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3272. Optimizing the Determination of Criteria for Cyclists' Route Selection
Invited abstract in session TA-18: Optimization of sustainable urban mobility, stream Sustainable Cities.
Tuesday, 8:30-10:00Room: 42 (building: 116)
Authors (first author is the speaker)
| 1. | Rafael Praxedes
|
| Department of Engineering and Architecture, University of Parma | |
| 2. | Stefano Ardizzoni
|
| Department of Engineering and Architecture, University of Parma | |
| 3. | Luca Consolini
|
| Department of Engineering and Architecture, University of Parma | |
| 4. | Mattia Laurini
|
| Department of Engineering and Architecture, University of Parma | |
| 5. | Marco Locatelli
|
| Department of Engineering and Architecture, University of Parma | |
| 6. | Irene Saccani
|
| Department of Engineering and Architecture, University of Parma | |
| 7. | Anand Subramanian
|
| Universidade Federal da Paraíba |
Abstract
The promotion of sustainable transportation modes, such as cycling, has emerged as a crucial aspect of urban planning and design. The design of an efficient bicycle network is a complex optimization problem that requires analysis of different factors including infrastructure layout, connectivity, safety, and accessibility. However, before building the network, it is fundamental to understand the behavior of its users. The fastest routes are not always the preferred options for cyclists, who consider different criteria when choosing the best routes from their origin to their destination. Therefore, the aim of this study is to analyze the cyclists' choice behavior in the city of Parma, Italy. This analysis consists of defining a set of user profiles, where each profile corresponds to a combination of criteria used by the cyclist to choose a route, such as shortest time, safety, and connectivity. We proposed a bilevel formulation and developed a hybrid method to address this problem, which combines the solution of multiple shortest path problems with a heuristic method to determine the profiles. This approach takes advantage of solving the problem in a closed form by computing beforehand as many shortest path solutions as the number of profiles and then solving a simple quadratic programming problem. We validated our method using both randomly generated and real data collected from bicycle sharing companies in the city of Parma.
Keywords
- Network Design
- Continuous Optimization
Status: accepted
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