城市内密集交通流行程时间预测数学建模仿真  

Mathematical Modeling and Simulation of Travel Time Prediction of Dense Traffic Flow in the city

作　　者：宋娜娜 葛杨[2] 程海涛[3] SONG Na-na;GE Yang;CHENG Hai-tao(Chongqing College of Mobile Communication,Chongqing 401520,China;School of Software Engineering,Pass College of Chongqing Technology and Business University,Chongqing 401520,China;Qingdao University of Technology,Qingdao Shandong 266000,China)

机构地区：[1]重庆移通学院,重庆401520 [2]重庆工商大学派斯学院软件工程学院,重庆401520 [3]青岛理工大学,山东青岛266000

出　　处：《计算机仿真》2024年第6期211-215,共5页Computer Simulation

基　　金：重庆市高等教育教学改革研究一般项目(193318)。

摘　　要：城市交通网络包含大量的道路交叉口和车辆,且受工作日和休息日多因素的影响,使得交通流量具有不确定性,增加了预测的难度。为此,提出城市密集交通流行程时间预测数学建模研究。取样城市车辆历史数据,明确数据插值点,插值处理车辆运行数据。利用深度学习数据归一化,计算平均行程时间、行程时间方差以及可靠度指标,提取城市密集交通流特征。基于卡尔曼滤波将预测问题转化成空间状态计算问题,实现密集交通流行程时间预测。通过实验证明,所建模型能够准确预测城市密集交通流行程时间,平均绝对百分误差均在2.3%以下,能帮助驾驶人合理规划出行。Currently,urban traffic networks contain a large number of road intersections and vehicles.Due to the influence of multiple factors such as workdays and rest days,traffic flow is uncertain,which increases the difficulty of prediction.Therefore,this article proposed a mathematical modeling study on predicting trip time in dense urban traffic.Firstly,we sampled historical data of urban vehicles and then identified data interpolation points to interpolate vehicle operation data.Secondly,we used deep learning data normalization to calculate average travel time,travel time variance,and reliability indicators,thus extracting the features of dense urban traffic flow.Finally,based on the Kalman filter,we transformed the prediction problem into a problem about spatial state calculation,thereby achieving the prediction of trip time in dense traffic.Experiment results prove that the model can accurately predict the travel time in dense urban traffic and help drivers plan their trips reasonably.Meanwhile,the average absolute percentage error is less than 2.3%.

关 键 词：城市密集交通 行程时间 时间预测 插值处理 

分 类 号：U491.14[交通运输工程—交通运输规划与管理] TP391.9[交通运输工程—道路与铁道工程]