摘要
为研究道路交通流特性,基于车载高精度GPS跟驰试验数据进行车辆跟驰建模研究,结合深度学习理论和数据驱动方法,构建了基于粒子群优化(particle swarm optimization,PSO)的长短期记忆(long short term memory,LSTM)车辆跟驰模型。首先,清洗和平滑车辆轨迹数据,并对驾驶特征行为参数及相关关系进行研究,如加速度、车头时距以及速度与跟驰距离特性关系等。在此基础上,制定跟驰状态筛选规则;其次,构建考虑时间序列的PSO-LSTM模型,识别跟驰数据样本集,将当前时刻的前车速度、车头间距和上一时刻的车头时距作为模型输入,预测当前时刻的跟驰车速度;接着,选用25辆车跟驰试验的高精度GPS数据验证PSO-LSTM模型性能;最后,为验证该模型的优越性,选用传统机器学习支持向量回归(support vector regression,SVR)模型以及深度学习LSTM模型作为对比。结果表明,基于粒子群优化的长短期记忆模型预测精度高达0.993,整体预测效果高于SVR模型和LSTM模型,其中预测误差指标平均绝对百分比误差(mean absolute percentage error,MAPE)较SVR和LSTM分别降低了60.02%和1.52%。PSO算法进行超参数优化后的PSO-LSTM模型能更好地模拟车辆的跟驰行为。
In order to study the characteristics of road traffic flow,a car-following model based on vehicle-mounted high-precision GPS car-following test data was studied.Combining the deep learning theory and data-driven method,a long and short term memory(LSTM)car-following model based on particle swarm optimization(PSO)was constructed.Firstly,the vehicle track data was cleaned and smoothed,and the parameters related to driving behavior,such as acceleration,headway,and the relationship between speed and car-following distance were examined.Car-following status screening rules were formulated based on this analysis.Secondly,a PSO-LSTM model considering time series was constructed to identify the sample set of car-following data.The current time's preceding car speed,headway,and the preceding car's headway at the previous time were utilized as inputs for the model to predict the car-following speed at the current time.Subsequently,the performance of the PSO-LSTM model was verified using high-precision GPS data from 25 car-following tests.Finally,traditional machine learning support vector regression(SVR)model and deep learning LSTM model were selected for comparison in order to validate the superiority of the proposed model.The results demonstrate that the LSTM model based on PSO achieves a prediction accuracy as high as 0.993,outperforming the SVR and LSTM models overall.Moreover,the prediction error index mean absolute percentage error(MAPE)is reduced by 60.02% and 1.52% compared to SVR and LSTM,respectively.The PSO-LSTM model,optimized through the PSO algorithm for hyperparameter tuning,provides a better simulation of the car-following behavior of vehicles.
作者
李轶群
石家继
陈淼
申薇
赵建东
LI Yi-qun;SHI Jia-ji;CHEN Miao;SHEN Wei;ZHAO Jian-dong(Henan Transport Investment Group Co.,Ltd.,Zhengzhou 450000,China;Airport Branch of Henan Zhongyuan Expressway Co.,Ltd.,Zhengzhou 450000,China;School of Traffic and Transportation,Beijing Jiaotong University,Beijing 100044,China)
出处
《科学技术与工程》
北大核心
2024年第1期408-415,共8页
Science Technology and Engineering
基金
国家自然科学基金(71871011)
河南省交通运输厅科技项目(2020G3)。
关键词
跟驰模型
车辆跟驰行为预测
数据驱动
机器学习
粒子群算法
car-following model
car-following behavior prediction
data driven
machine learning
particle swarm optimization
