The Impact of Rainfall and Temperature on Peak and Off-Peak Urban Traffic

Essien, Aniekan; Petrounias, Ilias; Sampaio, Pedro; Sampaio, Sandra

doi:10.1007/978-3-319-98812-2_36

Cited by 16 publications

(8 citation statements)

References 14 publications

(14 reference statements)

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“…Many studies have examined driver behavior, travel mode, road conditions, and the importance of weather data. For example, in a study [4], rainfall intensity was associated with a 4-9% reduction in traffic. It is also found that traffic congestion has a significant relationship with temperature intensity.…”

Section: Introductionmentioning

confidence: 96%

Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression

et al. 2021

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Traffic prediction is critical to expanding a smart city and country because it improves urban planning and traffic management. This prediction is very challenging due to the multifactorial and random nature of traffic. This study presented a method based on ensemble learning to predict urban traffic congestion based on weather criteria. We used the NAS algorithm, which in the output based on heuristic methods creates an optimal model concerning input data. We had 400 data, which included the characteristics of the day’s weather, including six features: absolute humidity, dew point, visibility, wind speed, cloud height, and temperature, which in the final column is the urban traffic congestion target. We have analyzed linear regression with the results obtained in the project; this method was more efficient than other regression models. This method had an error of 0.00002 in terms of MSE criteria and SVR, random forest, and MLP methods, which have error values of 0.01033, 0.00003, and 0.0011, respectively. According to the MAE criterion, this method has a value of 0.0039. The other methods have obtained values of 0.0850, 0.0045, and 0.027, respectively, which show that our proposed model has a minor error than other methods and has been able to outpace the other models.

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Section: Introductionmentioning

confidence: 96%

Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression

et al. 2021

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“…Data-driven techniques for predictive analytics in smart manufacturing can be classified as (traditional) ML techniques and deep learning (DL) techniques [7]. In the literature, many data-driven approaches have been applied toward time-series forecasting or classification, including autoregressive integrated moving average (ARIMA) [8], support vector machines [9], statistical analysis [10], and instance-based learning techniques. The application of traditional ML techniques on large datasets consistently exposes the inherent vulnerabilities of these models, such as the inability to deal with the high dimensionality of the data feature space, multicollinearity, and varying data aggregation [11].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders

Essien

Giannetti

2020

IEEE Trans. Ind. Inf.

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196

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Time-series forecasting is applied to many areas of smart factories, including machine health monitoring, predictive maintenance, and production scheduling. In smart factories, machine speed prediction can be used to dynamically adjust production processes based on different system conditions, optimize production throughput, and minimize energy consumption. However, making accurate data-driven machine speed forecasts is challenging. Given the complex nature of industrial manufacturing process data, predictive models that are robust to noise and can capture the temporal and spatial distributions of input time-series signals are prerequisites for accurate forecasting. Motivated by recent deep learning studies in smart manufacturing, in this article, we propose an end-to-end model for multistep machine speed prediction. The model comprises a deep convolutional LSTM encoder-decoder architecture. Extensive empirical analyses using real-world data obtained from a metal packaging plant in the United Kingdom demonstrate the value of the proposed method when compared with the state-of-the-art predictive models.

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“…In terms of traffic operation, it is an acknowledged fact that rainfall reduces traffic capacity and operating speeds, thereby increasing congestion and road network productivity loss [8]. For instance, daily weather conditions like fog, heavy rainfall, and snow can reduce travel demand, cause trip postponement or cancellation, or affect travel mode (changing from a slow mode like walking or cycling to a faster one, such as car or train).…”

Section: Introductionmentioning

confidence: 99%

Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

Essien

Petrounias

Sampaio

et al. 2019

2019 IEEE International Conference on Big Data and Smart Computing (BigComp)

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Traffic parameter forecasting is critical to effective traffic management but is a challenging task due to the stochasticity of traffic flow characteristics, especially in urban road networks. Traffic networks can be affected by external factors, such as weather, events, accidents, and road construction networks. The impact of these factors can affect traffic flow parameters by influencing travel time, density, and operating speed. Although deep neural networks (DNNs) have recently shown promising signs in traffic prediction using big data, there still exists the issue of maximizing the use of the model capabilities by using big data sources. This paper proposes an improved urban traffic speed prediction approach involving input-level data fusion and deep learning. Motivated by deep learning prediction methods, we propose a Long Short-Term Memory Neural Network (LSTM-NN) for traffic speed prediction that combines traffic and weather datasets on an urban road network in Greater Manchester, United Kingdom. The experimental results substantiate the value of the approach when compared to the use of traffic-only data sources for traffic speed prediction.

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The Impact of Rainfall and Temperature on Peak and Off-Peak Urban Traffic

Cited by 16 publications

References 14 publications

Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression

Presentation of a Novel Method for Prediction of Traffic with Climate Condition Based on Ensemble Learning of Neural Architecture Search (NAS) and Linear Regression

A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders

Improving Urban Traffic Speed Prediction Using Data Source Fusion and Deep Learning

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