A Short-term Prediction Model for Taxi Speed Based on XGBoost

XIAO Yu; ZHAO Jianyou; CHIGAN Du; LIU Qingyun

doi:10.3963/j.jssn.1674-4861.2022.03.017

Volume 40 Issue 3

Jun. 2022

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Article Navigation > Journal of Transport Information and Safety > 2022 > 40(3): 163-170

XIAO Yu, ZHAO Jianyou, CHIGAN Du, LIU Qingyun. A Short-term Prediction Model for Taxi Speed Based on XGBoost[J]. Journal of Transport Information and Safety, 2022, 40(3): 163-170. doi: 10.3963/j.jssn.1674-4861.2022.03.017

Citation:

XIAO Yu, ZHAO Jianyou, CHIGAN Du, LIU Qingyun. A Short-term Prediction Model for Taxi Speed Based on XGBoost[J]. Journal of Transport Information and Safety, 2022, 40(3): 163-170. doi: 10.3963/j.jssn.1674-4861.2022.03.017

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A Short-term Prediction Model for Taxi Speed Based on XGBoost

doi: 10.3963/j.jssn.1674-4861.2022.03.017

1.
College of Transportation Engineering, Chang'an University, Xi'an 710064, China
2.
School of Automobile, Chang'an University, Xi'an 710064, China

Received Date: 2022-01-14
Available Online: 2022-07-25

Abstract

Abstract

An accurate short-term prediction for taxi speed is the premise of identifying abnormal driving behaviors of acceleration and deceleration in advance, which helps to enhance passengers'comfort and safety. A short-term prediction model is proposed to forecast real-time speed of taxis with an Extreme Gradient Boosting(XGBoost) model. The dataset of taxi speeds is divided into a training set and a test set, where a sequence of historical speed data in a time window are taken as an input variable, and the current speed data is taken as an output variable. The accuracy of the model is evaluated by a method called walk-forward validation. Based on the Bayesian algorithm, a hyperopt module is used to optimize model parameters, and a combination of optimal parameters can be obtained in a timely fashion. Experiments are carried out based on a data set of taxi GPS trajectory, which was collected in the City of Shenzhen on October 22, 2013, and the results of the proposed model are compared with those of two other models, including a non-parametric regression model and a neural network model. The results shows that the mean absolute error(MAE)and the root mean square error(RMSE)of the proposed model is 9.841 and 12.711. respectively. Due to the lack of regularity in the taxi speed sequence, the corrected R²(R² _adjusted)is 0.592, which outperform those of the non-parametric regression model and the neural network model. Besides, compared with the two other models, the proposed model has a better goodness of fit under the scenario that a taxi suddenly changes its speed in a significant way, which can be used to avoid degraded accuracy due to model overfitting.
- urban transportation,
- taxi speed,
- short-term prediction,
- XGBoost

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References(23)

References

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