TY - GEN
T1 - Sensitivity Analysis-Based Multi-Modal Transportation Network Vulnerability Assessment with Weibit Choice Models
AU - Gu, Y.
AU - Chen, A.
AU - Li, G.
N1 - Funding Information:
The research was sponsored by the National Natural Science Foundation of China at the Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, Guangdong, China (Project No. 72071174), the Research Grants Council of the Hong Kong Special Administrative Region (PolyU 15222221), and the Smart Cities Research Institute at the Hong Kong Polytechnic University (CDA9).
Publisher Copyright:
© 2022 IEEE.
PY - 2022/11
Y1 - 2022/11
N2 - This study proposes a sensitivity analysis-based multi-modal network vulnerability analysis through the variation in utility-based accessibility measures associated with mode and route choice dimensions. The weibit expected travel disutility is used to assess network accessibility, which is the composite travel cost derived from the weibit travel choice model. Benefiting from the properties of weibit model, the proposed accessibility measures can accurately reflect proportional variation in network performance and account for heterogenous network scales. The equilibrium mode and route choice patterns are reproduced via a weibit-based combined modal split and traffic assignment (CMSTA) model while specifically considering the mode similarity and route overlapping in travel choice modeling. The sensitivity analysis of the weibit-based CMSTA model with respect to inputs from both sides of supply and demand is conducted for vulnerability assessment and critical link identification. The sensitivity analysis-based method can reduce the computational burden of repetitively solving the CMSTA model required by commonly used enumeration- or scenario-based approaches. The proposed vulnerability measures and analysis method are demonstrated via numerical experiments on a multi-modal transportation network.
AB - This study proposes a sensitivity analysis-based multi-modal network vulnerability analysis through the variation in utility-based accessibility measures associated with mode and route choice dimensions. The weibit expected travel disutility is used to assess network accessibility, which is the composite travel cost derived from the weibit travel choice model. Benefiting from the properties of weibit model, the proposed accessibility measures can accurately reflect proportional variation in network performance and account for heterogenous network scales. The equilibrium mode and route choice patterns are reproduced via a weibit-based combined modal split and traffic assignment (CMSTA) model while specifically considering the mode similarity and route overlapping in travel choice modeling. The sensitivity analysis of the weibit-based CMSTA model with respect to inputs from both sides of supply and demand is conducted for vulnerability assessment and critical link identification. The sensitivity analysis-based method can reduce the computational burden of repetitively solving the CMSTA model required by commonly used enumeration- or scenario-based approaches. The proposed vulnerability measures and analysis method are demonstrated via numerical experiments on a multi-modal transportation network.
KW - Accessibility
KW - Combined modal split and traffic assignment
KW - Multi-modal network
KW - Sensitivity analysis
KW - Vulnerability analysis
KW - Weibit choice model
UR - http://www.scopus.com/inward/record.url?scp=85143067938&partnerID=8YFLogxK
U2 - 10.1109/ICRMS55680.2022.9944606
DO - 10.1109/ICRMS55680.2022.9944606
M3 - Conference article published in proceeding or book
AN - SCOPUS:85143067938
T3 - 13th International Conference on Reliability, Maintainability, and Safety: Reliability and Safety of Intelligent Systems, ICRMS 2022
SP - 40
EP - 44
BT - 13th International Conference on Reliability, Maintainability, and Safety
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Reliability, Maintainability, and Safety, ICRMS 2022
Y2 - 21 August 2022 through 24 August 2022
ER -