Urban Vehicle Mobility Characteristic Mining and Trip Generation Based on Knowledge Graph

• URL: http://arxiv.org/abs/2203.04085v1
• Date: Sat, 19 Feb 2022 04:13:17 GMT
• Title: Urban Vehicle Mobility Characteristic Mining and Trip Generation Based on Knowledge Graph
• Authors: Guilong Li, Yixian Chen, Jun Xie, Qinghai Lin, Zhaocheng He
• Abstract summary: We introduce the knowledge graph for the study to handle individual-level urban vehicle trip big data better. For organization of individual level trip data, we designed and constructed an individual-level trip knowledge graph. We propose an individual-level trip synthesis method based on knowledge graph generation to address the privacy issue of individual-level traffic data.
• Score: 14.032480351768395
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The operation of urban transportation produces massive traffic data, which contains abundant information and is of great significance for the study of intelligent transportation systems. In particular, with the improvement of perception technology, it has become possible to obtain trip data in individual-level of vehicles. It has finer granularity and greater research potential, but at the same time requires higher requirements in terms of data organization and analysis. More importantly it cannot be made public due to privacy issues. To handle individual-level urban vehicle trip big data better, we introduce the knowledge graph for the study. For organization of individual level trip data, we designed and constructed an individual-level trip knowledge graph which greatly improves the efficiency of obtaining data. Then we used the trip knowledge graph as the data engine and designed logical rules to mine the trip characteristics of vehicles by combining the transportation domain knowledge. Finally, we further propose an individual-level trip synthesis method based on knowledge graph generation to address the privacy issue of individual-level traffic data. The experiment shows that the final generated trip data are similar to the historical one in mobility patterns and vehicle associations, and have high spatial continuity.

Related papers

• Extracting Insights from Large-Scale Telematics Data for ITS Applications: Lessons and Recommendations [0.0]
  Transportation planners have previously utilized telematics data in various forms, but its current scale offers significant new opportunities.<n>This paper takes a step towards addressing these needs through four primary objectives.<n>First, a data processing pipeline was built to efficiently analyze 1.4 billion miles (120 million trips) of telematics data collected in Virginia between August 2021 and August 2022.<n>Second, an open data repository of trip and roadway segment level summaries was created.<n>Third, interactive visualization tools were designed to extract insights from these data about trip-taking behavior and the speed profiles of roadways.
  arXiv  Detail & Related papers  (2025-07-18T14:09:40Z)
• Learning to Drive Anywhere with Model-Based Reannotation [49.80796496905606]
  We develop a framework for generalizable visual navigation policies for robots.<n>We leverage passively collected data, including crowd-sourced teleoperation data and unlabeled YouTube videos.<n>This relabeled data is then distilled into LogoNav, a long-horizon navigation policy conditioned on visual goals or GPS waypoints.
  arXiv  Detail & Related papers  (2025-05-08T18:43:39Z)
• Urban Mobility Assessment Using LLMs [19.591156495742922]
  This work proposes an innovative AI-based approach for synthesizing travel surveys by prompting large language models (LLMs) Our study evaluates the effectiveness of this approach across various U.S. metropolitan areas by comparing the results against existing survey data at different levels.
  arXiv  Detail & Related papers  (2024-08-22T19:17:33Z)
• Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction [69.29802752614677]
  RouteFormer is a novel ego-trajectory prediction network combining GPS data, environmental context, and the driver's field-of-view. To tackle data scarcity and enhance diversity, we introduce GEM, a dataset of urban driving scenarios enriched with synchronized driver field-of-view and gaze data.
  arXiv  Detail & Related papers  (2023-12-13T23:06:30Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• A sequential transit network design algorithm with optimal learning under correlated beliefs [4.8951183832371]
  This study proposes an artificial intelligence-driven algorithm that combines sequential transit network design with optimal learning to address the operation under limited data. An operator gradually expands its route system to avoid risks from inconsistency between designed routes and actual travel demand. For validation, a new route system is designed on an artificial network based on public use microdata areas in New York City.
  arXiv  Detail & Related papers  (2023-05-16T14:14:51Z)
• City-scale synthetic individual-level vehicle trip data [5.191606231133951]
  We produce a city-scale synthetic individual-level vehicle trip dataset by generating for each individual based on the historical trip data. The result shows that the synthetic data is consistent with the real data on the aggregated level and reasonable from the individual perspective.
  arXiv  Detail & Related papers  (2022-06-02T08:08:01Z)
• Spatio-Temporal Graph Few-Shot Learning with Cross-City Knowledge Transfer [58.6106391721944]
  Cross-city knowledge has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. We propose a model-agnostic few-shot learning framework for S-temporal graph called ST-GFSL. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-05-27T12:46:52Z)
• Deep Learning based Urban Vehicle Trajectory Analytics [1.3706331473063877]
  This dissertation focuses on the urban vehicle trajectory,' which refers to vehicles in urban traffic networks. The objective of this dissertation is to develop deep-learning based models for urban vehicle trajectory analytics.
  arXiv  Detail & Related papers  (2021-11-15T01:44:18Z)
• An Experimental Urban Case Study with Various Data Sources and a Model for Traffic Estimation [65.28133251370055]
  We organize an experimental campaign with video measurement in an area within the urban network of Zurich, Switzerland. We focus on capturing the traffic state in terms of traffic flow and travel times by ensuring measurements from established thermal cameras. We propose a simple yet efficient Multiple Linear Regression (MLR) model to estimate travel times with fusion of various data sources.
  arXiv  Detail & Related papers  (2021-08-02T08:13:57Z)
• Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers [126.81938540470847]
  We propose Euro-PVI, a dataset of pedestrian and bicyclist trajectories. In this work, we develop a joint inference model that learns an expressive multi-modal shared latent space across agents in the urban scene. We achieve state of the art results on the nuScenes and Euro-PVI datasets demonstrating the importance of capturing interactions between ego-vehicle and pedestrians (bicyclists) for accurate predictions.
  arXiv  Detail & Related papers  (2021-06-22T15:40:21Z)
• End-to-end Interpretable Neural Motion Planner [78.69295676456085]
  We propose a neural motion planner (NMP) for learning to drive autonomously in complex urban scenarios. We design a holistic model that takes as input raw LIDAR data and a HD map and produces interpretable intermediate representations. We demonstrate the effectiveness of our approach in real-world driving data captured in several cities in North America.
  arXiv  Detail & Related papers  (2021-01-17T14:16:12Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Leveraging the Self-Transition Probability of Ordinal Pattern Transition Graph for Transportation Mode Classification [0.0]
  We propose the use of a feature retained from the Ordinal Pattern Transition Graph, called the probability of self-transition for transportation mode classification. The proposed feature presents better accuracy results than Permutation Entropy and Statistical Complexity, even when these two are combined.
  arXiv  Detail & Related papers  (2020-07-16T23:25:09Z)

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.