Taxi Trip Travel Time Prediction with Isolated XGBoost Regression

Kankanamge, Kusal D.; Witharanage, Y; Withanage, Chanaka S.; Hansini, M; Lakmal, D; Thayasivam, Uthayasanker

doi:10.1109/mercon.2019.8818915

Cited by 29 publications

(18 citation statements)

References 16 publications

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“…It was applied in solving machine learning challenges in different application domains. XGBoost is an algorithm that has an ensemble of DTs and is robust to outliers, and therefore XGBoost algorithm is thought to have a good performance in time series related predictions [31]. Boosting with another ensemble tree-based method, which was first proposed by Kearns in 1988 [32] Compared with the bagging method which has a parallel process (i.e., each DT runs independently and then aggregates their outputs at the end), the boosting method behaves more like a gradual process that improves the prediction through developing multiple models in sequence by emphasizing these training cases that are difficult to estimate [30].…”

Section: Extreme Gradient Boostingmentioning

confidence: 99%

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Fan

2021

Sustainability

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Due to the increasing traffic volume in metropolitan areas, short-term travel time prediction (TTP) can be an important and useful tool for both travelers and traffic management. Accurate and reliable short-term travel time prediction can greatly help vehicle routing and congestion mitigation. One of the most challenging tasks in TTP is developing and selecting the most appropriate prediction algorithm using the available data. In this study, the travel time data was provided and collected from the Regional Integrated Transportation Information System (RITIS). Then, the travel times were predicted for short horizons (ranging from 15 to 60 min) on the selected freeway corridors by applying four different machine learning algorithms, which are Decision Trees (DT), Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Long Short-Term Memory neural network (LSTM). Many spatial and temporal characteristics that may affect travel time were used when developing the models. The performance of prediction accuracy and reliability are compared. Numerical results suggest that RF can achieve a better prediction performance result than any of the other methods not only in accuracy but also with stability.

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Section: Extreme Gradient Boostingmentioning

confidence: 99%

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Fan

2021

Sustainability

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“…The experimental part was carried out on a real-world dataset. Specifically, we trained the model on taxi trip recordings across New York City territories, a popular open data source already utilized in a variety of mobility-related studies (Jindal et al, 2017;Kankanamge et al, 2019;Wang & Ross, 2019;Xu et al, 2021).…”

Section: Datamentioning

confidence: 99%

Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions

Crivellari

Resch

2022

Comput.Urban Sci.

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Urban morphology and human mobility are two sides of the complex mixture of elements that implicitly define urban functionality. By leveraging the emerging availability of crowdsourced data, we aim for novel insights on how they relate to each other, which remains a substantial scientific challenge. Specifically, our study focuses on extracting spatial-temporal information from taxi trips in an attempt on grouping urban space based on human mobility, and subsequently assess its potential relationship with urban functional characteristics in terms of local points-of-interest (POI) distribution. Proposing a vector representation of urban areas, constructed via unsupervised machine learning on trip data’s temporal and geographic factors, the underlying idea is to define areas as “related” if they often act as destinations of similar departing regions at similar points in time, regardless of any other explicit information. Hidden relations are mapped within the generated vector space, whereby areas are represented as points and stronger/weaker relatedness is conveyed through relative distances. The mobility-related outcome is then compared with the POI-type distribution across the urban environment, to assess the functional consistency of mobility-based clusters of urban areas. Results indicate a meaningful relationship between spatial-temporal motion patterns and urban distributions of a diverse selection of POI-type categorizations, paving the way to ideally identify homogenous urban functional zones only based on the movement of people. Our data-driven approach is intended to complement traditional urban development studies on providing a novel perspective to urban activity modeling, standing out as a reference for mining information out of mobility and POI data types in the context of urban management and planning.

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“…Regardless of the data type, it is well known to provide better solutions than other ML algorithms, because of its rapidity, efficiency, and scalability [52,53]. It has been the focus of research in various fields [54][55][56]. In particular, in mechanical machining [52,57,58], XGB is a good choice to predict tool wear and surface roughness.…”

Section: Extreme Gradient Boosting Regression (Xgb)mentioning

confidence: 99%

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

Nguyen

Truong

et al. 2021

Mathematical Problems in Engineering

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This paper deals with the prediction of surface roughness in manufacturing polycarbonate (PC) by applying Bayesian optimization for machine learning models. The input variables of ultraprecision turning—namely, feed rate, depth of cut, spindle speed, and vibration of the X-, Y-, and Z-axis—are the main factors affecting surface quality. In this research, six machine learning- (ML-) based models—artificial neural network (ANN), Cat Boost Regression (CAT), Support Vector Machine (SVR), Gradient Boosting Regression (GBR), Decision Tree Regression (DTR), and Extreme Gradient Boosting Regression (XGB)—were applied to predict the surface roughness (Ra). The predictive performance of the baseline models was quantitatively assessed through error metrics: root means square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). The overall results indicate that the XGB and CAT models predict Ra with the greatest accuracy. In improving baseline models such as XGB and CAT, the Bayesian optimization (BO) is next used to determine their best hyperparameters, and the results indicate that XGB is the best model according to the evaluation metrics. Results have shown that the performance of the models has been improved significantly with BO. For example, the values of RMSE and MAE of XGB have decreased from 0.0076 to 0.0047 and from 0.0063 to 0.0027, respectively, for the training dataset. Using the testing dataset, the values of RMSE and MAE of XGB have decreased from 0.4033 to 0.2512 and from 0.2845 to 0.2225, respectively. Moreover, the vibrations of the X, Y, and Z axes and feed rate are the most significant feature in predicting the results, which is in high accordance with the literature. We find that, in a specified value domain, the vibration of the axes has a greater influence on the surface quality than does the cutting condition.

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Taxi Trip Travel Time Prediction with Isolated XGBoost Regression

Cited by 29 publications

References 16 publications

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Machine Learning Based Short-Term Travel Time Prediction: Numerical Results and Comparative Analyses

Investigating functional consistency of mobility-related urban zones via motion-driven embedding vectors and local POI-type distributions

Applying Bayesian Optimization for Machine Learning Models in Predicting the Surface Roughness in Single-Point Diamond Turning Polycarbonate

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