Deep Learning based Urban Vehicle Trajectory Analytics

• URL: http://arxiv.org/abs/2111.07489v1
• Date: Mon, 15 Nov 2021 01:44:18 GMT
• Title: Deep Learning based Urban Vehicle Trajectory Analytics
• Authors: Seongjin Choi
• Abstract summary: 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.
• Score: 1.3706331473063877
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: A `trajectory' refers to a trace generated by a moving object in geographical spaces, usually represented by of a series of chronologically ordered points, where each point consists of a geo-spatial coordinate set and a timestamp. Rapid advancements in location sensing and wireless communication technology enabled us to collect and store a massive amount of trajectory data. As a result, many researchers use trajectory data to analyze mobility of various moving objects. In this dissertation, we focus on the `urban vehicle trajectory,' which refers to trajectories of vehicles in urban traffic networks, and we focus on `urban vehicle trajectory analytics.' The urban vehicle trajectory analytics offers unprecedented opportunities to understand vehicle movement patterns in urban traffic networks including both user-centric travel experiences and system-wide spatiotemporal patterns. The spatiotemporal features of urban vehicle trajectory data are structurally correlated with each other, and consequently, many previous researchers used various methods to understand this structure. Especially, deep-learning models are getting attentions of many researchers due to its powerful function approximation and feature representation abilities. As a result, the objective of this dissertation is to develop deep-learning based models for urban vehicle trajectory analytics to better understand the mobility patterns of urban traffic networks. Particularly, this dissertation focuses on two research topics, which has high necessity, importance and applicability: Next Location Prediction, and Synthetic Trajectory Generation. In this study, we propose various novel models for urban vehicle trajectory analytics using deep learning.

Related papers

• VeTraSS: Vehicle Trajectory Similarity Search Through Graph Modeling and Representation Learning [9.325787573209201]
  Trajectory similarity search plays an essential role in autonomous driving. We propose VeTraSS -- an end-to-end pipeline for Vehicle Trajectory Similarity Search.
  arXiv  Detail & Related papers  (2024-04-11T06:19:55Z)
• Deep Learning for Trajectory Data Management and Mining: A Survey and Beyond [58.63558696061679]
  Trajectory computing is crucial in various practical applications such as location services, urban traffic, and public safety. We present a review of development and recent advances in deep learning for trajectory computing (DL4Traj) Notably, we encapsulate recent advancements in Large Language Models (LLMs) that hold potential to augment trajectory computing.
  arXiv  Detail & Related papers  (2024-03-21T05:57:27Z)
• Clustering Dynamics for Improved Speed Prediction Deriving from Topographical GPS Registrations [0.0]
  We propose solutions for speed prediction using sparse GPS data points and their associated topographical and road design features. Our goal is to investigate whether we can use similarities in the terrain and infrastructure to train a machine learning model that can predict speed in regions where we lack transportation data.
  arXiv  Detail & Related papers  (2024-02-12T09:28:16Z)
• RSG-Net: Towards Rich Sematic Relationship Prediction for Intelligent Vehicle in Complex Environments [72.04891523115535]
  We propose RSG-Net (Road Scene Graph Net): a graph convolutional network designed to predict potential semantic relationships from object proposals. The experimental results indicate that this network, trained on Road Scene Graph dataset, could efficiently predict potential semantic relationships among objects around the ego-vehicle.
  arXiv  Detail & Related papers  (2022-07-16T12:40:17Z)
• Predicting Future Occupancy Grids in Dynamic Environment with Spatio-Temporal Learning [63.25627328308978]
  We propose a-temporal prediction network pipeline to generate future occupancy predictions. Compared to current SOTA, our approach predicts occupancy for a longer horizon of 3 seconds. We publicly release our grid occupancy dataset based on nulis to support further research.
  arXiv  Detail & Related papers  (2022-05-06T13:45:32Z)
• 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)
• Predicting Vehicles Trajectories in Urban Scenarios with Transformer Networks and Augmented Information [0.0]
  This paper exploits simple structures for predicting pedestrian trajectories, based on Transformer Networks. We adapt their use to the problem of vehicle trajectory prediction in urban scenarios in prediction horizons up to 5 seconds. Our model achieves state-of-the-art results and proves to be flexible and adaptable to different types of urban contexts.
  arXiv  Detail & Related papers  (2021-06-01T15:18:55Z)
• Spatial-Temporal Dual Graph Neural Networks for Travel Time Estimation [5.614908141182951]
  We propose a graph-based deep learning framework for travel time estimation, namely Spatial-Temporal Dual Graph Neural Networks (STDGNN) We first establish the spatial-temporal dual graph architecture to capture the complex correlations of both intersections and road segments. In order to capture the joint spatial-temporal dynamics of the intersections and road segments, we adopt the spatial-temporal learning layer.
  arXiv  Detail & Related papers  (2021-05-28T05:15:45Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)
• TrajGAIL: Generating Urban Vehicle Trajectories using Generative Adversarial Imitation Learning [9.01310450044549]
  This research suggests a generative modeling approach to learn the underlying distributions of urban vehicle trajectory data. A generative model for urban vehicle trajectories can better generalize from training data by learning the underlying distribution of the training data. TrajGAIL is a generative adversarial imitation learning framework for the urban vehicle trajectory generation.
  arXiv  Detail & Related papers  (2020-07-28T13:17:51Z)
• 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.