Exploration and Practice of New Technologies for Transportation Simulation Modeling in Beijing
Transportation simulation represents an indispensable technical support for urban transportation planning and comprehensive governance.To address the transportation development demand in a megaci-ty,a data-driven and AI-enabled technical framework for transportation simulation platform has been estab-lished in Beijing,with a research focus on critical transportation simulation technologies.Based on deep learning technology,this paper presents the development of a big data driven Transportation Computation-al Graph,alternatively Beijing Computational Graph(BTCG),addressing the difficulty in multi-source spa-tiotemporal data fusion and improving the assessment accuracy of the operating state of urban transporta-tion network.Using a Travel Pattern database,the paper proposes a travel distribution prediction model based on the Universal Geographic Neural Network(UGNN),substantially improving the prediction preci-sion compared with traditional methods.In addition,a micro traffic behavior database with distinctive Chi-nese characteristics and a micro driving behavior model are developed,which are closer to the characteris-tics and rules of local driving behavior.Moreover,the paper presents a bottleneck queuing model associat-ed with the dynamic traffic assignment model,more accurately simulating the formation and dissipation process of over-saturated traffic flow.A parallel algorithm is also developed for large-scale network traffic assignment,greatly improving simulation operation efficiency.Finally,Beijing Municipal Administrative Center is used as an empirical scenario for the simulation platform to predict and evaluate the implementa-tion effect of the transportation planning scheme.
NETL
NSTL
万方数据
