Abstract
This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.
| Original language | English |
|---|---|
| Pages (from-to) | 167-181 |
| Number of pages | 15 |
| Journal | Networks and Spatial Economics |
| Volume | 12 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2012 Mar 1 |
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All Science Journal Classification (ASJC) codes
- Software
- Computer Networks and Communications
- Artificial Intelligence
Cite this
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Dynamic Traffic Assignment under Uncertainty : A Distributional Robust Chance-Constrained Approach. / Chung, Byung Do; Yao, Tao; Zhang, Bo.
In: Networks and Spatial Economics, Vol. 12, No. 1, 01.03.2012, p. 167-181.Research output: Contribution to journal › Article
TY - JOUR
T1 - Dynamic Traffic Assignment under Uncertainty
T2 - A Distributional Robust Chance-Constrained Approach
AU - Chung, Byung Do
AU - Yao, Tao
AU - Zhang, Bo
PY - 2012/3/1
Y1 - 2012/3/1
N2 - This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.
AB - This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.
UR - http://www.scopus.com/inward/record.url?scp=84858700896&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84858700896&partnerID=8YFLogxK
U2 - 10.1007/s11067-011-9157-8
DO - 10.1007/s11067-011-9157-8
M3 - Article
AN - SCOPUS:84858700896
VL - 12
SP - 167
EP - 181
JO - Networks and Spatial Economics
JF - Networks and Spatial Economics
SN - 1566-113X
IS - 1
ER -