Traffic flow combination forecasting method based on improved LSTM and ARIMA

Abstract

ABS)Traffic flow forecasting is hot spot research of intelligent traffic system construction. The existing traffic flow prediction methods have problems such as poor stability, high data requirements, or poor adaptability. In this paper, we define the traffic data time singularity ratio in the dropout module and propose a combination prediction method based on the improved long short-term memory neural network and time series autoregressive integrated moving average model (SDLSTM-ARIMA), which is derived from the Recurrent Neural Networks (RNN) model. It compares the traffic data time singularity with the probability value in the dropout module and combines them at unequal time intervals to achieve an accurate prediction of traffic flow data. Then, we design an adaptive traffic flow embedded system that can adapt to Java, Python and other languages and other interfaces. The experimental results demonstrate that the method based on the SDLSTM -ARIMA model has higher accuracy than the similar method using only autoregressive integrated moving average or autoregressive. Our embedded traffic prediction system integrating computer vision, machine learning and cloud has the advantages such as high accuracy, high reliability and low cost. Therefore, it has a wide application prospect.

Smart Cities: Data-Driven Solutions to Understand Disruptive Problems in Transportation—The Lisbon Case Study

“…Current prediction models have problems such as poor stability, significant data needs, and poor adaptability. Liu [24] defined a model derived from the RNN that combined long short-term memory (SDLSTM) with auto-regressive integrated moving average (ARIMA). The end result presented excellent adaptability and higher precision than common machinelearning methods.…”

“…Dimension Application [22] Traffic simulations based on spatially abstracted transportation networks using dependency models derived from real traffic data Identify correlations between the traffic intensity and movement speed on links of a spatially abstracted transportation network [23] Data-driven short-term data processing and LSTM-RNN Forecast urban road network traffic [24] Traffic flow combination forecasting method based on improved LSTM and ARIMA Forecast traffic flow [25] Traffic flow forecast through time series analysis based on deep learning Forecast traffic flow [26] Palm distribution application to analyze road accident risk assessment Identify correlations between traffic, road, and weather conditions in road accidents…”

“…When the traffic level was 5, the Delay column had the value "−1", and this value could not be used for analysis. To surpass this problem without impacting the column values, "−1" was treated like missing data and replaced with a value prediction using a Pearson correlation and linear regression [24,25]. Table 8 shows the Pearson correlation between all variables.…”

An attention‐based deep learning model for traffic flow prediction using spatiotemporal features towards sustainable smart city

“…A combination of LSTM and ARIMA model 57 was designed to predict the traffic flow. A DBN‐based support vector regression classifier 58 was developed for predicting the short‐term traffic data.…”

Urban Traffic Flow Forecast Based on FastGCRNN

“…However, these models are generally suitable for relatively stable traffic flow, which cannot well reflect the temporal correlation of traffic flow data, nor can they reflect the realtime nature of traffic flow. In order to solve the unstable characteristics of traffic flow data, ARIMA [8] and its variants [9,10] are used in this field [11]. Although these studies show that the prediction can be improved by considering various other factors, they are still unable to capture the complex nonlinear spatiotemporal correlation.…”