Robust design of bicycle infrastructure networks
Abstract Promoting active mobility like cycling relies on the availability of well-connected, high-quality bicycle networks. However, expanding these networks over an extended planning horizon presents one of the most complex challenges in transport science. This complexity arises from the intricate...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-99976-9 |
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| author | Christoph Steinacker Mads Paulsen Malte Schröder Jeppe Rich |
| author_facet | Christoph Steinacker Mads Paulsen Malte Schröder Jeppe Rich |
| author_sort | Christoph Steinacker |
| collection | DOAJ |
| description | Abstract Promoting active mobility like cycling relies on the availability of well-connected, high-quality bicycle networks. However, expanding these networks over an extended planning horizon presents one of the most complex challenges in transport science. This complexity arises from the intricate interactions between infrastructure availability and usage, such as network spillover effects and mode choice substitutions. In this paper, we approach the problem from two perspectives: direct optimization methods, which generate near-optimal solutions using operations research techniques, and conceptual heuristics, which offer intuitive and scalable algorithms grounded in network science. Specifically, we compare direct welfare optimization with an inverse network percolation approach to planning cycle superhighway extensions in Copenhagen. Interestingly, while the more complex optimization models yield better overall welfare results, the improvements over simpler methods are small. More importantly, we demonstrate that the increased complexity of planning approaches generally makes them more vulnerable to input uncertainty, reflecting the bias-variance tradeoff. This issue is particularly relevant in the context of long-term planning, where conditions change during the implementation of the planned infrastructure expansions. Therefore, while planning bicycle infrastructure is important and renders exceptionally high benefit-cost ratios, considerations of robustness and ease of implementation may justify the use of more straightforward network-based methods. |
| format | Article |
| id | doaj-art-4b032b518f1f42e5ba2a27be242e1e65 |
| institution | OA Journals |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-4b032b518f1f42e5ba2a27be242e1e652025-08-20T02:10:46ZengNature PortfolioScientific Reports2045-23222025-05-0115111810.1038/s41598-025-99976-9Robust design of bicycle infrastructure networksChristoph Steinacker0Mads Paulsen1Malte Schröder2Jeppe Rich3Chair of Network Dynamics, Center for Advancing Electronics Dresden (cfaed) and Institute of Theoretical Physics, TUD Dresden University of TechnologyTransportation Science Division, Department of Technology, Management and Economics, Technical University of DenmarkChair of Network Dynamics, Center for Advancing Electronics Dresden (cfaed) and Institute of Theoretical Physics, TUD Dresden University of TechnologyTransportation Science Division, Department of Technology, Management and Economics, Technical University of DenmarkAbstract Promoting active mobility like cycling relies on the availability of well-connected, high-quality bicycle networks. However, expanding these networks over an extended planning horizon presents one of the most complex challenges in transport science. This complexity arises from the intricate interactions between infrastructure availability and usage, such as network spillover effects and mode choice substitutions. In this paper, we approach the problem from two perspectives: direct optimization methods, which generate near-optimal solutions using operations research techniques, and conceptual heuristics, which offer intuitive and scalable algorithms grounded in network science. Specifically, we compare direct welfare optimization with an inverse network percolation approach to planning cycle superhighway extensions in Copenhagen. Interestingly, while the more complex optimization models yield better overall welfare results, the improvements over simpler methods are small. More importantly, we demonstrate that the increased complexity of planning approaches generally makes them more vulnerable to input uncertainty, reflecting the bias-variance tradeoff. This issue is particularly relevant in the context of long-term planning, where conditions change during the implementation of the planned infrastructure expansions. Therefore, while planning bicycle infrastructure is important and renders exceptionally high benefit-cost ratios, considerations of robustness and ease of implementation may justify the use of more straightforward network-based methods.https://doi.org/10.1038/s41598-025-99976-9 |
| spellingShingle | Christoph Steinacker Mads Paulsen Malte Schröder Jeppe Rich Robust design of bicycle infrastructure networks Scientific Reports |
| title | Robust design of bicycle infrastructure networks |
| title_full | Robust design of bicycle infrastructure networks |
| title_fullStr | Robust design of bicycle infrastructure networks |
| title_full_unstemmed | Robust design of bicycle infrastructure networks |
| title_short | Robust design of bicycle infrastructure networks |
| title_sort | robust design of bicycle infrastructure networks |
| url | https://doi.org/10.1038/s41598-025-99976-9 |
| work_keys_str_mv | AT christophsteinacker robustdesignofbicycleinfrastructurenetworks AT madspaulsen robustdesignofbicycleinfrastructurenetworks AT malteschroder robustdesignofbicycleinfrastructurenetworks AT jepperich robustdesignofbicycleinfrastructurenetworks |