Author(s)

Wei Yu ¹, Feifei Xie ²

Affiliation(s)

¹ School of Automotive and Transportation Engineering, Jiangsu University, Zhenjiang, 212013, China
² School of Business, Anhui University, Hefei, 230601, China

Corresponding Author

Wei Yu

ABSTRACT

Accurate prediction of road congestion is imperative for improving road utilization, thereby reducing economic losses and enhancing traffic management efficiency. Employing the XGBoost algorithm, this study integrates both temporal and spatial dimensions into the prediction of road congestion. Analysis of road congestion box plots across various coordinates and directions reveals significant disparities in traffic congestion coefficients, indicating a close relationship between the spatial dimension and traffic congestion conditions. Additionally, discernible variations in congestion coefficients between weekdays and non-workdays highlight a crucial association between traffic congestion conditions and time. The model incorporates spatial and temporal data to predict and simulate real Chicago road traffic conditions. Comparative analysis between actual and predicted values demonstrates the model's alignment with real data, attesting to its excellent predictive efficiency. Finally, elucidation of the influence of each variable on the traffic congestion prediction model is achieved through the feature importance ranking.

KEYWORDS

Congestion, XGBoost Algorithm, Spatio-Temporal Distribution, Feature Importance Ranking

CITE THIS PAPER

Wei Yu, Feifei Xie, Research on Traffic Congestion Prediction Based on XGBoost. Frontiers in Traffic and Transportation Engineering (2024) Vol. 4: 1-8. DOI: http://dx.doi.org/10.23977/ftte.2024.040101.

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