已入深夜,您辛苦了!由于当前在线用户较少,发布求助请尽量完整的填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!祝你早点完成任务,早点休息,好梦!

Multi-Stage Fusion Framework for Short-Term Passenger Flow Forecasting in Urban Rail Transit Systems Using Multi-Source Data

运输工程 期限(时间) 计算机科学 阶段(地层学) 轨道交通 流量(计算机网络) 过境(卫星) 公共交通 工程类 环境科学 地质学 古生物学 物理 计算机安全 量子力学
作者
Yijie Chen,Jinlei Zhang,Lu Yuan,Kuo Yang,Hanxiao Liu,Ying Liang
出处
期刊:Transportation Research Record [SAGE]
被引量:2
标识
DOI:10.1177/03611981231224740
摘要

To improve real-time operation and management in urban rail transit (URT) systems, accurate and reliable short-term passenger flow forecasting at the network level is a crucial task. Although numerous endeavors have been devoted to this field, the insufficient topological representation for passenger flows in the URT network, the overlooking of intrinsic correlations among multi-source data, and the information loss in deep-learning frameworks are still critical issues that need to be addressed. This study proposes a multi-stage fusion passenger forecasting (MSFPF) model to accomplish short-term multi-step passenger forecasting leveraging multi-source data, and overcome the above-mentioned challenges. Based on the characteristics of passenger flows in the URT network, time-based origin–destination flow data is involved and utilized to enhance the representation of flows and provide spatial-temporal features. Then, the interaction and relationship among multi-source data are estimated to capture their intrinsic correlations. To effectively and comprehensively extract temporal and spatial features, a transformer long short-term memory block and a depth-wise attention block are constructed with attention mechanisms and employed. Furthermore, we construct the multi-stage fusion (MSF) structure to alleviate the information loss during the learning process, which is a significant component in improving the forecasting accuracy. In addition, the model is applied to two large-scale real-world datasets, in which it outperforms nine widely used baselines and four specific variants of itself. The quantitative experiments demonstrate the robustness and superiority of the proposed MSFPF model, and the significant contribution of the MSF structure in the model.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
大幅提高文件上传限制,最高150M (2024-4-1)

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
zkwgly发布了新的文献求助10
2秒前
可爱的函函应助LL采纳,获得10
3秒前
Goldensun完成签到,获得积分20
3秒前
5秒前
7秒前
小蘑菇应助Bonnienuit采纳,获得10
8秒前
简晴完成签到,获得积分10
8秒前
瀚海的雄狮完成签到,获得积分10
10秒前
郎晟完成签到 ,获得积分10
10秒前
terrence完成签到,获得积分20
10秒前
今后应助wyj采纳,获得10
11秒前
jmg03发布了新的文献求助30
11秒前
DYN完成签到 ,获得积分10
12秒前
12秒前
13秒前
在水一方应助科研通管家采纳,获得10
14秒前
今后应助科研通管家采纳,获得10
14秒前
科研通AI2S应助科研通管家采纳,获得10
14秒前
Orange应助科研通管家采纳,获得10
14秒前
爱科研完成签到,获得积分10
14秒前
14秒前
Goldensun发布了新的文献求助10
16秒前
闪闪问安发布了新的文献求助10
17秒前
18秒前
愤怒的豆腐人完成签到 ,获得积分10
19秒前
熏香澡牝完成签到,获得积分10
19秒前
20秒前
上官若男应助小xx采纳,获得10
20秒前
Kaiser完成签到,获得积分20
21秒前
Pineapple Sun完成签到,获得积分10
22秒前
22秒前
wonder发布了新的文献求助10
22秒前
大抽是谁完成签到,获得积分10
23秒前
无花果应助七七采纳,获得10
24秒前
菜鸡5号完成签到,获得积分10
25秒前
Kaiser发布了新的文献求助10
26秒前
wanci应助Daisy采纳,获得30
26秒前
29秒前
华仔应助隐形小小采纳,获得10
29秒前
科研通AI2S应助skysleeper采纳,获得10
30秒前
高分求助中
Lire en communiste 1000
Ore genesis in the Zambian Copperbelt with particular reference to the northern sector of the Chambishi basin 800
Mantiden: Faszinierende Lauerjäger Faszinierende Lauerjäger 700
PraxisRatgeber: Mantiden: Faszinierende Lauerjäger 700
Becoming: An Introduction to Jung's Concept of Individuation 600
Evolution 3rd edition 500
Die Gottesanbeterin: Mantis religiosa: 656 500
热门求助领域 (近24小时)
化学 医学 生物 材料科学 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 基因 遗传学 催化作用 物理化学 免疫学 量子力学 细胞生物学
热门帖子
关注 科研通微信公众号,转发送积分 3171276
求助须知:如何正确求助?哪些是违规求助? 2822139
关于积分的说明 7938382
捐赠科研通 2482666
什么是DOI,文献DOI怎么找? 1322693
科研通“疑难数据库(出版商)”最低求助积分说明 633708
版权声明 602627