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![Fig. 1 Actual vs. predicted [ARIMA and LSTM] for 15 min ahead prediction](profile/Bhramaramba-Ravi/publication/345780423/figure/fig1/AS:971449938505729@1608623122690/Actual-vs-predicted-ARIMA-and-LSTM-for-15-min-ahead-prediction_Q320.jpg)
Accurate prediction of traffic makes it easy to make decisions of travelling route, travelling schedule, travel vehicles choice for a commuter. The surveillance systems, GPS system installed on road way are the abundant source of traffic data. This huge amount of traffic data and increased computing power definitely motivates researchers to analyze...
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... for Forecast Result Three criteria are commonly used to evaluate the performance of traffic forecast model. The criteria that are commonly used for evaluating the performance of forecast model of traffic are 1. Mean absolute error (MAE) 2. Root mean square error (RMSE) 3. Mean relative error (MRE) In this preliminary study the Fig. 1 clearly shows that the traffic on Saturday and Sunday is comparatively lower than other 5 days of the week. We have performed data cleaning step before experiment. The parameter considered are date, flow, time period, average journey time, average speed of vehicle, data quality, link length and day of week. ...
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Citations
... LASSO regression is used to find the correlations between the pattern features and select the most critical features that are linearly related to the response. Lonare and Bhramaramba (2020) integrated the spatiotemporal correlation and compared linear regression model and LSTM model in traffic speed prediction. According to the results, LSTM model provided better performance. ...
... All these models used statistical relationships of the data to improve the accuracy of traffic forecasting. With enhanced computing power, storage and availability of huge training data acquired from the data warehouse, nonparametric methods are proven to give better results than parametric methods [8][9][10][11] . The major work among them is in Refs. ...
Existing traffic flow prediction frameworks have already achieved enormous success due to large traffic datasets and capability of deep learning models. However, data privacy and security are always a challenge in every field where data need to be uploaded to the cloud. Federated learning (FL) is an emerging trend for distributed training of data. The primary goal of FL is to train an efficient communication model without compromising data privacy. The traffic data have a robust spatio-temporal correlation, but various approaches proposed earlier have not considered spatial correlation of the traffic data. This paper presents FL-based traffic flow prediction with spatio-temporal correlation. This work uses a differential privacy (DP) scheme for privacy preservation of participant’s data. To the best of our knowledge, this is the first time that FL is used for vehicular traffic prediction while considering the spatio-temporal correlation of traffic data with DP preservation. The proposed framework trains the data locally at the client-side with DP. It then uses the model aggregation mechanism federated graph convolutional network (FedGCN) at the server-side to find the average of locally trained models. The results of the proposed work show that the FedGCN model accurately predicts the traffic. DP scheme at client-side helps clients to set a budget for privacy loss.
Over the last decade, the Advanced Driver Assistance System (ADAS) concept has evolved prominently. ADAS involves several advanced approaches such as automotive electronics, vehicular communication, RADAR, LIDAR, computer vision, and its associated aspects such as machine learning and deep learning. Of these, computer vision and machine learning-based solutions have mainly been effective that have allowed real-time vehicle control, driver-aided systems, etc. However, most of the existing works deal with ADAS deployment and autonomous driving functionality in countries with well-disciplined lane traffic. These solutions and frameworks do not work in countries and cities with less-disciplined/ chaotic traffic. Hence, critical ADAS functionalities and even L2/ L3 autonomy levels in driving remain a major open challenge. In this regard, this work proposes a novel framework called Auto-Alert. Auto-Alert performs a two-stage spatial and temporal analysis based on external traffic environment and tri-axial sensor system for safe driving assistance. This work investigates time-series analysis with deep learning models for driving events prediction and assistance. Further, as a basic premise, various essential design considerations towards the ADAS are discussed. Significantly, the Convolutional Neural Network (CNN) and Long-Short-Term-Memory (LSTM) models are applied in the proposed Auto-Alert. It is shown that the LSTM outperforms the CNN with 99% for the considered window length. Importantly, this also involves developing and demonstrating an efficient traffic monitoring and density estimation system. Further, this work provides the benchmark results for Indian Driving Dataset (IDD), specifically for the object detection task. The findings of this proposed work demonstrate the significance of using CNN and LSTM networks to assist the driver in the holistic traffic environment.
