基于人工智能的公交到达时间预测研究综述

IF 6.4 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2022-04-03 DOI:10.1002/widm.1457
Nisha Singh, K. Kumar
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引用次数: 5

摘要

公共汽车是公共交通系统的重要组成部分。在公交站点提供准确的公交到达和出发时间信息是优质公共交通的主要参数之一。准确的到达和离开时间信息对公共交通模式很重要,因为它可以提高乘客数量和旅客的满意度。与准确的到达时间和出发时间的信息,旅客可以作出明智的决定,他们的旅程。详细介绍了基于人工智能(AI)的公交到达时间预测方法/算法的应用。通过应用人工智能的不同分支,对不同研究人员进行的现有研究进行了系统的调查。预测模型已经被隔离,并在各自的人工智能分支下积累。深入讨论了人工智能的不同分支,这些分支已应用于BAT预测的几个方面。总结了研究的不足和未来可能的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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A review of bus arrival time prediction using artificial intelligence
Buses are one of the important parts of public transport system. To provide accurate information about bus arrival and departure times at bus stops is one of the main parameters of good quality public transport. Accurate arrival and departure times information is important for a public transport mode since it enhances ridership as well as satisfaction of travelers. With accurate arrival‐time and departure time information, travelers can make informed decisions about their journey. The application of artificial intelligence (AI) based methods/algorithms to predict the bus arrival time (BAT) is reviewed in detail. Systematic survey of existing research conducted by various researchers by applying the different branches of AI has been done. Prediction models have been segregated and are accumulated under respective branches of AI. Thorough discussion is presented to elaborate different branches of AI that have been applied for several aspects of BAT prediction. Research gaps and possible future directions for further research work are summarized.
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来源期刊
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery
Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
22.70
自引率
2.60%
发文量
39
审稿时长
>12 weeks
期刊介绍: The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD) are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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