7,624 research outputs found

    Leveraging Personal Navigation Assistant Systems Using Automated Social Media Traffic Reporting

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    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.Comment: This paper is accepted for publication in IEEE Technology Engineering Management Society International Conference (TEMSCON'20), Metro Detroit, Michigan (USA

    DxNAT - Deep Neural Networks for Explaining Non-Recurring Traffic Congestion

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    Non-recurring traffic congestion is caused by temporary disruptions, such as accidents, sports games, adverse weather, etc. We use data related to real-time traffic speed, jam factors (a traffic congestion indicator), and events collected over a year from Nashville, TN to train a multi-layered deep neural network. The traffic dataset contains over 900 million data records. The network is thereafter used to classify the real-time data and identify anomalous operations. Compared with traditional approaches of using statistical or machine learning techniques, our model reaches an accuracy of 98.73 percent when identifying traffic congestion caused by football games. Our approach first encodes the traffic across a region as a scaled image. After that the image data from different timestamps is fused with event- and time-related data. Then a crossover operator is used as a data augmentation method to generate training datasets with more balanced classes. Finally, we use the receiver operating characteristic (ROC) analysis to tune the sensitivity of the classifier. We present the analysis of the training time and the inference time separately

    Machine learning for early detection of traffic congestion using public transport traffic data

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    The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte és proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trànsit urbà
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