List of parameter weight values.

With the rise in vehicle ownership, traffic congestion has emerged as a major barrier to urban progress, making the study and optimization of urban road capacity exceedingly crucial. The research on the medium and long-term free-flowing capacity and queue emission rate of roads takes an in-depth exploration of this issue from a cutting-edge perspective, aiming to find solutions adaptable to the progression of the times. The purpose of this study is to understand and predict the road capacity and queue emission rate more accurately, thus improving the urban traffic condition. Existing literature primarily focuses on short-term forecasts of road capacity, leaving a notable void in the research of medium and long-term road capacity and queue emission rate. This gap often results in a lack of sufficient foresight when urban traffic planning faces practical issues. To fill this void, this study undertook an in-depth examination of the road capacity and queue emission rate over the medium and long term (10 years) based on big data analysis and artificial intelligence theories. This paper employs a Radial Basis Function (RBF) neural network, combined with twelve other parameters that could potentially impact road capacity, such as traffic volume, road width, number of lanes, traffic signal control methods, etc., to analyze the relationship between each parameter and free-flow traffic and queue emission rate. These analyses are grounded in extensive road data, encompassing not only the city’s main roads but also secondary roads and community roads. The study results show a continuous downward trend in the free-flowing capacity of roads and a slight upward trend in the queue emission rate over the past decade. Further analysis reveals the extent of impact each factor has on the free-flow traffic and queue emission rate, providing a scientific basis for future urban traffic planning.

随着机动车保有量的攀升，交通拥堵已成为制约城市发展的一大瓶颈，对城市道路通行能力开展研究与优化显得愈发关键。针对道路中长期自由流通行能力与排队排放率的研究，从前沿视角深入剖析该问题，旨在探寻适配时代发展的解决方案。本研究的目标在于更精准地掌握并预测道路通行能力与排队排放率，进而改善城市交通状况。现有文献大多聚焦于道路通行能力的短期预测，在道路中长期通行能力与排队排放率的研究领域存在显著空白，这一空白往往导致城市交通规划在应对实际问题时缺乏足够的前瞻性。为填补这一研究空白，本研究基于大数据分析与人工智能理论，针对10年跨度的中长期道路通行能力与排队排放率展开了深入探究。本文采用径向基函数（Radial Basis Function, RBF）神经网络，结合交通流量、道路宽度、车道数、交通信号控制方式等十二项可能影响道路通行能力的参数，分析各参数与自由流交通及排队排放率之间的关联。这些分析基于海量道路数据展开，涵盖城市主干道、次干道以及社区道路。研究结果显示，在过去十年间，道路自由流通行能力呈持续下降趋势，而排队排放率则呈现小幅上升态势。进一步的分析揭示了各因素对自由流交通及排队排放率的影响程度，可为未来城市交通规划提供科学依据。