary results show strengths and weaknesses of common models.
A risk-sensitive approach to inverse reinforcement learning.
problem Risk-sensitive reinforcement learning in Markov decision processes.
method Gradient-based algorithm minimizing a loss function defined on observed behavior.
result Demonstrated performance on Grid World and ride-sharing examples.
Modeling vessel speed to balance efficiency and environmental risks in Arctic shipping.
problem Balancing vessel speed with environmental and ecological risks in Arctic shipping.
method Inverse control constrained optimization framework with risk parameters estimated from AIS data.
result Distinct decision-making patterns across vessel types and navigational statuses, with varying sensitivity to ice and whale risks.
New algorithms for high-dimensional HMMs reduce complexity by discarding non-local factors.
problem High-dimensional HMMs are computationally expensive to filter and smooth.
method Approximate filtering and smoothing via locality in factor graphs, avoiding exponential cost.
result Error bounds in local total variation norm are dimension-free, improving scalability.
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.
Adaptive multiple kernel learning predicts railway points failures.
problem Predicting failures of railway points to minimize negative effects.
method Formulated as multiple kernel learning problem, robust algorithm considers missing data and point variance.
result Superior performance compared to state-of-the-art methods.
Aims to learn optimal behavior from ranked experts in MDPs.
problem Learning optimal behavior without a specified reward function from ranked experts.
method Uses ordinal regression to find a reward function maximizing the margin between ranks.
result Shows important differences in reward function hidden from existing algorithms.
Enhanced kernel framework for advanced data forecasting.
problem Complex mechanical behaviors and timefrequency dynamics in aircraft systems.
method Frequency-aware surrogate modeling with SMT kernels, extending kernel types and incorporating derivatives.
result Improved accuracy in forecasting CO2 concentrations and airline passenger traffic.
Physics-driven regularization improves DNNs for engineering tasks.
problem Training deep neural networks for engineering systems using incomplete data.
method Regularization using governing laws to penalize divergence from physical laws.
result Proposed method yields more interpretable and accurate DNN models.
Paper tackles ride-sharing user experience enhancement with weakly supervised learning.
problem Compound weakly supervised learning problem in ride-sharing comment data.
method CWSL method with instance reweighting, robust criteria, and alternating optimization.
result Effectiveness validated on Didi ride-sharing comment data.
Study improves cross-modal bike-share and transit demand prediction.
problem Cross-modal ripple effects in urban transportation demand.
method Transfer learning and stacked LSTM models for cross-modal demand prediction.
result Transfer learning models outperform unimodal models in cross-modal demand prediction.
Paper examines how dimension reduction affects aircraft load prediction.
problem Efficiently predicting aircraft loads for derivative models.
method Compared PCA, polynomial fitting, and combined techniques on regression trees.
result AdaBoost with Random Forest offers promising results for load estimation.
This article presents results from the first statistically significant study of traffic forecasts in transportation infrastructure projects. The sample used is the largest of its kind, covering 210 projects in 14 nations worth US$59 billion. The study shows with very high statistical significance that forecasters gener…
Automated vehicles learn to predict upcoming maneuvers with high accuracy.
problem Making self-driving cars feel safer by anticipating future actions.
method Machine learning techniques applied to a large dataset of real-world driving.
result Automated vehicles can predict maneuvers up to 5 seconds in advance with high accuracy.
Paper proposes a statistical approach for predicting lane changes in highway scenarios.
problem Early recognition of lane changes for automated driving.
method Prototype trajectories generated from real data using Agglomerative Hierarchical Clustering. Maneuver prediction via Boosted Decision Trees and mixture model.
result Improved performance for lane change and trajectory prediction compared to a reference approach.
Facial landmark localization and occlusion estimation for driver safety.
problem Robust facial landmark localization and occlusion estimation under harsh lighting and occlusion.
method Occluded Stacked Hourglass approach based on Stacked Hourglass network.
result State-of-the-art results in face detection, head pose, and occlusion estimation on various datasets.
AI learns to adapt driving behavior based on human interest.
problem Adapting AI behavior to individual human preferences for trust and comfort.
method Hybrid brain-computer interface (hBCI) detects human interest, which is used to adapt AI behavior in a virtual vehicle.
result AI agent slows down when passengers encounter objects of interest, increasing viewing time for subjects.
A framework combining HSMM and survival analysis for lifecycle-oriented mobility analysis.
problem Understanding individual metro usage dynamics over multi-year horizons.
method A state-based lifecycle modeling framework integrating HSMM and discrete-time survival analysis.
result Identification of interpretable mobility states, transition dynamics, and state-dependent exit and re-entry processes.
Paper combines multiple ETA models into a stacked ensemble for better ETA predictions.
problem Improving ETA predictions for taxi schedules and trips.
method Developed a two-level stacked ensemble model and applied XAI methods to explain it.
result The stacked ensemble model outperforms previous ETA approaches.
Paper tackles edge computing for reliable ITS data analytics.
problem Real-time data analytics for ITSs with low latency and reliability.
method Edge-centric architecture with deep learning for heterogeneous data.
result Preliminary results show reliable, secure ITS environment.
A new network learns to prioritize messages for efficient multi-robot path planning.
problem Efficient path planning and coordination for large-scale multi-robot systems.
method Message-Aware Graph Attention Network (MAGAT) incorporating attention mechanisms.
result MAGAT achieves performance close to a coupled centralized expert algorithm.
ATFM predicts traffic flow using attention mechanism and neural networks.
problem Predicting traffic flow with diverse factors integration.
method Unified neural network (ATFM) with attention mechanism and ConvLSTM units.
result ATFM outperforms in predicting citywide short-term/long-term traffic flow.