Designing cycle networks to maximize health, environmental, and travel time impacts: An optimization-based approach

Abstract

There has been a recent surge of research interest in quantifying the health and environmental impacts of increased cycling in urban environments. Although there is general agreement that the benefits of increased cycling outweigh the risks, most of the methodologies developed have had limited value for evaluating real-world transport policies. This is because they are based on hypothetical scenarios where increased cycling takes place but give no consideration to the courses of action which may help policymakers to achieve the scenarios. A useful extension to these methodologies would be one which allowed a user to find the optimal infrastructure design and/or policies which would maximize total societal benefit, taking into account the health and environmental impacts of cycling. In this study, a Network Design Problem is formulated for systematically designing cycling network layouts in order to maximize the net benefits to the network users and society. The problem is formulated as a mathematical program with equilibrium constraints (MPEC) and a solution approach based on a genetic algorithm (GA) is provided to solve the problem. The problem formulation and solution algorithm are tested using a numerical example. The GA algorithm was shown to efficiently converge to an optimal or near-optimal solution for the cycle network design. The proposed optimization framework may be adopted by transport authorities and/or urban planners as a decision support tool to help them to systematically identify the best design for a cycle network which balances the benefits and risks to all stakeholders.

Keywords:

• Bicycle network design
• bicycling
• collisions
• cost-benefit analysis
• health impacts
• optimization
• pollution exposure

Acknowledgements

The authors are grateful to the three reviewers for the constructive comments.

Additional information

Funding

This research was jointly supported by the Environmental Protection Agency of Ireland (Project number: 2012-EH-PhD-11) and the National Natural Science Foundation of China (71771194).