Multidimensional resilience assessment of road transport systems using a resilience matrix-based bayesian network : a case study of Khyber Pakhtunkhwa, Pakistan | |
| Author | Khan, Imran |
| Call Number | AIT Thesis no.TE-24-10 |
| Subject(s) | Bayesian statistical decision theory--Data processing Transportation systems--Pakistan |
| Note | A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Transportation Engineering |
| Publisher | Asian Institute of Technology |
| Abstract | Resilient transport systems are critical for sustaining mobility, economic stability, and emergency response in hazard-prone regions. However, existing assessment frameworks often remain fragmented, rely on extensive datasets, and lack applicability in developing regions. Addressing these gaps, this study develops a resilience matrix, informed by a systematic review and synthesis of over 250 indicators from transport resilience literature. The matrix organizes transport resilience into three overarching domains aligned with three-phase disaster resiliency cycle and nine operational dimensions, providing a structured approach for metric selection in comprehensive resilience assessments.The matrix is integrated into a Bayesian Network Model to evaluate and compare road transport resilience across 35 districts in Khyber Pakhtunkhwa, Pakistan, with emphasis on flood hazards and future climate projections. A curated set of 39 indicators, prioritized for relevance and data availability, captures attributes across network topology, functionality, socio-institutional capacity, geospatial factors, and environmental risks. Computational analysis integrates open-source tools: OSMnx and NetworkX for road network modeling and simulation, GIS for hazard-exposure mapping, and GeNIe for probabilistic inference, enabling systematic resilience evaluation at the district level.Results highlight significant spatial disparities. High-resilience districts (e.g., Mardan, Abbottabad, Peshawar) exhibit robust infrastructure, strong institutional capacity, and low hazard exposure. Conversely, critical vulnerabilities emerge in semi-urbanized, hazard-prone districts (Swat, Nowshera, Dera Ismail Khan, Charsadda, Mansehra, Lakki Marwat), where flood risks and climate stressors significantly reduce resilience despite moderate socioeconomic development. Clustering analysis categorizes districts into five resilience typologies, informing targeted interventions. Sensitivity and backward inference analyses identify pre-disaster robustness, hazard exposure, and technical capacities as key determinants of resilience outcomes. The study advances a novel, scalable, and data-conscious framework for multidimensional assessment and comparative evaluation, and long-term system-wide monitoring of transport resilience. |
| Year | 2025 |
| Type | Thesis |
| School | School of Engineering and Technology |
| Department | Department of Civil and Infrastucture Engineering (DCIE) |
| Academic Program/FoS | Transportation Engineering (TE) |
| Chairperson(s) | Bhatt, Ayushman |
| Examination Committee(s) | Kunnawee Kanitpong;Sano, Kazushi |
| Scholarship Donor(s) | Asian Development Bank-Japan |
| Degree | Thesis (M Sc.) - Asian Institute of Technology, 2025 |