Cover Image

A Comprehensive Evaluation Framework for Synthetic Trip Data Generation in Public Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Wu, Yuanyuan, Qin, Zhenlin, Ma, Zhenliang
Format:	Preprint
Published:	2025
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Group Effect Enhanced Generative Adversarial Imitation Learning for Individual Travel Behavior Modeling under Incentives
by: Wu, Yuanyuan, et al.
Published: (2025)

Advancing Retail Data Science: Comprehensive Evaluation of Synthetic Data
by: Xia, Yu, et al.
Published: (2024)

A Comprehensive Survey of Synthetic Tabular Data Generation
by: Shi, Ruxue, et al.
Published: (2025)

Machine Learning for Synthetic Data Generation: A Review
by: Lu, Yingzhou, et al.
Published: (2023)

FEST: A Unified Framework for Evaluating Synthetic Tabular Data
by: Niu, Weijie, et al.
Published: (2025)

FIRE: A Comprehensive Benchmark for Financial Intelligence and Reasoning Evaluation
by: Zhang, Xiyuan, et al.
Published: (2026)

The Causal Round Trip: Generating Authentic Counterfactuals by Eliminating Information Loss
by: Wu, Rui, et al.
Published: (2025)

Defining 'Good': Evaluation Framework for Synthetic Smart Meter Data
by: Chai, Sheng, et al.
Published: (2024)

Comprehensive Exploration of Synthetic Data Generation: A Survey
by: Bauer, André, et al.
Published: (2024)

A Systematic Evaluation of Generative Models on Tabular Transportation Data
by: Wang, Chengen, et al.
Published: (2025)

Masked Clinical Modelling: A Framework for Synthetic and Augmented Survival Data Generation
by: Kuo, Nicholas I-Hsien, et al.
Published: (2024)

Benchmarking Synthetic Tabular Data: A Multi-Dimensional Evaluation Framework
by: Sidorenko, Andrey, et al.
Published: (2025)

A Data-Driven Approach to Enhancing Gravity Models for Trip Demand Prediction
by: Acharya, Kamal, et al.
Published: (2025)

Unsupervised Evaluation of Code LLMs with Round-Trip Correctness
by: Allamanis, Miltiadis, et al.
Published: (2024)

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
by: Maddock, Samuel, et al.
Published: (2025)

An Interpretability-Guided Framework for Responsible Synthetic Data Generation in Emotional Text
by: Martinez, Paula Joy B., et al.
Published: (2025)

Improving the Generation and Evaluation of Synthetic Data for Downstream Medical Causal Inference
by: Amad, Harry, et al.
Published: (2025)

Evaluating Inter-Column Logical Relationships in Synthetic Tabular Data Generation
by: Long, Yunbo, et al.
Published: (2025)

Evaluating Synthetic Tabular Data Generated To Augment Small Sample Datasets
by: Marin, Javier
Published: (2022)

A Multi-Faceted Evaluation Framework for Assessing Synthetic Data Generated by Large Language Models
by: Yuan, Yefeng, et al.
Published: (2024)

SynthEval: A Framework for Detailed Utility and Privacy Evaluation of Tabular Synthetic Data
by: Lautrup, Anton Danholt, et al.
Published: (2024)

Sensitivity, Specificity, and Consistency: A Tripartite Evaluation of Privacy Filters for Synthetic Data Generation
by: Koeken, Adil, et al.
Published: (2025)

Structured Evaluation of Synthetic Tabular Data
by: Yang, Scott Cheng-Hsin, et al.
Published: (2024)

Reasoning-Driven Synthetic Data Generation and Evaluation
by: Davidson, Tim R., et al.
Published: (2026)

Retrieval-Reasoning Large Language Model-based Synthetic Clinical Trial Generation
by: Xu, Zerui, et al.
Published: (2024)

TimePro: Efficient Multivariate Long-term Time Series Forecasting with Variable- and Time-Aware Hyper-state
by: Ma, Xiaowen, et al.
Published: (2025)

Hierarchical Synthetic Tabular Data Generation: A Hybrid Top-Down and Bottom-Up Framework
by: Nie, Junfeng, et al.
Published: (2026)

A Unified Framework for Generative Data Augmentation: A Comprehensive Survey
by: Chen, Yunhao, et al.
Published: (2023)

SynQP: A Framework and Metrics for Evaluating the Quality and Privacy Risk of Synthetic Data
by: Hu, Bing, et al.
Published: (2026)

Utility Theory of Synthetic Data Generation
by: Xu, Shirong, et al.
Published: (2023)

TabularARGN: A Flexible and Efficient Auto-Regressive Framework for Generating High-Fidelity Synthetic Data
by: Tiwald, Paul, et al.
Published: (2025)

Advancements in Recommender Systems: A Comprehensive Analysis Based on Data, Algorithms, and Evaluation
by: Ma, Xin, et al.
Published: (2024)

TAEGAN: Generating Synthetic Tabular Data For Data Augmentation
by: Li, Jiayu, et al.
Published: (2024)

A Reinforcement Learning Approach to Synthetic Data Generation
by: Espinosa-Dice, Natalia, et al.
Published: (2025)

TripCast: Pre-training of Masked 2D Transformers for Trip Time Series Forecasting
by: Liao, Yuhua, et al.
Published: (2024)

Synthetic Flight Data Generation Using Generative Models
by: Aly, Karim, et al.
Published: (2026)

Debiasing Synthetic Data Generated by Deep Generative Models
by: Decruyenaere, Alexander, et al.
Published: (2024)

Matrix: Peer-to-Peer Multi-Agent Synthetic Data Generation Framework
by: Wang, Dong, et al.
Published: (2025)

A Comprehensive Framework for Evaluating API-oriented Code Generation in Large Language Models
by: Wu, Yixi, et al.
Published: (2024)

Harlequin: Color-driven Generation of Synthetic Data for Referring Expression Comprehension
by: Parolari, Luca, et al.
Published: (2024)