Spatio-Temporal Segmented Traffic Flow Prediction with ANPRS Data Based on Improved XGBoost

Abstract

Traffic prediction is highly significant for intelligent traffic systems and traffic management. eXtreme Gradient Boosting (XGBoost), a scalable tree lifting algorithm, is proposed and improved to predict more high-resolution traffic state by utilizing origin-destination (OD) relationship of segment flow data between upstream and downstream on the highway. In order to achieve fine prediction, a generalized extended-segment data acquirement mode is added by incorporating information of Automatic Number Plate Recognition System (ANPRS) from exits and entrances of toll stations and acquired by mathematical OD calculation indirectly without cameras. Abnormal data preprocessing and spatio-temporal relationship matching are conducted to ensure the effectiveness of prediction. Pearson analysis of spatial correlation is performed to find the relevance between adjacent roads, and the relative importance of input modes can be verified by spatial lag input and ordinary input. Two improved models, independent XGBoost (XGBoost-I) with individual adjustment parameters of different sections and static XGBoost (XGBoost-S) with overall adjustment of parameters, are conducted and combined with temporal relevant intervals and spatial staggered sectional lag. The early_stopping_rounds adjustment mechanism (EAM) is introduced to improve the effect of the XGBoost model. The prediction accuracy of XGBoost-I-lag is generally higher than XGBoost-I, XGBoost-S-lag, XGBoost-S, and other baseline methods for short-term and long-term multistep ahead. Additionally, the accuracy of the XGBoost-I-lag is evaluated well in nonrecurrent conditions and missing cases with considerable running time. The experiment results indicate that the proposed framework is convincing, satisfactory, and computationally reasonable.

Visible-NIR hyperspectral classification of grass based on multivariate smooth mapping and extreme active learning approach

“…Due to there is still an over-fitting problem, different strategies such as hybrid prediction and regularization need to be used for non-recursive datasets 24 . The research 25 proposed that extreme gradient boosting algorithm (XGBoost) based on gradient boosting.…”

“…Equation ( 9 ) can be used as the fraction of tree cotyledons, and the tree structure is directly proportional to the fraction. If the result after splitting is less than the maximum value of the given parameter, the cotyledon depth stops growing 24 , 28 .…”

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods

“…Sun et al proposed an XGBoost-based approach to predict highway traffic flow [19]. Their method started by dividing highway segments and using cameras to directly cover road sections.…”

“…Table 2 summarizes the information about the related work documented in terms of the dataset used, the processing techniques that were used and the learning process objective. This analysis allows to perceive different forecasting objective [13,15,21,22], works that did not sufficiently detail the dataset used or the conditions of use [17,19] or were developed for very different road models [14,18], so our option was to test several deep learning methods in order to evaluate them in terms of accuracy and efficiency.…”

Traffic Flow Online Prediction Based on a Generative Adversarial Network with Multi-Source Data

“…Another type of typical non-parametric method is the neural network, which is widely used in prediction [16,17]. The improved extreme gradient boosting (XGBoost) with spatial lag has improved prediction accuracy [18]. Neural networks are good at learning multidimensional complex nonlinear problems.…”