A Real-Time Passenger Flow Estimation and Prediction Method for Urban Bus Transit Systems

被引：80

作者：

Zhang, Jun ^{[1
,2
]}

Shen, Dayong ^{[3
]}

Tu, Lai ^{[4
]}

Zhang, Fan ^{[5
]}

Xu, Chengzhong ^{[6
,7
]}

Wang, Yi ^{[4
]}

Tian, Chen ^{[8
]}

Li, Xiangyang ^{[9
,10
]}

Huang, Benxiong ^{[4
]}

Li, Zhengxi ^{[11
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Hubei, Peoples R China

[2] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

[3] Natl Univ Def Technol, Res Ctr Computat Experiments & Parallel Syst, Changsha 410073, Hunan, Peoples R China

[4] Huazhong Univ Sci & Technol, Sch Elect Informat & Commun, Wuhan 430074, Hubei, Peoples R China

[5] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

[6] Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Peoples R China

[7] Illinois Inst Technol, Dept Comp Sci, Chicago, IL 60616 USA

[8] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210093, Jiangsu, Peoples R China

[9] Univ Sci & Technol China, Hefei 230026, Anhui, Peoples R China

[10] Illinois Inst Technol, Chicago, IL 60616 USA

[11] North China Univ Technol, Dept Automat, Beijing, Peoples R China

来源：

IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS | 2017年 / 18卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Real-time; estimation; prediction; urban bus transit systems; TRAFFIC FLOW; NEURAL-NETWORKS; DEMAND; MODELS;

D O I：

10.1109/TITS.2017.2686877

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Bus service is the most important function of public transportation. Besides the major goal of carrying passengers around, providing a comfortable travel experience for passengers is also a key business consideration. To provide a comfortable travel experience, effective bus scheduling is essential. Traditional approaches are based on fixed timetables. The wide adoptions of smart card fare collection systems and GPS tracing systems in public transportation provide new opportunities for using the data-driven approaches to fit the demand of passengers. In this paper, we associate these two independent data sets to derive the passengers' origin and destination. As the data are real time, we build a system to forecast the passenger flow in real time. To the best of our knowledge, this is the first paper, which implements a system utilizing smart card data and GPS data to forecast the passenger flow in real time.

引用

页码：3168 / 3178

页数：11

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