## 6 Conclusions
For different road network density zones (low density \(<\)13 km/km\({}^{2}\), medium density 22-24 km/km\({}^{2}\), and high density \(\geq\)30 km/km\({}^{2}\)), differentiated connection strategies are adopted to optimise the effect of passenger attraction. When the density of the road network reaches 30 km/km\({}^{2}\) or more, roads and railways form a complementary effect, improving the accessibility of the 'last kilometre'. However, between 0 and 13 km/km\({}^{2}\), the number of passengers entering the station decreases with the increase of the network density, and between 22 and 24 km/km\({}^{2}\), there is a short-term peak and then a drop in the number of passengers due to traffic congestion. Therefore, it is important to rationalise the density of the network, especially in high-density areas (\(\geq\)30 km/km\({}^{2}\)), to expand the catchment area of the metro stations by enhancing pedestrian and cycling connections. For low-density areas, it is necessary to gradually increase the number of metro lines and frequency of service and improve ground transportation conditions to attract passengers. 4. There is an optimal combination of floor area ratio and accessibility (FAR \(>\) 2 and accessibility \(<\)25 min), which requires hierarchical control and multi-modal connection optimisation to balance development intensity and traffic efficiency. The study shows that total traffic peaks when the FAR is greater than 2 and accessibility is less than 25 min. However, when the plot ratio reaches 3, the total passenger flow tends to be saturated, and the medium-density area fails to significantly increase the outbound passenger flow due to traffic congestion and other problems. Therefore, it is crucial to manage development intensity hierarchically and optimise multi-modal transport connections to avoid new congestion problems caused by high-density development. In urban planning, the synergy between plot ratio and accessibility should be emphasised to ensure that high development densities are combined with short commuting times, in order to maximise the use of public transport resources and improve the efficiency and service quality of the urban transport system. This study has several limitations. First, the model construction did not incorporate the quantified impact of transportation policies on ridership as an independent variable, potentially underestimating the potential role of policy interventions. Second, while focusing on the physical characteristics of built environments (e.g., floor area ratio (FAR) and accessibility), the research did not fully account for residents' personal travel preferences (subjective factors like time costs and comfort levels). Future studies could integrate psychological variables to enhance analytical comprehensiveness. Additionally, although the study identified correlations between low-altitude economy and built environments, it lacked in-depth exploration of regulatory frameworks for coordinated planning and spatial resource allocation mechanisms. Future research should focus on three key areas: multi-source data integration and model validation, incorporate real-time mobility data (mobile signalling and shared travel records) and cross-city datasets (neighbouring cities like Dongguan and Huizhou) to verify model generalisability across different spatiotemporal scales. For dynamic spatiotemporal analysis, combine socioeconomic variables (income levels and travel behaviour patterns) with temporal dynamics (seasonal variations and event-driven demand fluctuations) to investigate nonlinear spatiotemporal heterogeneity in built environment impacts. For low-altitude economy and transportation system synergy, examine the reverse impact mechanisms of drone delivery systems and low-altitude node layouts on urban built environments. Develop adaptive threshold standards for floor area ratios (FARs) and accessibility in three-dimensional transportation networks.