Abstract
This paper presents a rapid tree-based reoptimization framework for solving traffic assignment problems in many critical rapid decision making applications. In recent years, regional transportation decision makers and planning organizations are faced with many important decision-making situations with significantly changed origin-destination (O-D) demand patterns, partially due to pattern shifts in land use and emerging transportation modes such as shared bikes and shared vehicles. Thus, there is a critical need for a faster-than-real-time decision-making support system for enabling more informed planning processes. In our approach, we propose a new reoptimization method to recalculate new paths according to baseline traffic assignment results when responding to a new set of traffic demands or supply scenarios. Through smart indexing of previously-calculated traffic assignment outputs, our proposed algorithm can generate new network flow distributions quickly with satisfactory convergence performance. Finally, numerical experiments are performed to demonstrate the adaptive converging behavior and computational efficiency of our tree-based reoptimization algorithm.
| Original language | English (US) |
|---|---|
| Title of host publication | CICTP 2018 |
| Subtitle of host publication | Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals |
| Editors | Diange Yang, Xiaokun Wang, Zheng You, Yu Zhang |
| Publisher | American Society of Civil Engineers (ASCE) |
| Pages | 205-214 |
| Number of pages | 10 |
| ISBN (Electronic) | 9780784481523 |
| State | Published - Jan 1 2018 |
| Event | 18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018 - Beijing, China Duration: Jul 5 2018 → Jul 8 2018 |
Other
| Other | 18th COTA International Conference of Transportation Professionals: Intelligence, Connectivity, and Mobility, CICTP 2018 |
|---|---|
| Country | China |
| City | Beijing |
| Period | 7/5/18 → 7/8/18 |
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ASJC Scopus subject areas
- Transportation
Cite this
A Tree-Based Reoptimization Framework for Solving Traffic Assignment Problem in Rapid Decision Making Applications. / Zhuge, Lijuan; Li, Wei; Guo, Jifu; Xian, Kai; Wu, Xin; Zhou, Xuesong.
CICTP 2018: Intelligence, Connectivity, and Mobility - Proceedings of the 18th COTA International Conference of Transportation Professionals. ed. / Diange Yang; Xiaokun Wang; Zheng You; Yu Zhang. American Society of Civil Engineers (ASCE), 2018. p. 205-214.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A Tree-Based Reoptimization Framework for Solving Traffic Assignment Problem in Rapid Decision Making Applications
AU - Zhuge, Lijuan
AU - Li, Wei
AU - Guo, Jifu
AU - Xian, Kai
AU - Wu, Xin
AU - Zhou, Xuesong
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper presents a rapid tree-based reoptimization framework for solving traffic assignment problems in many critical rapid decision making applications. In recent years, regional transportation decision makers and planning organizations are faced with many important decision-making situations with significantly changed origin-destination (O-D) demand patterns, partially due to pattern shifts in land use and emerging transportation modes such as shared bikes and shared vehicles. Thus, there is a critical need for a faster-than-real-time decision-making support system for enabling more informed planning processes. In our approach, we propose a new reoptimization method to recalculate new paths according to baseline traffic assignment results when responding to a new set of traffic demands or supply scenarios. Through smart indexing of previously-calculated traffic assignment outputs, our proposed algorithm can generate new network flow distributions quickly with satisfactory convergence performance. Finally, numerical experiments are performed to demonstrate the adaptive converging behavior and computational efficiency of our tree-based reoptimization algorithm.
AB - This paper presents a rapid tree-based reoptimization framework for solving traffic assignment problems in many critical rapid decision making applications. In recent years, regional transportation decision makers and planning organizations are faced with many important decision-making situations with significantly changed origin-destination (O-D) demand patterns, partially due to pattern shifts in land use and emerging transportation modes such as shared bikes and shared vehicles. Thus, there is a critical need for a faster-than-real-time decision-making support system for enabling more informed planning processes. In our approach, we propose a new reoptimization method to recalculate new paths according to baseline traffic assignment results when responding to a new set of traffic demands or supply scenarios. Through smart indexing of previously-calculated traffic assignment outputs, our proposed algorithm can generate new network flow distributions quickly with satisfactory convergence performance. Finally, numerical experiments are performed to demonstrate the adaptive converging behavior and computational efficiency of our tree-based reoptimization algorithm.
UR - http://www.scopus.com/inward/record.url?scp=85050498089&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85050498089&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85050498089
SP - 205
EP - 214
BT - CICTP 2018
A2 - Yang, Diange
A2 - Wang, Xiaokun
A2 - You, Zheng
A2 - Zhang, Yu
PB - American Society of Civil Engineers (ASCE)
ER -