Bayesian Optimized XGBoost Model for Traffic Speed Prediction Incorporating Weather Effects

Bouktif, Salah; Fiaz, Ali; Ouni, Ali; Alnaqbi, Badreyya; Alsereidi, Fatmah Salem; Alsereidi, Fatma Abdulla

doi:10.1109/icds50568.2020.9268722

Cited by 6 publications

(3 citation statements)

References 14 publications

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“…Bouktif et al [15] developed weather-sensitive models that forecasted speed for 6-, 24-and 36-h horizons in Manhattan, New York, United States. Three distinct models were developed to predict speed on the basis of: (1) only traffic data, (2) traffic and weather data, and (3) Bayesian-based optimized traffic and weather data.…”

Section: Literature Reviewmentioning

confidence: 99%

“…First, while various prediction methods have been utilized for roadway speed prediction, there are very few studies that have incorporated weather and precipitation data in the speed prediction process. Bouktif et al [15] and Huang and Ran [20] utilized weather data with 1-h resolution and Leong et al [21] utilized high-resolution precipitation data to predict speed. However, Leong et al [21] did not directly use the precipitation intensity to predict the speed; instead, the intensity of rainfall was classified on a scale of one to ten, and speed prediction was conducted based on the scale of rainfall instead of the direct measurement of rainfall.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Low-spatial resolution indicates that the data obtained from a weather station was generalized to several traffic detectors in the vicinity of the weather station, and it was assumed that weather-related parameters are all the same across the traffic detectors in that vicinity. For example, Bouktif et al [15] assumed that the data obtained from the weather station is even across all the traffic detectors in a study area of about a 3.27 km radius. Low-temporal resolution indicates that the weather records were aggregated to intervals such as 1 h, and it was assumed that the weather did not change during the aggregation period.…”

Section: Literature Reviewmentioning

confidence: 99%

See 2 more Smart Citations

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

2022

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Inclement weather and environmental factors impact traffic operations resulting in travel delays and a reduction in travel time reliability. Precipitation is an example of an environmental factor that affects travel conditions, including traffic speed. While Intelligent Transportation Systems services aim to proactively mitigate congestion on roadways, these services are often not sensitive to weather conditions. This paper investigates the application of high-resolution weather data in improving the performance of proactive transportation management models and proposes short-term speed prediction models that fuse real-time high-resolution weather surveillance radar data with traffic stream data to conduct spatial and temporal prediction of the speed of roadway segments. Extreme gradient boosting weather-aware speed prediction models were developed for a 7-km segment of Interstate 270 in St. Louis, MO, USA. The performance of the weather-aware models was compared with the performance of weather-insensitive speed prediction models that did not take precipitation into account. The results indicated that in the majority of instances, the weather-aware models outperformed the weather-insensitive models. The extreme gradient boosting models were compared with the K-nearest neighbors algorithm and feed-forward neural network models. The extreme gradient boosting model consistently outperformed the other two methods. In addition to speed prediction models, van Aerde speed-flow traffic stream models were developed for rain and no-rain conditions to study the impact of precipitation on the traffic stream across the corridor. Results indicated that the impact of precipitation is not identical across the corridor, which was mirrored in the results obtained from weather-aware speed prediction models.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

2022

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Prediction of Automobile Aerodynamic Drag Coefficient for SUV Cars Based on a Novel XGBoost Model

Liu²,

Peng³

et al. 2023

Iran J Sci Technol Trans Mech Eng

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Traffic Speed Prediction of Urban Road Network Based on High Importance Links Using XGB and SHAP

Lee

2023

IEEE Access

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With the introduction of the intelligent transportation system (ITS), traffic speed prediction has been regarded as one of the key challenging tasks in a complex urban road network. The main idea of this study is to identify links that have a significant impact on the target link and develop a highperformance travel speed prediction model using those links. This study proposes the Extreme gradient boosting model with high importance links (HI-XGB) to predict traffic speed in the urban area. Shapley additive explanations (SHAP) and XGB are used to select input features and predict traffic speed, respectively. The results show that the performance of the HI-XGB model with one-and 12-time steps ahead achieved 98.5% and 90.7% accuracy, respectively. Feature analysis and link classification analysis are performed to identify the impact of the spatial characteristic on predicted speed. Among the eight features, the speed of the target link at t and the speed change of the target link at t-1 have the most impact on the predicted target link speed. In addition, link classification analysis is performed to identify the impact of the spatial characteristic of the input feature on predicted speed. The result indicates that links other than upstream or downstream could have a greater impact on traffic speed prediction.

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Bayesian Optimized XGBoost Model for Traffic Speed Prediction Incorporating Weather Effects

Cited by 6 publications

References 14 publications

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Impact of High Resolution Radar-Obtained Weather Data on Spatio-Temporal Prediction of Freeway Speed

Prediction of Automobile Aerodynamic Drag Coefficient for SUV Cars Based on a Novel XGBoost Model

Traffic Speed Prediction of Urban Road Network Based on High Importance Links Using XGB and SHAP

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