清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

The Role of Deep Learning in Parking Space Identification and燩rediction燬ystems

鉴定(生物学) 计算机科学 交通拥挤 空格(标点符号) 突出 停车位 运输工程 停车指引和信息 工作(物理) 人工智能 工程类 植物 机械工程 生物 操作系统
作者
Faizan Rasheed,Yasir Saleem,Kok‐Lim Alvin Yau,Yung-Wey Chong,Sye Loong Keoh
出处
期刊:Computers, materials & continua 卷期号:75 (1): 761-784 被引量:2
标识
DOI:10.32604/cmc.2023.034988
摘要

In today's smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver's frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
orixero应助Linseed采纳,获得10
7秒前
15秒前
墨殇发布了新的文献求助10
21秒前
yx完成签到 ,获得积分10
25秒前
26秒前
Linseed发布了新的文献求助10
30秒前
墨殇完成签到,获得积分10
35秒前
Copyright应助科研通管家采纳,获得10
46秒前
48秒前
Fung发布了新的文献求助10
54秒前
Linseed完成签到,获得积分10
55秒前
科研通AI6.2应助Fung采纳,获得10
1分钟前
1分钟前
大大大忽悠完成签到 ,获得积分10
1分钟前
卡卡完成签到,获得积分10
1分钟前
木木发布了新的文献求助30
1分钟前
kkdg完成签到,获得积分10
1分钟前
千帆完成签到,获得积分10
1分钟前
KKDG完成签到,获得积分10
1分钟前
kaka完成签到,获得积分10
1分钟前
李健应助awa606采纳,获得10
1分钟前
房天川完成签到 ,获得积分10
2分钟前
慕容杏子完成签到 ,获得积分10
2分钟前
2分钟前
naczx完成签到,获得积分0
2分钟前
2分钟前
ztlaky发布了新的文献求助10
2分钟前
科研通AI6.3应助木木采纳,获得10
2分钟前
NexusExplorer应助ztlaky采纳,获得10
2分钟前
3分钟前
Fung发布了新的文献求助10
3分钟前
awa606发布了新的文献求助10
3分钟前
CPU完成签到 ,获得积分10
3分钟前
单薄海亦完成签到 ,获得积分10
3分钟前
3分钟前
CodeCraft应助Fung采纳,获得10
3分钟前
喝醉的cc发布了新的文献求助10
3分钟前
田様应助awa606采纳,获得10
3分钟前
慧子完成签到 ,获得积分10
4分钟前
发nature的研究生大人完成签到 ,获得积分10
4分钟前
高分求助中
Principles of Economics, 11th Edition 10000
University Physics with Modern Physics, 16th edition 10000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Arthritis and Related Conditions, An Issue of Orthopedic Clinics 1000
Development of a Bridge Weigh-In-Motion System: A technology to convert the bridge response to the passage of traffic into data on vehicle configurations, speeds, times of travel and weights 1000
ズームレンズの光学設計に関する研究 800
Fundamentals of Pharmaceutical and Biologics Regulations: A Global Perspective, Second Edition 700
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7290318
求助须知:如何正确求助?哪些是违规求助? 8909524
关于积分的说明 18856875
捐赠科研通 6957885
什么是DOI,文献DOI怎么找? 3209105
关于科研通互助平台的介绍 2378856
邀请新用户注册赠送积分活动 2184875