Predicting Human Mobility via Attentive Convolutional Network

Miao, Congcong; Luo, Ziyan; Zeng, Fengzhu; Wang, Jilong

doi:10.1145/3336191.3371846

Cited by 15 publications

(5 citation statements)

References 35 publications

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“…Technology enables greater interaction between citizens and governments, cultivating collaboration and engagement in urban policy planning. In addition, the aim is to develop technological solutions that improve the quality of life of citizens, such as mobile applications for transport management, health, or safety [10].…”

Section: Background Of Smart Citiesmentioning

confidence: 99%

“…DATA MINING: The process of discovering hidden patterns, trends, and relationships in large amounts of data [65]. It uses techniques and algorithms to analyse large data sets so that valuable information can be discovered to make strategic decisions and improve an organisation's performance [10,40,52,65].…”

Section: Analysis Of the Structure And Evolution Of Recommender Syste...mentioning

confidence: 99%

“…Applications used worldwide, such as Uber, Lyft, or Citymapper, allow users to find public transport routes, book shared rides, or even rent bicycles or electric scooters. These apps help reduce traffic congestion and promote more sustainable mobility in cities [1,10,56,80].…”

Section: Areas Of Application For Recommender Systems In Smart Citiesmentioning

confidence: 99%

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Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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Smart cities represent the convergence of information and communication technologies (ICT) with urban management to improve the quality of life of city dwellers. In this context, recommender systems, tools that offer personalised suggestions to city dwellers, have emerged as key contributors to this convergence. Their successful application in various areas of city life and their ability to process massive amounts of data generated in urban environments has expedited their status as a crucial technology in the evolution of city planning. Our methodology included reviewing the Web of Science database, resulting in 130 articles that, filtered for relevancy, were reduced to 86. The first stage consisted of carrying out a bibliometric analysis with the objective of analysing structural aspects with the SciMAT tool. Secondly, a systematic literature review was undertaken using the PRISMA 2020 statement. The results illustrated the different processes by which recommendations are filtered in areas such as tourism, health, mobility, and transport. This research is seen as a significant breakthrough that can drive the evolution and efficiency of smart cities, establishing a solid framework for future research in this dynamic field.

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Section: Background Of Smart Citiesmentioning

confidence: 99%

Section: Analysis Of the Structure And Evolution Of Recommender Syste...mentioning

confidence: 99%

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Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Andrade-Ruiz,

Carrasco,

Porcel

et al. 2024

Electronics

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“…Authors developing intelligent tutoring systems as well as assessment and evaluation tools usually took advantage of natural language techniques to analyse textual data and provide students with feedback. Typically, they assessed programming scripts [41], [76], [78], [102], students' essay or written report [37], [74], [79], [98], [101], [105], or chat or forum interactions [73], [95]- [97].…”

Section: ) Input Datamentioning

confidence: 99%

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Pierrès,

Christen,

Schmitt-Koopmann

et al. 2024

IEEE Access

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Literature reviews on artificial intelligence (AI) have focused on the different applications of AI in higher education, the AI techniques used, and the benefits/risks of the use of AI. One of the greatest potentials of AI is to personalize higher education to the needs of students and offer timely feedback. This could benefit students with disabilities tremendously if their needs are also considered in the development of new AI educational technologies (EdTech). However, current reviews have failed to address the perspective of students with disabilities, which prompts ethical concerns. For instance, AI could treat people with disabilities as outliers in the data and end up discriminating against them. For that reason, this systematic literature review raises the following two questions: To what extent are ethical concerns considered in articles presenting AI applications assessing students (with disabilities) in higher education? What are the potential risks of using AI that assess students with disabilities in higher education? This scoping review highlights the lack of ethical reflection on AI technologies and an absence of discussion and inclusion of people with disabilities. Moreover, it identifies eight risks associated with the use of AI EdTech for students with disabilities. The review concludes with suggestions on how to mitigate these potential risks. Specifically, it advocates for increased attention to ethics within the field, the involvement of people with disabilities in research and development, as well as careful adoption of AI EdTech in higher education.

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“…Human mobility forecasting such as the next location prediction task [8,25] and the customer flow prediction task [23] are essential ingredients in many domains including human mobility understanding and smart city applications. During the pandemic, support for contact tracing and crowd management has become of critical importance.…”

Section: Introductionmentioning

confidence: 99%

Translating Human Mobility Forecasting through Natural Language Generation

Xue

Salim

Ren

et al. 2022

Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining

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Existing human mobility forecasting models follow the standard design of the time-series prediction model which takes a series of numerical values as input to generate a numerical value as a prediction. Although treating this as a regression problem seems straightforward, incorporating various contextual information such as the semantic category information of each Place-of-Interest (POI) is a necessary step, and often the bottleneck, in designing an effective mobility prediction model. As opposed to the typical approach, we treat forecasting as a translation problem and propose a novel forecasting through a language generation pipeline. The paper aims to address the human mobility forecasting problem as a language translation task in a sequence-to-sequence manner. A mobilityto-language template is first introduced to describe the numerical mobility data as natural language sentences. The core intuition of the human mobility forecasting translation task is to convert the input mobility description sentences into a future mobility description from which the prediction target can be obtained. Under this pipeline, a two-branch network, SHIFT (Translating Human Mobility Forecasting), is designed. Specifically, it consists of one main branch for language generation and one auxiliary branch to directly learn mobility patterns. During the training, we develop a momentum mode for better connecting and training the two branches. Extensive experiments on three real-world datasets demonstrate that the proposed SHIFT is effective and presents a new revolutionary approach to forecasting human mobility. CCS CONCEPTS• Computing methodologies → Natural language generation; • Information systems → Data mining.

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Predicting Human Mobility via Attentive Convolutional Network

Cited by 15 publications

References 35 publications

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Emerging Perspectives on the Application of Recommender Systems in Smart Cities

Could the Use of AI in Higher Education Hinder Students With Disabilities? A Scoping Review

Translating Human Mobility Forecasting through Natural Language Generation

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