Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

Šemanjski, Ivana

doi:10.7307/ptt.v27i6.1762

Cited by 9 publications

(2 citation statements)

References 29 publications

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“…In the application of travel time prediction, semi-parametric models are presented as varying coefficient regression models. The prediction result (travel time) was defined as a linear function of the naive historical and instantaneous predictors; however, the parameters vary depending on the departure time interval and prediction horizon (Schmitt and Jula, 2007).…”

Section: Literature Reviewmentioning

confidence: 99%

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Fan

2021

SRT

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Purpose Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways. Design/methodology/approach As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways. Findings The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions. Originality/value This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

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Section: Literature Reviewmentioning

confidence: 99%

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Fan

2021

SRT

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“…Ref. [26] identifies random forests (RF) as the best approach for predicting travel times on urban street segments using GPS data from 300 probe vehicles. In a similar use-case to ref.…”

Section: Literature Reviewmentioning

confidence: 99%

Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

2019

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Accurate travel time prediction is of high value for freight transports, as it allows supply chain participants to increase their logistics quality and efficiency. It requires both sufficient input data, which can be generated, e.g., by mobile sensors, and adequate prediction methods. Machine Learning (ML) algorithms are well suited to solve non-linear and complex relationships in the collected tracking data. Despite that, only a minority of recent publications use ML for travel time prediction in multimodal transports. We apply the ML algorithms extremely randomized trees (ExtraTrees), adaptive boosting (AdaBoost), and support vector regression (SVR) to this problem because of their ability to deal with low data volumes and their low processing times. Using different combinations of features derived from the data, we have built several models for travel time prediction. Tracking data from a real-world multimodal container transport relation from Germany to the USA are used for evaluation of the established models. We show that SVR provides the best prediction accuracy, with a mean absolute error of 17 h for a transport time of up to 30 days. We also show that our model performs better than average-based approaches.

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Machine Learning on Big Data: A Developmental Approach on Societal Applications

Sơn

Tripathy²,

Acharya³

et al. 2018

Studies in Big Data

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Analysed potential of big data and supervised machine learning techniques in effectively forecasting travel times from fused data

Abstract: ABSTRACT

Cited by 9 publications

References 29 publications

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Travel time forecasting on a freeway corridor: a dynamic information fusion model based on the random forests approach

Travel Time Prediction in a Multimodal Freight Transport Relation Using Machine Learning Algorithms

Machine Learning on Big Data: A Developmental Approach on Societal Applications

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