A Neural Network Approach for Traffic Prediction and Routing with Missing Data Imputation for Intelligent Transportation System

2021 ◽  
pp. 114573
Author(s):  
Robin Kuok Cheong Chan ◽  
Joanne Mun-Yee Lim ◽  
Rajendran Parthiban
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Traffic Prediction ◽  
Data Imputation ◽  
Network Approach ◽  
Neural Network Approach ◽  
Missing Data Imputation

Archived Intelligent Transportation System Data Quality: Preliminary Analyses of San Antonio TransGuide Data

2000 ◽  
Vol 1719 (1) ◽  
pp. 77-84 ◽  
Author(s):  
Shawn Turner ◽  
Luke Albert ◽  
Byron Gajewski ◽  
William Eisele
Keyword(s):  
Missing Data ◽  
Data Quality ◽  
Intelligent Transportation System ◽  
San Antonio ◽  
Ground Truth ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Full Potential ◽  
Traffic Volumes ◽  
Erroneous Data

Described are three data quality attributes that are considered relevant to intelligent transportation system (ITS) data archiving: suspect or erroneous data, missing data, and data accuracy. Preliminary analyses of loop detector data from the TransGuide system in San Antonio were performed to identify the nature and extent of these data quality concerns in typical archived ITS data. The findings of the analyses indicated that missing data were inevitable, accounting for about one in five of all possible data records. Error detection rules were developed to screen for suspect or erroneous data, which accounted for only 1 percent of all possible data records. Baseline testing of TransGuide detector accuracy showed mixed results; one location collected traffic volumes within 5 percent of ground truth, whereas traffic volumes at another location ranged from 12 to 38 percent of ground truth. It was concluded that data quality procedures will be essential for realizing the full potential of archived ITS data.

scholarly journals Missing Data Imputation in Internet of Things Gateways

Information ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 425
Author(s):  
Cinthya M. França ◽  
Rodrigo S. Couto ◽  
Pedro B. Velloso
Keyword(s):  
Neural Network ◽  
Missing Data ◽  
Internet Of Things ◽  
Execution Time ◽  
Regression Models ◽  
Weather Data ◽  
Data Imputation ◽  
Missing Data Imputation ◽  
Imputation Methods ◽  
The Neural Network

In an Internet of Things (IoT) environment, sensors collect and send data to application servers through IoT gateways. However, these data may be missing values due to networking problems or sensor malfunction, which reduces applications’ reliability. This work proposes a mechanism to predict and impute missing data in IoT gateways to achieve greater autonomy at the network edge. These gateways typically have limited computing resources. Therefore, the missing data imputation methods must be simple and provide good results. Thus, this work presents two regression models based on neural networks to impute missing data in IoT gateways. In addition to the prediction quality, we analyzed both the execution time and the amount of memory used. We validated our models using six years of weather data from Rio de Janeiro, varying the missing data percentages. The results show that the neural network regression models perform better than the other imputation methods analyzed, based on the averages and repetition of previous values, for all missing data percentages. In addition, the neural network models present a short execution time and need less than 140 KiB of memory, which allows them to run on IoT gateways.

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