Table 2 An overview of the reviewed research articles in this study

Research Objectives

• Solving vehicle scheduling problem ([88], [93], [83], [38])

 • Assessing the criticality of infrastructure components ([117], [121], [138], [18], [108], [77], [84], [103], [134])

 • Developing post-disaster road restoration model ([136], [6], [90], [44], [95], [47])

 • Estimating reliability using uncertain parameters (e.g., link capacity, people’s

 • willingness to pay) ([118], [14], [31], [42], [32])

 • Analyzing vulnerability of the transportation network ([96], [113], [76], [87], [64], [81], [43], [134])

 • Developing resilience model ([132], [41], [141], [3], [72], [66], [26], [99], [73], [70])

Methodology

• Concept-based discussion ([27], [88], [96], [16], [113], [108], [106])

 • Uncertainty or probabilistic analysis-based model development ([118], [18], [15], [31], [32], [46], [19], [119])

 • Deterministic analysis of vulnerability or reliability ([81], [49])

 • Regressing model-based analysis [77]

 • Simulation-based analysis of vulnerability or reliability ([138], [43], [103], [14])

 • Disutility function-based analysis [137]

 • Sensitivity analysis of network reliability [42]

 • Optimization function-based model development ([93], [83], [88], [121], [136], [6], [90], [44], [95], [46])

 • Population-based formulation to measure accessibility [117]

 • Mathematical equation-based analysis ([132], [41])

 • GIS-based framework development [66]

Key findings

• Appropriate modeling tool can reduce computation time, cost or number of variables in solving rescheduling problem in transportation system operation ([88], [93], [83])

 • Medium traffic demand condition shows larger drops in speeds compared to high demand case in disaster [121]

 • Restoration model determines the optimized restoration time of the road network by planning road trips in a post-disaster scenario ([136], [6])

 • Vulnerability index can be used to explore and detect the criticality/importance of different network components ([76], [87], [18], [108], [81], [43], [77], [84], [31])

 • Based on drivers’ route choice behavior in a post-disaster scenario, the network capacity can be adjusted to accommodate traffic demand at a required level of service [32]

 • The impact of the disaster may vary significantly based on the region of the studied network system [132]

 • Additional travel time for a trip can be used to measure the disaster impact [41]

 • Increased delay due to the disaster can occur in the post-disaster situation due to the congestion on reduced functional links and nodes in a network [141]

Application domain

• Roadway transportation ([117], [121], [66], [136], [6], [76], [81], [43], [77], [41], [27], [88], [83], [38], [18], [15], [132])

 • Railway transportation ([27], [88], [83], [38], [93], [18], [15], [87], [64])

 • Airway transportation (both passenger and freight) ([138], [108])

Region of the study

• Hypothetical Network ([27], [88], [83], [38], [93], [121]), [136], [118], [32])

 • Real-world road network system ([117], [76], [77], [81], [43], [84], [134], [6], [132], [8], [9])

 • Real-world subway system ([87], [64])

Disaster scenarios

• Natural Disaster ([16], [81], [90], [44], [95], [46], [47], [117], [77], [49], [132], [41])

 • Man-made events ([43], [77], [49], [14], [64], [31], [27], [88], [83], [38], [93], [121], [136], [6])