Data-Driven Methodology for Bike Route Identification to Enhance Urban Cycling Infrastructure in Metropolitan Manila

Cycling has emerged as a popular mode of transportation in Metro Manila, particularly in the areas of Norzagaray, Bulacan, and Manila, following the pandemic. This trend has continued even after the pandemic subsided, as many individuals have opted to use cycling as a primary means of transportation. This shift has been attributed to the affordability of cycling and its ability to offer respite from congested roadways. The government has recognized this shift in transportation habits and has already implemented improvements in biking infrastructure throughout the country, including in Metro Manila, Norzagaray, Bulacan, and Manila, including the introduction of dedicated bike lanes and the enactment of relevant legislation aimed at ensuring the safety and efficiency of cyclists amidst other vehicular traffic. Despite these efforts, some critics argue that the infrastructure does not adequately meet the cyclists' needs. One of the primary concerns is the congestion in urban areas due to the increased prominence of cycling. While introducing bike lanes was intended to alleviate traffic congestion, the reality in many urban centres paints a different picture. Cyclists often navigate through crowded streets, facing challenges such as inadequate infrastructure and insufficient enforcement of traffic regulations. Therefore, there is a need for continued investment in biking infrastructure and proactive measures to mitigate urban congestion to fully realize the potential of cycling as a viable mode of transportation. This study aims to delineate key bicycle routes that lack essential infrastructure, hindering cyclists' ability to navigate designated pathways and thus impeding the flow of vehicular traffic. By examining methodologies employed in various nations to develop comprehensive infrastructure conducive to the safety of cyclists and motorists, this research aims to identify effective strategies for implementation. One of the methods that have been identified involves leveraging advanced technologies, such as bike sensors, to pinpoint areas frequented by cyclists and subsequently inform the creation of dedicated bike lanes and supportive infrastructure. This entails the source utilization of data analytics to discern patterns of cyclist movement, thereby facilitating informed decision-making in infrastructure planning. To facilitate this endeavour, a bespoke bike identification model will be devised. This model will be trained using diverse datasets comprising video footage captured by local cyclists across different times of the day. Exposing the model to varying lighting conditions and angles characteristic of bicycling environments will be primed to accurately identify and delineate cyclist pathways. The acquired data will be a foundational resource for local government bodies tasked with infrastructure development. This data-driven approach will enable policymakers to tailor infrastructure initiatives to cyclists' specific needs and safety considerations by providing a comprehensive understanding of cyclist behaviour and preferred routes. This research aims to bridge the gap between existing infrastructure deficiencies and the evolving demands of the cycling community. By integrating advanced technologies and data-driven insights, it seeks to foster the creation of cycling infrastructure that enhances safety and promotes sustainable urban mobility for all road users.