Prediction of Traffic Flow via Connected Vehicles

Mallah, Ranwa Al; Quintero, Alejandro; Farooq, Bilal

doi:10.1109/tmc.2020.3006713

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…The development of such technologies presents great improvements for the entire traffic network and each vehicle independently [59]. Vehicles' routes can be optimized in real time in order to reduce traffic congestion [60], notify vehicles of dangerous weather conditions, and even further reduce the total energy consumption from each vehicle by using smoother and synchronized driving motions.…”

Section: Delivery With Distributed Autonomous Vehiclesmentioning

confidence: 99%

A Viewpoint on the Challenges and Solutions for Driverless Last-Mile Delivery

et al. 2022

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The occurring growth in e-commerce comes along with an increasing number of first-time delivery failures due to the customer’s absence at the delivery location. Failed deliveries result in rework, causing a significant impact on the carriers’ delivery cost. Hence, the last mile is the portion of a journey that involves moving people and commodities from a transportation hub to a final destination, which should be an efficient process. The above-mentioned concept is used in supply chain management and transportation planning. The paper at hand is a position paper that aims to scrutinize the concept of driverless last-mile delivery, with autonomous vehicles, in order to highlight and stress the challenges and limitations in the existing technology that hinder level five autonomous driving. Specifically, this work documents the current capabilities of the existing autonomous vehicles’ perception and cognition system and outlines their future skills towards addressing complete autonomous last-mile delivery, as well as efficient robotic process automation in logistics from warehouse/distribution center to hub’s delivery.

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Section: Delivery With Distributed Autonomous Vehiclesmentioning

confidence: 99%

A Viewpoint on the Challenges and Solutions for Driverless Last-Mile Delivery

et al. 2022

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“…A considerable number of studies have been conducted on missing traffic data imputation. At the same time, many traffic predictions emphasize the processing of missing data ( 9 – 15 ). A classification by Li et al divides traffic imputation techniques into three categories: prediction, interpolation, and statistical learning ( 7 ).…”

Section: Related Workmentioning

confidence: 99%

Traffic Mosaic: Traffic Data Imputation Method Inspired by Image Super Resolution

Shuai

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Missing data is a major problem in data-driven intelligent transportation systems. This study presents an imputation method, called TrafficMosaic, for effective imputation of lost traffic flow data. If we consider traffic data as a image, then the missing data in it can be regarded as a mosaic. The design of TrafficMosaic is inspired by super resolution techniques. First, a deep neural network (DNN) is employed to recover the lost data by a suite of techniques inspired from image super resolution. This DNN method combines the temporal relevance of historical flow with spatial correlation of current fragmented flow data, and uses the imputation result as input for recursive calculation. Second, a data-driven road network matrixing algorithm is proposed to mine location relations from trajectory data and reconstruct a road network flow into matrixes of road network flow pictures. Convolutional local calculation in a convolutional neural network is introduced to extract local spatial features of the road network, thereby reducing the computational complexity and improving the generalization ability. The final step is to extract the local temporal features of the flow data. Traffic flows at adjacent moments have strong temporal characteristics, and we splice several historical road network traffic pictures in chronological order so that our model can capture temporal information from the sequence. The experimental evaluation used a real automatic number plate recognition dataset for experiments, and the results show the effectiveness of TrafficMosaic for lost data imputation.

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“…In the rapidly evolving field of industrial automation and monitoring, the analysis of sensory time-series data plays a fundamental role in ensuring the efficiency and reliability of machinery and systems (1,2). Meanwhile, one of the important applications of data analysis by various artificial intelligence algorithms that can be seen in daily life is the prediction of transport traffic and routing based on the shortest possible time to reach the destination (3)(4)(5). In addition, in the past few years, in order to study the corona virus and its spread in the world, time series analysis methods were also used to analyze the speed of transmission, progress and even treatment time (6).…”

Section: Introductionmentioning

confidence: 99%

Enhanced Autoregressive Integrated Moving Average Model for Anomaly Detection in Power Plant Operations

Khalid Fahmi,

Kashyzadeh,

Ghorbani

2024

IJE

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This study introduces an Enhanced Autoregressive Integrated Moving Average (E-ARIMA) model for anomaly detection in time-series data, using vibrations monitored by CA 202 accelerometers at the Kirkuk Gas Power Plant as a case study. The objective is to overcome the limitations of traditional ARIMA models in analyzing the non-linear and dynamic nature of industrial sensory data. The novel proposed methodology includes data preparation through linear interpolation to address dataset gaps, stationarity confirmation via the Augmented Dickey-Fuller Test, and ARIMA model optimization against the Akaike Information Criterion, with a specialized time-series cross-validation technique. The results show that E-ARIMA model has superior performance in anomaly detection compared to conventional Seasonal ARIMA (SARIMA) and Vector Autoregressive models. In this regard, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) criteria were utilized for this evaluation. Finally, the most important achievement of this research is that the results highlight the enhanced predictive accuracy of the E-ARIMA model, making it a potent tool for industrial applications such as machinery health monitoring, where early detection of anomalies is crucial to prevent costly downtimes and facilitate maintenance planning.

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Prediction of Traffic Flow via Connected Vehicles

Cited by 12 publications

References 31 publications

A Viewpoint on the Challenges and Solutions for Driverless Last-Mile Delivery

A Viewpoint on the Challenges and Solutions for Driverless Last-Mile Delivery

Traffic Mosaic: Traffic Data Imputation Method Inspired by Image Super Resolution

Enhanced Autoregressive Integrated Moving Average Model for Anomaly Detection in Power Plant Operations

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