Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

Fan, Pengcheng; Guo, Jingqiu; Zhao, Haifeng; Wijnands, Jasper S.; Wang, Yibing

doi:10.3390/su11236755

Cited by 17 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Generally speaking, through the image acquisition device, the quality of medical images will reduce to a certain extent the process of image acquisition, transmission, and conversion such as gray value changes, loss of details, geometric distortion, and noise pollution [33][34][35]. The original purpose of image preprocessing is to exclude unimportant information in the image, leave useful information, improve detection possibilities, simplify the image to the greatest extent, and make subsequent operations such as image segmentation and image recognition easier [36].…”

Section: Vr Technologymentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

View full text Add to dashboard Cite

Yield prediction has always been the focus of stock investors' attention in stock investment. This research uses the virtual stock scene constructed by VR technology to explore the mathematical model of return rate prediction. First, neural nodes, activation functions and output functions are designed under the LSTM framework, and a recurrent neural network that can handle long-term sequences is constructed. Use VR technology to simulate stock data and extract key frames from the image sequence. Aiming at the timing of key curve nodes, this study uses a long and short-term memory neural network that is good at processing time series data to classify curve trends, and realizes the identification of abnormal fluctuations in virtual scenes of stock images. Then, according to the existing probability control asset allocation theory, the index of investment allocation is adjusted from the entire time series to a dynamic time series. According to the stock price decomposition of EEMD, the EEMD-LSTM model is constructed to predict the stock market price. Use EEMD to decompose the original sequence to obtain IMF and trend items containing various timescale characteristics. Then, the LSTM neural network is used to predict and analyze each rate of return, and the analysis software Python is used to process and count the predicted results. At the same time, retrospective restrictions are regarded as riskier assets. Finally, the investment is made under conditions that do not allow shorting in the VR simulation scenario. The accuracy of the calculation is more than 70% except for the Shanghai and Shenzhen 300 Index reaching 66%. The research results show that in the case of different stock price forecasts, the prediction ability of LSTM neural network has been greatly improved compared with that of support vector machine, which fully reflects the advantages of LSTM in time series prediction.

show abstract

Section: Vr Technologymentioning

confidence: 99%

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

2024

View full text Add to dashboard Cite

show abstract

“…In this study, instead of defining an explicit formulation for f ( · ) and g ( · ) , they are expressed by a data-driven model. More specifically, the model makes use of LSTM, the ability of which to reproduce car-following behaviors has been demonstrated in earlier studies ( 21 , 22 ).…”

Section: Model Specificationmentioning

confidence: 99%

“…Zhou et al proposed a microscopic car-following model based on recurrent neural network to detect and predict traffic oscillation ( 20 ). Fan et al and Wang et al applied a long short-term memory (LSTM) neural network to a car-following model, demonstrating the significance of the long memory ( 21 , 22 ). Lee et al integrate the stochastic car-following model and neural-network-based lane-changing model and find that the proposed model can tackle the unpredictable fluctuations in the velocity of the vehicles in the acceleration/deceleration zone ( 23 ).…”

mentioning

confidence: 99%

Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Shiomi

Knoop

2023

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

Traffic dynamics on freeways are stochastic in nature because of errors in perception and operation of drivers as well as the heterogeneity between and within drivers. This stochasticity is often represented in car-following models by a stochastic term, which is assumed to follow a normal distribution for the convenience of mathematical processing. However, the validity of this assumption has not been studied yet. In this study, we focused on the shape of the distribution of a stochastic term in the car-following model that predicts an acceleration after a time step. Based on vehicle trajectory data on a freeway in Japan, a car-following model is first developed by using data-driven methodology in which long short-term memory (LSTM) network is applied. In this LSTM network, the acceleration value is discretized and the model parameters are trained with the focal loss function. The relationship between the predicted distributions’ modality, standard deviation (SD), and [Formula: see text] with respect to traffic states is then examined. The findings demonstrate that: 1) the developed model can accurately predict the accelerations; 2) a probabilistic distribution tends to have a large SD and multimodality around a merging point and at the beginning of and along stop-and-go waves; and 3) driving behavior can be classed in one of four clusters based on the variation of the percentile value that a driver takes within the probability distribution. The proposed model and the insights are helpful for improving microscopic simulation models when considering new traffic management measures.

show abstract

“…This can be done using real traffic data during model calibration, when the input hyperparameters are adjusted in a way that each car's behaviour and trajectory mirrors the real-life data as closely as possible. This optimisation process can be done using analytic calculations [24]; however, newer methods also use artificial intelligence and neural networks [25], [26].…”

Section: B Parametric Traffic Modelsmentioning

confidence: 99%

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

Hollósi¹,

Lukovszki²,

Bancsics³

et al. 2021

Infocommunications journal

View full text Add to dashboard Cite

High density traffic on highways and city streets consists of endless interactions among participants. These interactions and the corresponding behaviours have great impact not only on throughput of traffic but also on safety, comfort and economy. Because of this, there is a great interest in deeper understanding of these interactions and concluding the impacts on traffic participants. This paper explores and maps the world of traffic behaviour analysis, especially researches focusing on groups of vehicles called traffic swarm, while presents the state-of-the-art methods and algorithms. The conclusion of this paper states that there are special areas of traffic behaviour analysis which have great research potential in the near future to describe traffic behaviour in more detail than present methods.

show abstract

Car-Following Modeling Incorporating Driving Memory Based on Autoencoder and Long Short-Term Memory Neural Networks

Cited by 17 publications

References 26 publications

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Retracted: Mathematical Model of Yield Forecast Based on Long and Short-Term Memory Image Neural Network

Analysis of Stochasticity and Heterogeneity of Car-Following Behavior Based on Data-Driven Modeling

Traffic Swarm Behaviour: Machine Learning and Game Theory in Behaviour Analysis

Contact Info

Product

Resources

About