发表机构
Bangladesh University of Engineering and Technology (BUET); Accident Research Institute (ARI), Bangladesh University of Engineering and Technology (BUET)（孟加拉国工程与技术大学; 孟加拉国工程与技术大学事故研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究基于贝叶斯MCMC方法为城市主干道异质交通构建同步速度预测模型，涵盖六类车辆，决定系数0.922-0.956，并验证了模型的可迁移性。
AI 中文摘要
目标：本研究设计了一种基于贝叶斯马尔可夫链蒙特卡洛（MCMC）方法的同步速度预测模型，用于城市主干道上异质、非车道化交通，旨在确定车辆特定交通密度对六种车辆类型运行速度的影响。方法：交通数据来源于孟加拉国达卡五条主要城市道路上由无人机拍摄的视频，并通过深度学习方法进行处理，以获取车辆轨迹、交通量、密度和区间平均速度等信息。利用贝叶斯MCMC框架开发了六个同步速度预测方程，涵盖标准轿车、多用途车辆、重型车辆、三轮车、两轮车和非机动车。模型性能通过拟合优度指标进行评估。结果：六种车辆类型的模型决定系数在0.922至0.956之间，均方根误差在0.50至1.56公里/小时之间。随着交通量增加，运行速度持续下降，尽管在需求中等或较高时，交通组成对速度有显著影响。当排除非机动车时，运行速度增加且拥堵延迟。可迁移性研究表明，模型在独立路段上具有可靠的预测能力。
英文摘要
Objectives: The study designs a simultaneous speed prediction model based on Bayesian Markov Chain Monte Carlo (MCMC) methods for heterogeneous, non-lane-based traffic on urban primary roads, with the aim of determining the effects of vehicle-specific traffic densities on the operating speeds of the six vehicle types. Methods: The traffic data were obtained from videos recorded by unmanned aerial vehicles on five main urban roads in Dhaka, Bangladesh, and were then processed by a deep learning approach in order to derive information on vehicle trajectories, traffic volumes, densities, and space-mean speeds. Six simultaneous equations for speed prediction were developed using a Bayesian MCMC framework, covering standard cars, utility vehicles, heavy vehicles, three-wheelers, two-wheelers, and non-motorized vehicles. The performance of the models was assessed by means of goodness-of-fit measures. Findings: The coefficients of determination for the models varied from 0.922 to 0.956 and the root mean square errors were between 0.50 and 1.56 km/h for the six types of vehicle. Operating speeds decreased steadily as traffic volume increased, although traffic composition had a significant effect on speed when the demand was moderate or high. Operating speeds increased and congestion was delayed when non-motorized vehicles were excluded. The transferability study showed that the models had a reliable ability to make predictions on separate road sections.
CommentsTotal Number of Pages: 20
