Chemical Properties of Heterogenous Catalysts in Improving Yield of Biofuel Production

With the rapid urbanization, traditional single-mode transportation systems are increasingly unable to meet the growing travel demand, leading to inefficiencies and congestion. Consequently, optimizing the layout of multimodal transportation hubs has become a critical focus in urban planning.This study introduces a “multimodal network collaborative theoretical framework,” incorporating key concepts such as “transfer impedance threshold” and “dynamic traffic balance.” It systematically optimizes hub layouts, flexible resource allocation, and dynamic control by employing spatial topology algorithms, multi-agent game models, and digital twin technology.A three-tier toolkit comprising “model classification,” “algorithm adaptation,” and “scenario application” has been developed to address issues such as facility layout mismatches, inefficient resource scheduling, and limited resilience to sudden surges in passenger flow.Empirical evidence indicates that, following optimization, the distance between subway and bus connections is reduced to 150 meters, decreasing transfer time by 40%. The supply-demand matching rate for shared bicycles increases by 32%, and passenger search time is shortened to 1.8 minutes. Additionally, implementing three-dimensional layered designs and AR navigation technology reduces walking distances by 65%, enhancing hub turnover efficiency by 40%.The study confirms that multimodal collaborative optimization can increase the public transportation modal share by 15–20 percentage points, reduce unit energy consumption by 18%, and offer quantitative decision-support tools for urban transportation planning.