Paper Info
Title
ACP-Based Control and Management of Urban Rail Transportation Systems
Abstract
Urban rail transportation (URT) has long become the preferred public transportation choice for major metropolitan areas such as New York, London, Paris, Moscow, Tokyo, and Beijing. The highest daily record for Beijing's URT reached 5.71 million passenger trips in 2010, which makes the network extremely crowded in rush hours. To accommodate the increasing demand for URT, the service frequencies have been increased tremendously. To address these safety, efficiency, and reliability issues, the paper presents a novel parallel system for URT operations that uses the concept of parallel system and computational experiments based on artificial systems (ACP). The parallel URT system can analyze and facilitate passenger-flow management, vehicle scheduling, and other operational issues while considering human-related, environmental, and other social and economical factors.
Year
DOI
Venue
2011
10.1109/MIS.2011.25
IEEE Intelligent Systems
Keywords
Field
DocType
urt operation,parallel processing,parallel urt system,economical factor,vehicle scheduling,traffic engineering computing,passenger-flow management,artificial systems,complex system,novel parallel system,intelligent transportation systems,urban rail transportation,extensive involvement,extensive study,preferred public transportation choice,rail operational issue,urban rail transportation systems,acp-based control,intelligent systems,computational experiments,environmental factor,parallel system concept,rail traffic,agent modeling,human-related factor,control engineering,power system,human factors,production system
Intelligent decision support system,Computer science,Scheduling (computing),Simulation,Transport engineering,Knowledge management,Public transport,Intelligent transportation system,Sociotechnical system,TRIPS architecture,Metropolitan area,Beijing
Journal
Volume
Issue
ISSN
26
2
1541-1672
Citations 
PageRank 
References 
16
0.94
6
Authors
5
Name
Order
Citations
PageRank
Bin Ning144838.24
Hairong Dong229749.85
Ding Wen329117.91
Lefei Li46910.15
Changjian Cheng5427.01