Abstract
Rail transit is normally operated on a fixed train schedule (timetable), designed based on data from typical days. In practice, however, unexpected fluctuations in passenger flow and/or in facilities may occur, making the original schedule unrealizable or non-optimal. This calls for a real-time Decision Support System (DSS) which can assist transit operators to effectively adjust the train schedule on the real-time basis when the operation environment changes markedly. Such a system can be made possible by the latest developments in intelligent transportation technologies. As the theoretical part of an operational DSS, this paper presents an optimization model, based on information available from the advanced surveillance technologies (e.g. the current situation of facilities, the short-term prediction of passenger flow, etc.), to optimize the real-time train schedule for a specific time horizon. An approximation algorithm for this model is proposed and some computational results are reported.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 109-123 |
| Number of pages | 15 |
| Journal | Transportation Planning and Technology |
| Volume | 22 |
| Issue number | 2 |
| State | Published - 1998 |
| Externally published | Yes |
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Keywords
- Advanced transportation information
- Intelligent transportation system
- Nonlinear programming
- Optimization
- Rail transit
- Scheduling
ASJC Scopus subject areas
- Transportation
Cite this
Optimal operation for rail transit systems under advanced information. / Cao, Jia Ming; Lin, Wei Hua.
In: Transportation Planning and Technology, Vol. 22, No. 2, 1998, p. 109-123.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Optimal operation for rail transit systems under advanced information
AU - Cao, Jia Ming
AU - Lin, Wei Hua
PY - 1998
Y1 - 1998
N2 - Rail transit is normally operated on a fixed train schedule (timetable), designed based on data from typical days. In practice, however, unexpected fluctuations in passenger flow and/or in facilities may occur, making the original schedule unrealizable or non-optimal. This calls for a real-time Decision Support System (DSS) which can assist transit operators to effectively adjust the train schedule on the real-time basis when the operation environment changes markedly. Such a system can be made possible by the latest developments in intelligent transportation technologies. As the theoretical part of an operational DSS, this paper presents an optimization model, based on information available from the advanced surveillance technologies (e.g. the current situation of facilities, the short-term prediction of passenger flow, etc.), to optimize the real-time train schedule for a specific time horizon. An approximation algorithm for this model is proposed and some computational results are reported.
AB - Rail transit is normally operated on a fixed train schedule (timetable), designed based on data from typical days. In practice, however, unexpected fluctuations in passenger flow and/or in facilities may occur, making the original schedule unrealizable or non-optimal. This calls for a real-time Decision Support System (DSS) which can assist transit operators to effectively adjust the train schedule on the real-time basis when the operation environment changes markedly. Such a system can be made possible by the latest developments in intelligent transportation technologies. As the theoretical part of an operational DSS, this paper presents an optimization model, based on information available from the advanced surveillance technologies (e.g. the current situation of facilities, the short-term prediction of passenger flow, etc.), to optimize the real-time train schedule for a specific time horizon. An approximation algorithm for this model is proposed and some computational results are reported.
KW - Advanced transportation information
KW - Intelligent transportation system
KW - Nonlinear programming
KW - Optimization
KW - Rail transit
KW - Scheduling
UR - http://www.scopus.com/inward/record.url?scp=0032297478&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0032297478&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0032297478
VL - 22
SP - 109
EP - 123
JO - Transportation Planning and Technology
JF - Transportation Planning and Technology
SN - 0308-1060
IS - 2
ER -