Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation

• URL: http://arxiv.org/abs/2402.07369v2
• Date: Wed, 11 Sep 2024 09:06:54 GMT
• Title: Diff-RNTraj: A Structure-aware Diffusion Model for Road Network-constrained Trajectory Generation
• Abstract summary: Trajectory data is essential for various applications as it records the movement of vehicles. Publicly available trajectory datasets remain limited in scale due to privacy concerns. We propose a new problem to meet the practical application need, emphi.e., road network-constrained trajectory generation.
• Score: 18.28750985486491
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and trajectory-based applications. To address this issue, some methods for generating synthetic trajectories have been proposed to expand the scale of the dataset. However, all existing methods generate trajectories in the geographical coordinate system, which poses two limitations for their utilization in practical applications: 1) the inability to ensure that the generated trajectories are constrained on the road. 2) the lack of road-related information. In this paper, we propose a new problem to meet the practical application need, \emph{i.e.}, road network-constrained trajectory (RNTraj) generation, which can directly generate trajectories on the road network with road-related information. RNTraj is a hybrid type of data, in which each point is represented by a discrete road segment and a continuous moving rate. To generate RNTraj, we design a diffusion model called Diff-RNTraj. This model can effectively handle the hybrid RNTraj using a continuous diffusion framework by incorporating a pre-training strategy to embed hybrid RNTraj into continuous representations. During the sampling stage, a RNTraj decoder is designed to map the continuous representation generated by the diffusion model back to the hybrid RNTraj format. Furthermore, Diff-RNTraj introduces a novel loss function to enhance the spatial validity of the generated trajectories. Extensive experiments conducted on two real-world trajectory datasets demonstrate the effectiveness of the proposed model.

Related papers

• TrajDLM: Topology-Aware Block Diffusion Language Model for Trajectory Generation [12.444424009219077]
  TrajDLM is a topology-aware trajectory generation framework based on block diffusion language models.<n>Across three city-scale datasets, TrajDLM achieves strong performance on fine-grained local similarity metrics.<n>Results highlight the effectiveness of block-wise discrete diffusion as a scalable approach to accurate and efficient trajectory generation.
  arXiv  Detail & Related papers  (2026-05-11T05:44:40Z)
• Pathlet Variational Auto-Encoder for Robust Trajectory Generation [16.26294619946259]
  Trajectory generation has recently drawn growing interest in privacy-preserving urban mobility studies and location-based service applications.<n>We propose a deep generative model based on the pathlet representation, which encode trajectories with binary vectors associated with a learned dictionary of trajectory segments.<n>Our model can effectively learn data distribution even using noisy data, achieving relative improvements of $35.4%$ and $26.3%$ over strong baselines on two real-world trajectory datasets.
  arXiv  Detail & Related papers  (2025-11-20T06:57:27Z)
• Unifying Environment Perception and Route Choice Modeling for Trajectory Representation Learning [47.00223863430964]
  Tray Learning (TRL) aims to encode raw trajectories into low-dimensional vectors, which can be leveraged in various downstream tasks, including travel time estimation, location prediction, and trajectory similarity analysis.<n>We propose a framework that unifies comprehensive environment textbfPertemporal explicit textRoute choice modeling for effective textbfPRTrajectory representation learning, dubbed textbfPRTraj.
  arXiv  Detail & Related papers  (2025-10-16T15:55:28Z)
• Learning Spatio-Temporal Dynamics for Trajectory Recovery via Time-Aware Transformer [9.812530969395906]
  In real-world applications, GPS trajectories often suffer from low sampling rates, with large and irregular intervals between consecutive points.<n>This paper addresses the task of map-constrained trajectory recovery, aiming to enhance trajectory sampling rates.
  arXiv  Detail & Related papers  (2025-05-20T03:09:17Z)
• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• Map2Traj: Street Map Piloted Zero-shot Trajectory Generation with Diffusion Model [17.041443813376546]
  We develop a novel zero-shot trajectory generation method, named Map2Traj, by exploiting the diffusion model. With solely the street map of an unobserved area, Map2Traj generates synthetic trajectories that closely resemble the real-world mobility pattern.
  arXiv  Detail & Related papers  (2024-07-29T07:57:03Z)
• Generative Modeling with Phase Stochastic Bridges [49.4474628881673]
  Diffusion models (DMs) represent state-of-the-art generative models for continuous inputs. We introduce a novel generative modeling framework grounded in textbfphase space dynamics Our framework demonstrates the capability to generate realistic data points at an early stage of dynamics propagation.
  arXiv  Detail & Related papers  (2023-10-11T18:38:28Z)
• Continuous Trajectory Generation Based on Two-Stage GAN [50.55181727145379]
  We propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator.
  arXiv  Detail & Related papers  (2023-01-16T09:54:02Z)
• Estimating Link Flows in Road Networks with Synthetic Trajectory Data Generation: Reinforcement Learning-based Approaches [7.369475193451259]
  This paper addresses the problem of estimating link flows in a road network by combining limited traffic volume and vehicle trajectory data. We propose a novel generative modelling framework, where we formulate the link-to-link movements of a vehicle as a sequential decision-making problem. To ensure the generated population vehicle trajectories are consistent with the observed traffic volume and trajectory data, two methods based on Inverse Reinforcement Learning and Constrained Reinforcement Learning are proposed.
  arXiv  Detail & Related papers  (2022-06-26T13:14:52Z)
• Aerial Images Meet Crowdsourced Trajectories: A New Approach to Robust Road Extraction [110.61383502442598]
  We introduce a novel neural network framework termed Cross-Modal Message Propagation Network (CMMPNet) CMMPNet is composed of two deep Auto-Encoders for modality-specific representation learning and a tailor-designed Dual Enhancement Module for cross-modal representation refinement. Experiments on three real-world benchmarks demonstrate the effectiveness of our CMMPNet for robust road extraction.
  arXiv  Detail & Related papers  (2021-11-30T04:30:10Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• Graph-Based Spatial-Temporal Convolutional Network for Vehicle Trajectory Prediction in Autonomous Driving [2.6774008509841005]
  This paper proposes a graph-based spatial-temporal convolutional network ( GSTCN) to predict future trajectory distributions of all neighbor vehicles. The spatial-temporal features are encoded and decoded by a gated recurrent unit (GRU) network to generate future trajectory distributions. Our network is evaluated on two real-world freeway trajectory datasets: I-80 and US-101 in the Next Generation Simulation (NGSIM)
  arXiv  Detail & Related papers  (2021-09-27T02:20:38Z)
• A Deep Learning Framework for Generation and Analysis of Driving Scenario Trajectories [2.908482270923597]
  We propose a unified deep learning framework for the generation and analysis of driving scenario trajectories. We experimentally investigate the performance of the proposed framework on real-world scenario trajectories obtained from in-field data collection.
  arXiv  Detail & Related papers  (2020-07-28T23:33:05Z)
• Risk-Averse MPC via Visual-Inertial Input and Recurrent Networks for Online Collision Avoidance [95.86944752753564]
  We propose an online path planning architecture that extends the model predictive control (MPC) formulation to consider future location uncertainties. Our algorithm combines an object detection pipeline with a recurrent neural network (RNN) which infers the covariance of state estimates. The robustness of our methods is validated on complex quadruped robot dynamics and can be generally applied to most robotic platforms.
  arXiv  Detail & Related papers  (2020-07-28T07:34:30Z)
• PathGAN: Local Path Planning with Attentive Generative Adversarial Networks [0.0]
  We present a model capable of generating plausible paths from egocentric images for autonomous vehicles. Our generative model comprises two neural networks: the feature extraction network (FEN) and path generation network (PGN) We also introduce ETRIDriving, a dataset for autonomous driving in which the recorded sensor data are labeled with discrete high-level driving actions.
  arXiv  Detail & Related papers  (2020-07-08T03:31:58Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.