基于 WND-LSTM 模型的智慧城市交通流预测与分析。

Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model.

机构信息

School of Marxism, Guangzhou University of Chinese Medicine, Guangzhou 510006, China.

Business School of International Medicine, China Pharmaceutical University, Nanjing 210009, China.

出版信息

Comput Intell Neurosci. 2022 Aug 2;2022:7079045. doi: 10.1155/2022/7079045. eCollection 2022.

DOI:10.1155/2022/7079045

PMID:35958752

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9363163/

Abstract

Aiming at the problem that the road traffic flow in intelligent city is unevenly distributed in time and space, difficult to predict, and prone to traffic congestion, combined with pattern recognition and big data mining technology, this paper proposes a research method to analyze and mine the daily travel patterns of urban vehicles. This paper proposes a WND-LSTM model, which mainly includes data preprocessing, data modelling, and model implementation, to analyze the similarity of travel patterns in seasonal changes. Combining the data mining results with the data mining results, the daily travel model of road traffic vehicles in intelligent city is established. The results of the case study showed that the WND-LSTM model outperformed ARIMA (88.48%), LR (65.79%), SVR (70.46%), KNN (68.21%), SAEs (66.95%), GRU (68.43%), and LSTM (70.41%) in MAPE, respectively, with an average accuracy improvement of 71.25% (MAPE of 0.651%).

摘要

针对智慧城市道路交通流在时间和空间上分布不均匀、难以预测、容易出现交通拥堵的问题，结合模式识别和大数据挖掘技术，提出了一种分析和挖掘城市车辆日常出行模式的研究方法。本文提出了一种 WND-LSTM 模型，主要包括数据预处理、数据建模和模型实现，以分析季节性变化中出行模式的相似性。将数据挖掘结果与数据挖掘结果相结合，建立了智能城市道路交通车辆的日常出行模型。案例研究结果表明，WND-LSTM 模型在 MAPE 方面分别优于 ARIMA（88.48%）、LR（65.79%）、SVR（70.46%）、KNN（68.21%）、SAEs（66.95%）、GRU（68.43%）和 LSTM（70.41%），平均准确率提高了 71.25%（MAPE 为 0.651%）。