摘要
在利用人均延误进行信号控制交叉口优化时,研究者为了简化模型通常使用不同车型的车辆平均载客量和车均延误来估计人均延误。为揭示这一简化方法忽视的人均延误的影响因素,通过对传统Webster模型进行改进,从理论上证明了人均延误受到不同车辆载客量和车辆到达顺序的显著影响。进而提出一种融合改进的Webster模型和高斯混合模型的人均延误估计方法。利用VISSIM仿真软件在不同交通量、车辆到达顺序、车辆类型比例和车辆载客量分布组合的场景下测试所提出的估计方法。结果表明,信号控制交叉口人均延误受不同载客量车辆的到达顺序、车辆类型比例的影响,证明了所提出的估计方法通过反映这些影响因素能够对处于非饱和交通流条件下的人均延误及其极值进行更加准确的估计。
When optimizing signal-controlled intersections based on per capita delay,researchers often simplify models to estimate per capita delay by using average passenger capacity and average delay per vehicle for different vehicle types.This simplified method ignores certain influential factors of per capita delay.Based on the improved traditional Webster model,this paper presents theoretical proof to demonstrate that per capita delay is significantly influenced by different passenger capacities and arrival sequences of vehicles.An estimation method for per capita delay that combines the improved Webster model with Gaussian mixture model is proposed.The proposed estimation method was tested using VISSIM simulation software under various scenarios,including different traffic volume distributions,arrival sequences of vehicles,proportions of vehicle types,and vehicle passenger capacity distributions.The results show that per capita delay at signal-controlled intersections is affected by the arrival sequence of vehicles with varying passenger capacities and the vehicle type proportions.With these influential factors reflected,the proposed estimation method provides more accurate estimates of per capita delay and the extreme values under non-saturated traffic flow conditions.
作者
汤若天
邱红桐
卢健
封春房
郝明阳
TANG Ruotian;QIU Hongtong;LU Jian;FENG Chunfang;HAO Mingyang(Traffic Management Research Institute of the Ministry of Public Security,Wuxi Jiangsu 214151,China;Wuxi Huatong Intelligent Transportation Technology Development Co.Ltd.,Wuxi Jiangsu 214125,China;Beijing Key Laboratory of Traffic Engineering,Beijing University of Technology,Beijing 100124,China)
出处
《城市交通》
2023年第4期99-108,共10页
Urban Transport of China
基金
国家重点研发计划资助项目“城市智慧出行服务系统技术集成应用”(2019YFB1600300)。