Robust Natural Language Processing for Urban Trip Planning

Booth, Joel; Eugenio, Barbara Di; Cruz, Isabel F.; Wolfson, Ouri

doi:10.1080/08839514.2015.1082280

Cited by 14 publications

(3 citation statements)

References 46 publications

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“…Mobile Information Systems such as automatic speech recognition (ASR), natural language understanding (NLU), and natural language generation (NLG) [13,14]. ASR is the process of translating human speech into texts.…”

Section: Related Workmentioning

confidence: 99%

An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing

Jwo

Lin

Lee

2021

Mobile Information Systems

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In the era of Industry 4.0, manufacturing sites are becoming more sophisticated and connected with the aid of Information Technology (IT) and Industrial Internet of Things (IIoT) infrastructure. What makes a smart manufacturing enterprise as opposed to a traditional one is the ability to solve existing problems and predict issues to fix them before they occur while creating advantaged value. Dashboards, assisting decision-makers in business markets, can organize data from machines, sensors, and workers into a real-time visual representation. They provide a quick overview of how your entire business is operating and where you stand in relation to the key performance indicators for supporting the development toward more resource-efficient and sustainable processes. However, if users need more information related to the indicators shown on the dashboard, current approaches mainly rely on human effort to search and retrieve the information. The lack of interaction between the dashboard and users often results in inconvenient operations and limits the usage of the tool, especially in the manufacturing industry. In this study, an interaction design is developed to resolve the issue. The proposed dashboard is developed by a three-layer structure including data layer, processing layer, and presentation layer. A virtual assistant is introduced to play the role of a mediator between the dashboard and the user. It provides users with a natural language interface to communicate, directly interacts with the dashboard on users’ behalf, and actively reminds relevant personnel to expedite data collection. Compared with the traditional human-machine interface, the adoption of this lightweight and easy-to-use design can not only create a frictionless and intuitive experience but also ensure that data is displayed in a timely manner.

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Section: Related Workmentioning

confidence: 99%

An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing

Jwo

Lin

Lee

2021

Mobile Information Systems

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“…As NLP models have improved, the classification accuracy and flexibility of models that leverage social media data in ITS have improved in lockstep. The authors of [45] presented a natural language interface for trip planning in complex multi-modal urban transportation networks, which provides robust understanding of complex requests with the aim of giving users flexibility in their language. The model's main shortcomings, however, stemmed from a lack of flexibility and limited training data.…”

Section: Related Workmentioning

confidence: 99%

Empowering Real-Time Traffic Reporting Systems With NLP-Processed Social Media Data

Wan

Lucic

Ghazzai

et al. 2020

IEEE Open J. Intell. Transp. Syst.

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Current urbanization trends are leading to heightened demand of smarter technologies to facilitate a variety of applications in intelligent transportation systems. Automated crowdsensing constitutes a strong base for ITS applications by providing novel and rich data streams regarding congestion tracking and real-time navigation. Along with these wellleveraged data streams, drivers and passengers tend to report traffic information to social media platforms. Despite their abundance, the use of social media data in ITS has gained more and more attention as of now. In this paper, we develop an automated Natural Language Processing (NLP)-based framework to empower and complement traffic reporting solutions by text mining social media, extracting desired information, and generating alerts and warning for drivers. We employ the finetuned Bidirectional Encoder Representations from Transformers classification model to filer and classify data. Then, we apply the Question-Answering model to extract necessary information characterizing the reported incident such as its location, occurrence time, and nature of the incidents. Afterwards, we convert the collected information into alerts to be integrated into personal navigation assistants. Finally, we compare the recently posted incident reports from both official authorities and social media in order to provide more complete incident pictures and suggest some open research directions.

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“…This effect has led to the number of AI/ML papers to the TRB Annual Meeting to skyrocket from 5 in 2015 to 162 in 2020 (1). Most of the AI/ML work is concentrated on navigation applications, advanced driver assistance, and smart traffic systems (2)(3)(4)(5)(6)(7). A common technology used for these applications, deep learning, is often used to detect defects in the manufacture of vehicles and vehicle components (7,8).…”

mentioning

confidence: 99%

SmarTxT: A Natural Language Processing Approach for Efficient Vehicle Defect Investigation

Francis¹,

Cates²,

Caldiera³

2022

Transportation Research Record: Journal of the Transportation R

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The investigation of vehicle defects, which is generally led by the National Highway Traffic Safety Administration (NHTSA) in the U.S., is critical to the continued trust of the general public in the safety of vehicles. NHTSA routinely receives millions of reports of potential defects, complaints, recalls, and manufacturer communications, which may provide evidence of a new vehicle defect. However, the large quantity and text-based communication make efficiently identifying defect trends difficult for analysts. To accelerate the investigation of defect reports, we introduce a natural language processing (NLP) application that identifies key topics and similar defect reports to assist analysts and investigators. Further, our application is built to provide users with a web interface for interacting with the NLP models. The integration of NLP with current NHTSA datasets provides a method for quickly identifying defect trends in large text-based datasets. To demonstrate the effectiveness of our method, we apply our approach to two publicly available NHTSA datasets, namely the Technical Service Bulletins and Recalls Dataset.

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Robust Natural Language Processing for Urban Trip Planning

Cited by 14 publications

References 46 publications

An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing

An Interactive Dashboard Using a Virtual Assistant for Visualizing Smart Manufacturing

Empowering Real-Time Traffic Reporting Systems With NLP-Processed Social Media Data

SmarTxT: A Natural Language Processing Approach for Efficient Vehicle Defect Investigation

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