Advancements in Passenger Flow Optimization in Smart Transport: A Holistic Survey

被引：0

作者：

Raj, Harshit ^{[1
]}

Patel, Kalp ^{[1
]}

Patidar, Sanjay ^{[1
]}

机构：

[1] Delhi Technol Univ, Dept Software Engn, Delhi 110042, India

来源：

PROCEEDINGS OF NINTH INTERNATIONAL CONGRESS ON INFORMATION AND COMMUNICATION TECHNOLOGY, VOL 10, ICICT 2024 | 2025年 / 1055卷

关键词：

Passenger flow; Smart transport; Deep learning; ARIMA; Machine learning; CNN; LSTM; PREDICTION;

D O I：

10.1007/978-981-97-5441-0_32

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Accurately predicting passenger flow is increasingly crucial, garnering significant attention in academia due to its benefits in managing crowds and potentially enhancing efficiency. This paper offers a comprehensive survey of contemporary developments in the field of passenger flow optimization. It focuses on three different approaches, namely ARIMA, traditional machine learning and deep learning. The study systematically compares and analyzes multiple research papers within the subtopics. By understanding the insights, identifying the trends and providing critical synthesis, this survey provides an overview of the state-of-art techniques in passenger flow optimization. The findings contribute to a deeper understanding of a broad field and offer valuable guidance for the researchers.

引用

页码：379 / 389

页数：11

共 15 条

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