On-Board-Unit Data: A Big Data Platform for Scalable storage and Processing

Buroni, Giovanni; Borgne, Yann‐Aël Le; Bontempi, Gianluca; Determe, Karl

doi:10.1109/cloudtech.2018.8713342

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…Each OBU record contains an anonymous Identifier (ID resetting every day at 3 a.m.), the Timestamp, the GPS Position (latitude, longitude), the Speed (engine) and the Direction (compass). The large volume and the streaming nature of the OBU data required the setup of a big data platform for an efficient collection, storage, and analysis [32], [33].…”

Section: A Obu Data Descriptionmentioning

confidence: 99%

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Buroni¹,

Lebichot

Bontempi³

2021

IEEE Access

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Road traffic forecasting is crucial in Intelligent Transportation Systems (ITS). To achieve accurate results, it is necessary to model the dynamic nature and the complex non-linear dependencies governing traffic. The goal is particularly challenging when the prediction involves more than just one traffic variable. This paper proposes a novel multi-task learning model, called AST-MTL, to perform multi-horizon predictions of the traffic flow and speed at the road network scale. The strategy combines a multilayer fully-connected neural network (FNN) and a multi-head attention mechanism to learn related tasks while improving generalization performance. The model also includes the graph convolutional network (GCNs) and the gated recurrent unit network (GRUs) to extract the spatial and temporal features of traffic conditions. Our experiments employ new sets of GPS data, called OBU data, to perform traffic prediction in the freeway and urban contexts. The experimental results prove our model can effectively perform multi-horizon traffic forecasting for different types of roads and outperform state-of-the-art models.INDEX TERMS Deep learning, multi-task learning, graph mining, traffic prediction.

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Section: A Obu Data Descriptionmentioning

confidence: 99%

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Buroni¹,

Lebichot

Bontempi³

2021

IEEE Access

Self Cite

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“…Data from OBU OBU data consist of the real-time information of ICVs while driving at the intersection. Therefore, millions of observations and terabytes of data are generated every day [25]. In this system, the recorded information from OBU contains an anonymous identifier (ID), the timestamp, the GPS position (latitude, longitude), the speed, the acceleration, the license plate number, the wheel speed, the steering angle, and so on.…”

mentioning

confidence: 99%

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

et al. 2021

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Real-time and reliable short-term traffic state prediction is one of the most critical technologies in intelligent transportation systems (ITS). However, the traffic state is generally perceived by single sensor in existing studies, which is difficult to satisfy the requirement of real-time prediction in complex traffic networks. In this paper, a short-term traffic prediction model based on complex neural network is proposed under the environment of vehicle-to-everything (V2X) communication systems. Firstly, a traffic perception system of multi-source sensors based on V2X communication is proposed and designed. A mobile edge computing (MEC)-assisted architecture is then introduced in a V2X network to facilitate perceptual and computational abilities of the system. Moreover, the graph convolutional network (GCN), the gated recurrent unit (GRU), and the soft-attention mechanism are combined to extract spatiotemporal features of traffic state and integrate them for future prediction. Finally, an intelligent roadside test platform is demonstrated for perception and computation of real-time traffic state. The comparison experiments show that the proposed method can significantly improve the prediction accuracy by comparing with the existing neural network models, which consider one of the spatiotemporal features. In particular, for comparison results of the traffic state prediction and the error value of root mean squared error (RMSE) is reduced by 39.53%, which is the greatest reduction in error occurrences by comparing with the GCN and GRU models in 5, 10, 15 and 30 minutes respectively.

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On-Board Unit Freight Transport Data Analysis and Prediction: Big Data Analysis for Data Pre-processing and Result Accuracy

Pourroostaei Ardakani,

Cheshmehzangi

2023

Urban Sustainability

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On-Board-Unit Data: A Big Data Platform for Scalable storage and Processing

Cited by 3 publications

References 5 publications

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

AST-MTL: An Attention-Based Multi-Task Learning Strategy for Traffic Forecasting

Short-Term Traffic State Prediction Based on Mobile Edge Computing in V2X Communication

On-Board Unit Freight Transport Data Analysis and Prediction: Big Data Analysis for Data Pre-processing and Result Accuracy

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