Dynamic Estimation of OD Flow and OD travel Time Based on Measurement Data

基于测量数据的OD流量和OD行程时间的动态估计

• 批准号: 12650523
• 负责人: NAKATSUJI Takashi
• 金额: $ 1.09万
• 依托单位: Hokkaido University
• 依托单位国家: 日本
• 项目类别: Grant-in-Aid for Scientific Research (C)
• 财政年份: 2000
• 资助国家: 日本
• 起止时间: 2000 至 2001
• 项目状态: 已结题
• 来源: https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-12650523/
• 关键词: Macroscopic Model; Kalman Filter; Neural Network Model; OD Flow; OD Travel Time; Probe Vehicle; Traffic Detector; Feedback; 動的予測; パラメータ

1) Dynamic Estimation of OD Travel Time and OD Flow・Development of a Neural Kalman Filter (NKF) model by integrating a macroscopic traffic simulation model into Kalman filter to estimate OD travel time and flow・Consideration of traffic states several time steps before・Simultaneous estimation of OD travel time and flow2) Identification of Model Parameters of a Macroscopic Traffic Flow Simulation Model・Comparison of non-linear least mean squared method with Box Complex method3) Estimation of Travel Time Using Traffic State Variables Estimated by a Feedback Technique・Indirect estimation of travel time using traffic state variables estimated by Kalman Filtering technique・Comparison of the indirect method with the NKF model.・Validation of the indirect method for both free flow and congested states4) Estimation of Traffic States on Freeway Using Probe Vehicle Data・Formulation of Kalman filtering method by assembling the probe vehicle data into the fixed detector data・Numerical examination of four estimation scenarios for various traffic situations・Success of the developed model in improving the estimation accuracy by 30 to 80% compared with the one estimated by only the macroscopic traffic flow model.・Quantification of the probe vehicle percentage and the sampling interval

1) OD行程时间和OD流量的动态估计・通过将宏观交通模拟模型集成到卡尔曼滤波器中，开发神经卡尔曼滤波器（NKF）模型，以估计OD行程时间和流量・考虑几个时间步之前的交通状态・同时OD行程时间和流量的估计2)宏观交通流仿真模型的模型参数辨识・非线性最小均方法与Box Complex方法的比较3)使用反馈技术估计的交通状态变量来估计行程时间・使用卡尔曼滤波技术估计的交通状态变量间接估计行程时间・间接方法与 NKF 模型的比较。・对自由流和自由流的间接方法进行验证拥堵状态4) 使用探测车辆数据估计高速公路上的交通状况・通过将探测车辆数据组装到固定检测器数据中来制定卡尔曼滤波方法・数值检查各种交通状况的四种估计场景・与仅通过宏观交通流模型估计的估计精度相比，开发的模型成功地将估计精度提高了 30% 至 80%。・探测车辆百分比和采样间隔的量化

项目成果

Nanthawichit, C.: "Parameter Estimation of Macroscopic Traffic Simulation Model"Infrastructure Planning Review. 18. 935-942 (2001)

Nanthawichit, C.：“宏观交通仿真模型的参数估计”基础设施规划评论。

Hironori Suzuki: "Effective of Adaptive Cruise Control on Congested freeway traffic Flow with Lane Closure"Proc. of 8th World Congress on Intelligent Transport Systems. (CD-ROM). (2001)

Hironori Suzuki：“自适应巡航控制对车道封闭的拥堵高速公路交通流的有效性”Proc。

Nanthawichit C.: "Parameter Estimation of Macroscopic Traffic Simulation Model"Infrastructure Planning Review. Vol.18, No.4. 935-942 (2001)

Nanthawichit C.：“宏观交通仿真模型的参数估计”基础设施规划评论。

H. Suzuki: "Effect of adaptive cruise control on congested freeway traffic Flow with lane closure"Proc. 8th World Congress on Intelligent Transport Systems. (CD-ROM). (2001)

H. Suzuki：“自适应巡航控制对车道封闭的拥堵高速公路交通流的影响”Proc。

Ian C.Espada: "A Discharge Capacity Formula for priority T-Inter section and Its application to Macroscopic Simulation"土木学会論文集. No.695 IV-54. 177-186 (2002)

Ian C.Espada：“优先 T-Inter 部分的放电容量公式及其在宏观模拟中的应用”日本土木工程师学会会刊第 695 IV-54 号（2002 年）。