Enhancing Bicycle Trajectory Planning in Urban Environments through Complex Network Optimization

Abstract

This paper introduces a novel methodology for devising bike trajectory plans. In this approach, the entire trip is associated with a complex network or graph, where the nodes and edges correspond to potential trajectory segments. Utilizing the information collected from a bike-sharing system, the cost attributed to each segment is determined using the multinomial logistic regression (MLR) technique by analyzing its usage frequency and other user preferences. Consequently, segments with high usage and strong preferences entail lower costs, whereas those with limited use and weaker predilection assume higher costs. After assigning costs to all segments within the network, the subsequent step involves generating the trajectory with the lowest accumulated cost. Unlike other approaches, our method considers realistic conditions and user preferences without inconsistencies imposed by the techniques based on surveys. To validate the performance of the proposed method, a set of extensive experiments and case studies were conducted considering the urban model from downtown Guadalajara, Mexico. As a result, this approach improves the efficiency of the bike trajectory planning system by providing shorter and safer routes for both cyclists and motor vehicle drivers. The proposed bicycle trajectory planning methodology, grounded in multinomial logistic regression, unfolds various practical applications. Beyond conventional distance-centric models, our approach, driven by user-generated data from bike-sharing systems, crafts tailored routes prioritizing safety and convenience. This breakthrough optimizes trajectories and strategically targets new cyclist adoption, fostering sustainable biking cultures. Successfully validated in the urban model of Guadalajara, Mexico, our methodology equips urban planners and policymakers with a powerful tool for designing trajectories that are not only shorter but also safer. The versatility of our method extends its applicability to diverse data sets, positioning it as a forward-thinking solution in the realm of efficient and sustainable urban transportation. Practitioners can harness its potential to reshape micromobility systems, aligning them with the evolving needs of urban mobility. It is a comprehensive framework for crafting user-centric, secure, and efficient biking experiences. Cities aspiring to enhance their micromobility infrastructure could find a blueprint for urban planners in our methodology, facilitating the creation of accessible, safe, and enjoyable biking environments. In summary, our method catalyzes a transformative shift in micromobility, prompting cities to prioritize the development of not just functional but delightful biking experiences, ultimately contributing to healthier, more sustainable urban living.