A gradient boosting method to improve travel time prediction

Zhang, Yanru; Haghani, Ali

doi:10.1016/j.trc.2015.02.019

Cited by 640 publications

(293 citation statements)

References 39 publications

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“…Hence, in this study the GBDT model can handle the nonlinear features of short-term subway ridership and leads to superior prediction accuracy. Similar studies on gradient boosting trees in travel time prediction [24] and auto insurance loss cost prediction can be also found [32].…”

Section: Model Optimizationsupporting

confidence: 54%

“…Thus, for any h(x; a) for which a feasible least-squares algorithm exists, optimal solutions can be computed by solving Equations (4) and (6) via any differentiable loss function in conjunction with forward stagewise additive modeling. Based on the above discussion, the algorithm for the gradient boosting decision trees can be summarized as follows in Figure 1 [24,28,29]: …”

Section: Gradient Boosting Decision Trees Approachmentioning

confidence: 99%

“…The dataset used in this study was collected from the DWL, FXM, HLG subway stations and its adjacent bus stops between July 2012 and November 2012. The alighting stops for those flat-fare based buses can be properly inferred by using the approach proposed by [23,24], and the number of passengers transferring from buses to subway can then be estimated by counting the alighting passengers. Similarly, the subway ridership can be calculated based on the smart card transactions entering the station.…”

Section: Data Sources and Preparationmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

et al. 2016

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Understanding the relationship between short-term subway ridership and its influential factors is crucial to improving the accuracy of short-term subway ridership prediction. Although there has been a growing body of studies on short-term ridership prediction approaches, limited effort is made to investigate the short-term subway ridership prediction considering bus transfer activities and temporal features. To fill this gap, a relatively recent data mining approach called gradient boosting decision trees (GBDT) is applied to short-term subway ridership prediction and used to capture the associations with the independent variables. Taking three subway stations in Beijing as the cases, the short-term subway ridership and alighting passengers from its adjacent bus stops are obtained based on transit smart card data. To optimize the model performance with different combinations of regularization parameters, a series of GBDT models are built with various learning rates and tree complexities by fitting a maximum of trees. The optimal model performance confirms that the gradient boosting approach can incorporate different types of predictors, fit complex nonlinear relationships, and automatically handle the multicollinearity effect with high accuracy. In contrast to other machine learning methods-or "black-box" procedures-the GBDT model can identify and rank the relative influences of bus transfer activities and temporal features on short-term subway ridership. These findings suggest that the GBDT model has considerable advantages in improving short-term subway ridership prediction in a multimodal public transportation system.

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Section: Model Optimizationsupporting

confidence: 54%

Section: Gradient Boosting Decision Trees Approachmentioning

confidence: 99%

Section: Data Sources and Preparationmentioning

confidence: 99%

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Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

et al. 2016

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“…Because it does not require making assumptions on the data, it is extensively used in certain fields, such as in the optimization of recommendation systems [62,63], visual tracking algorithms [64], and traffic systems [65][66][67][68]. The attractiveness of GBRT comes from its ability to deal with the uneven distribution of data attributes, its lack of limitation for any hypothesis of input data, its better predictive capacity than a single decision tree, its power to deal with larger data size, and its transparency in terms of model development.…”

Section: Gradient Boosting Regression Treementioning

confidence: 99%

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

et al. 2018

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Downward shortwave radiation (DSR) is an essential parameter in the terrestrial radiation budget and a necessary input for models of land-surface processes. Although several radiation products using satellite observations have been released, coarse spatial resolution and low accuracy limited their application. It is important to develop robust and accurate retrieval methods with higher spatial resolution. Machine learning methods may be powerful candidates for estimating the DSR from remotely sensed data because of their ability to perform adaptive, nonlinear data fitting. In this study, the gradient boosting regression tree (GBRT) was employed to retrieve DSR measurements with the ground observation data in China collected from the China Meteorological Administration (CMA) Meteorological Information Center and the satellite observations from the Advanced Very High Resolution Radiometer (AVHRR) at a spatial resolution of 5 km. The validation results of the DSR estimates based on the GBRT method in China at a daily time scale for clear sky conditions show an R 2 value of 0.82 and a root mean square error (RMSE) value of 27.71 W·m −2 (38.38%). These values are 0.64 and 42.97 W·m −2 (34.57%), respectively, for cloudy sky conditions. The monthly DSR estimates were also evaluated using ground measurements. The monthly DSR estimates have an overall R 2 value of 0.92 and an RMSE of 15.40 W·m −2 (12.93%). Comparison of the DSR estimates with the reanalyzed and retrieved DSR measurements from satellite observations showed that the estimated DSR is reasonably accurate but has a higher spatial resolution. Moreover, the proposed GBRT method has good scalability and is easy to apply to other parameter inversion problems by changing the parameters and training data.

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“…The GBDT algorithm is a powerful supervised learning method, which integrates the gradient boosting framework and decision tree technique into one ensemble model [22]. Due to its successful applications in many research fields, such as disease modeling [27,28], web-search ranking [29,30], and travel time prediction [31], we argue that the GBDT algorithm will be quite competent to the work of intrusion detection. The parameters of GBDT, for instance, learning rate ν, are optimized by the PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

Bai

et al. 2018

Journal of Sensors

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In order to protect computing systems from malicious attacks, network intrusion detection systems have become an important part in the security infrastructure. Recently, hybrid models that integrating several machine learning techniques have captured more attention of researchers. In this paper, a novel hybrid model was proposed with the purpose of detecting network intrusion effectively. In the proposed model, Gini index is used to select the optimal subset of features, the gradient boosted decision tree (GBDT) algorithm is adopted to detect network attacks, and the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of GBDT. The performance of the proposed model is experimentally evaluated in terms of accuracy, detection rate, precision, F1-score, and false alarm rate using the NSL-KDD dataset. Experimental results show that the proposed model is superior to the compared methods.

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A gradient boosting method to improve travel time prediction

Cited by 640 publications

References 39 publications

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

Predicting Short-Term Subway Ridership and Prioritizing Its Influential Factors Using Gradient Boosting Decision Trees

Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method

Towards Effective Network Intrusion Detection: A Hybrid Model Integrating Gini Index and GBDT with PSO

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