Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

• URL: http://arxiv.org/abs/2004.13823v1
• Date: Tue, 21 Apr 2020 02:26:06 GMT
• Title: Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting
• Authors: Xiangpeng Wan, Hakim Ghazzai, and Yehia Massoud
• Abstract summary: We develop an automated traffic alert system based on Natural Language Processing (NLP) We employ the fine-tuning Bidirectional Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. We show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications.
• Score: 1.552282932199974
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern urbanization is demanding smarter technologies to improve a variety of applications in intelligent transportation systems to relieve the increasing amount of vehicular traffic congestion and incidents. Existing incident detection techniques are limited to the use of sensors in the transportation network and hang on human-inputs. Despite of its data abundance, social media is not well-exploited in such context. In this paper, we develop an automated traffic alert system based on Natural Language Processing (NLP) that filters this flood of information and extract important traffic-related bullets. To this end, we employ the fine-tuning Bidirectional Encoder Representations from Transformers (BERT) language embedding model to filter the related traffic information from social media. Then, we apply a question-answering model to extract necessary information characterizing the report event such as its exact location, occurrence time, and nature of the events. We demonstrate the adopted NLP approaches outperform other existing approach and, after effectively training them, we focus on real-world situation and show how the developed approach can, in real-time, extract traffic-related information and automatically convert them into alerts for navigation assistance applications such as navigation apps.

Related papers

• Decentralized Semantic Traffic Control in AVs Using RL and DQN for Dynamic Roadblocks [9.485363025495225]
  We present a novel semantic traffic control system that entrusts semantic encoding responsibilities to the vehicles themselves. This system processes driving decisions obtained from a Reinforcement Learning (RL) agent, streamlining the decision-making process.
  arXiv  Detail & Related papers  (2024-06-26T20:12:48Z)
• Open-TI: Open Traffic Intelligence with Augmented Language Model [23.22301632003752]
  Open-TI is an innovative model targeting the goal of Turing Indistinguishable Traffic Intelligence. It is the first method capable of conducting exhaustive traffic analysis from scratch. Open-TI is able to conduct task-specific embodiment like training and adapting the traffic signal control policies.
  arXiv  Detail & Related papers  (2023-12-30T11:50:11Z)
• Intelligent Traffic Monitoring with Hybrid AI [78.65479854534858]
  We introduce HANS, a neuro-symbolic architecture for multi-modal context understanding. We show how HANS addresses the challenges associated with traffic monitoring while being able to integrate with a wide range of reasoning methods.
  arXiv  Detail & Related papers  (2022-08-31T17:47:22Z)
• Efficient Federated Learning with Spike Neural Networks for Traffic Sign Recognition [70.306089187104]
  We introduce powerful Spike Neural Networks (SNNs) into traffic sign recognition for energy-efficient and fast model training. Numerical results indicate that the proposed federated SNN outperforms traditional federated convolutional neural networks in terms of accuracy, noise immunity, and energy efficiency as well.
  arXiv  Detail & Related papers  (2022-05-28T03:11:48Z)
• Modelling and Reasoning Techniques for Context Aware Computing in Intelligent Transportation System [0.0]
  The amount of raw data generation in Intelligent Transportation System is huge. This raw data are to be processed to infer contextual information. This article aims to study context awareness in the Intelligent Transportation System.
  arXiv  Detail & Related papers  (2021-07-29T23:47:52Z)
• Automated Machine Learning Techniques for Data Streams [91.3755431537592]
  This paper surveys the state-of-the-art open-source AutoML tools, applies them to data collected from streams, and measures how their performance changes over time. The results show that off-the-shelf AutoML tools can provide satisfactory results but in the presence of concept drift, detection or adaptation techniques have to be applied to maintain the predictive accuracy over time.
  arXiv  Detail & Related papers  (2021-06-14T11:42:46Z)
• Online and Adaptive Parking Availability Mapping: An Uncertainty-Aware Active Sensing Approach for Connected Vehicles [1.7259824817932292]
  We propose an online and adaptive scheme for parking availability mapping. Specifically, we adopt an information-seeking active sensing approach to select the incoming data, thus preserving the onboard storage and processing resources. We compare the proposed algorithm with several baselines, which attain inferior performance in terms of mapping convergence speed and adaptivity capabilities.
  arXiv  Detail & Related papers  (2021-05-01T13:35:36Z)
• AI in Smart Cities: Challenges and approaches to enable road vehicle automation and smart traffic control [56.73750387509709]
  SCC ideates on a data-centered society aiming at improving efficiency by automating and optimizing activities and utilities. This paper describes AI perspectives in SCC and gives an overview of AI-based technologies used in traffic to enable road vehicle automation and smart traffic control.
  arXiv  Detail & Related papers  (2021-04-07T14:31:08Z)
• Learning Connectivity for Data Distribution in Robot Teams [96.39864514115136]
  We propose a task-agnostic, decentralized, low-latency method for data distribution in ad-hoc networks using Graph Neural Networks (GNN) Our approach enables multi-agent algorithms based on global state information to function by ensuring it is available at each robot. We train the distributed GNN communication policies via reinforcement learning using the average Age of Information as the reward function and show that it improves training stability compared to task-specific reward functions.
  arXiv  Detail & Related papers  (2021-03-08T21:48:55Z)
• Deep traffic light detection by overlaying synthetic context on arbitrary natural images [49.592798832978296]
  We propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds. It also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state.
  arXiv  Detail & Related papers  (2020-11-07T19:57:22Z)
• Federated Learning in Vehicular Networks [41.89469856322786]
  Federated learning (FL) framework has been introduced as an efficient tool with the goal of reducing transmission overhead. In this paper, we investigate the usage of FL over centralized learning (CL) in vehicular network applications to develop intelligent transportation systems. We identify the major challenges from both learning perspective, i.e., data labeling and model training, and from the communications point of view, i.e., data rate, reliability, transmission overhead, privacy and resource management.
  arXiv  Detail & Related papers  (2020-06-02T06:32:59Z)

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.