Examples of research on the optimisation of road designs considering variable traffic demand but no possible future adaptation of the infrastructure
| Objective | Source | Tools and methods for decision optimisation |
|---|---|---|
| Maximise robustness of service in transportation network design under demand uncertainty | Ukkusuri et al. (2007) | A genetic algorithm to solve the robust network design problem |
| Maximise service reliability in network design | Sumalee et al. (2006) | A gradient-based optimisation algorithm is used to solve the reliable network design problem |
| Maximise accessibility in transportation network design | Di et al. (2018) | Monte Carlo simulations |
| Maximise both capacity reliability and travel time reliability | Chen et al. (2011) | Probability model |
| Maximise the network capacity | Yim et al. (2011) | Probability model |
| Maximise the expanded net present value of a network investment by optimising not only the design variables but also the timing of the investment decisions | Chow and Regan (2011) | Expected value model |
| Maximise the present expected system consumer surplus | Ukkusuri and Patil (2009) | Expected value model |
| Optimise road design to balance road pricing, traffic congestion and environmental costs | Li et al. (2012) | Monte Carlo simulations |
| Objective | Source | Tools and methods for decision optimisation |
|---|---|---|
| Maximise robustness of service in transportation network design under demand uncertainty | A genetic algorithm to solve the robust network design problem | |
| Maximise service reliability in network design | A gradient-based optimisation algorithm is used to solve the reliable network design problem | |
| Maximise accessibility in transportation network design | Monte Carlo simulations | |
| Maximise both capacity reliability and travel time reliability | Probability model | |
| Maximise the network capacity | Probability model | |
| Maximise the expanded net present value of a network investment by optimising not only the design variables but also the timing of the investment decisions | Expected value model | |
| Maximise the present expected system consumer surplus | Expected value model | |
| Optimise road design to balance road pricing, traffic congestion and environmental costs | Monte Carlo simulations |
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