Abstract
There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gained momentum in the recent years among potential users. Connected and Autonomous Electric Vehicle (CAEV) technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and self-parking. Therefore, Traffic Flow Prediction (TFP) is identified as a major issue in CAEV technologies which needs to be addressed with the help of Deep Learning (DL) techniques. In this view, the current research paper presents an artificial intelligence-based parallel autoencoder for TFP, abbreviated as AIPAE-TFP model in CAEV. The presented model involves two major processes namely, feature engineering and TFP. In feature engineering process, there are multiple stages involved such as feature construction, feature selection, and feature extraction. In addition to the above, a Support Vector Data Description (SVDD) model is also used in the filtration of anomaly points and smoothen the raw data. Finally, AIPAE model is applied to determine the predictive values of traffic flow. In order to illustrate the proficiency of the model’s predictive outcomes, a set of simulations was performed and the results were investigated under distinct aspects. The experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.
Highlights
The progressive development of Autonomous Vehicles (AV) in the recent years has attracted the maximum attention from a number of developers working under different applications
Fuel mitigation, and congestion control have been applied in Traffic Flow Prediction (TFP) data-based models to collect the previous data flow from different sources and the accumulated data is employed in detection process
The optimization of W and b is carried out using Mean Squared Error (MSE) as a loss function in Eq (12), where p = {1, ..., k} with k is assumed to be the count of hidden layers in dimensionality reduction, N depicts the overall count of training samples, fp signifies the encoder process for pth layer in which ReLU is applied to impose the sparsity on hidden representation
Summary
The progressive development of Autonomous Vehicles (AV) in the recent years has attracted the maximum attention from a number of developers working under different applications. Global Positioning System (GPS) providers such as Google Map predicts the flow of traffic and vehicle speed using Machine Learning (ML) models. To depict this notion in mathematical form, Traffic Flow Prediction (TFP) is assumed as Xti which implies the determined traffic flow value at tth time interval and ith observation. According to the study conducted earlier [4], TFP is defined as a regression problem, applied in time-series data, from traffic systems It increases the traffic management by appropriate exploitation and traffic demands on existing road structure. Fuel mitigation, and congestion control have been applied in TFP data-based models to collect the previous data flow from different sources (sensors, GPS) and the accumulated data is employed in detection process. Deep Learning (DL) is a well-known and reputed model used in traffic prediction and the identification of dependencies in high dimension dataset
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