The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks

Sun, Xiaoyu; Zhang, Hang; Tian, Fengliang; Yang, Lei

doi:10.1155/2018/4368045

Cited by 19 publications

(17 citation statements)

References 42 publications

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“…The literature also confirms this statement, as ML approaches usually perform better than average-based approaches [18][19][20]. Despite this situation, ML has only been applied in a minority of recent publications dealing with travel time prediction in freight transports [1,2,21,22].…”

Section: Introductionmentioning

confidence: 77%

“…The authors show that SVR performs better than ANNs and that weather data does not have a significant influence on the transport time. Finally, the authors of [21] evaluate kNN, SVR, and RF for arrival time prediction of open-pit trucks. Hereby, a site-based approach is used.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to [21,44], both ExtraTrees and AdaBoost are used for travel time prediction and have shown reasonable results with low errors. With the support of previous practical success, we also chose these two methods to build our prediction model.…”

Section: Choice Of Learning Algorithmsmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 77%

Section: Literature Reviewmentioning

confidence: 99%

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

2019

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“…Zhao et al [21] proposed a tensor completion method to recover lost RTMS speed and volume data and the optimal K -nearest neighbor algorithm is proposed for travel time prediction. Sun et al [22] used k-nearest neighbor, SVM and random forest to predict the travel time of open-pit tramcars. Zhao et al [23] proposed a multi-dimensional travel time prediction method based on toll collection data and meteorological data of highway.…”

Section: Prediction Methodsmentioning

confidence: 99%

Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

et al. 2019

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Due to the restriction of traffic management measure in large cities, large heavy-haul trucks can only travel on the circuits and expressways around the city, which often causes congestion in these areas. It is necessary to study the travel speed prediction of trucks on the urban ring road and provide special information services for trucks. Based on the data generated by the trucks driving on the Sixth Ring Road in Beijing, an optimized GRU algorithm is proposed to predict the travel speed of trucks driving on urban express roads under non-recurrent congested conditions. First, a GPS map-matching algorithm that can simultaneously meet the accuracy and efficiency requirements of matching is proposed. Then, the trucks' data traveling on the Sixth Ring Road in Beijing are extracted from the original data. Aiming at getting rid of the abnormal data in GPS data, the screening and processing rules of the abnormal data are made, and then, the traffic speed sequence is extracted. Aiming at the problem that the commonly used weight optimization algorithm SGD cannot adaptively adjust the learning rate, Adam, Adadelta, and Rmsprop are used to optimize the weights in the GRU model in this paper. Considering the four scenarios, including workday, weekend, rainy, and accident, the accuracies of the proposed methods are verified.

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“…Approaches that involve machine learning methods are used for a similar prediction task in the aviation industry [24,25] and automobile transportation [26,27]. The research results prove that ML methods work more accurately than classical forecasting methods.…”

Section: Literature Review and Problem Statementmentioning

confidence: 97%

Predicting the estimated time of cargo dispatch from a marshaling yard

Panchenko

Prokhorchenko

Panchenko

et al. 2020

EEJET

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6 chain to the destination becomes more complicated. This leads to an increase in the operating costs of a railroad system compared to railroads that follow the system of train traffic on schedule. To improve the competitiveness of a railroad system within which the traffic of trains fails to follow a timetable, it is important to build the functional for a periodic notification about the state of train dispatch. One of the important notification functions is the calculation of the estimated time of arrival (ETA) of a cargo dispatch [2]. Within the framework of the ETA calculation, which is determined for a cargo dispatch at each railroad station 1. Introduction Given the conditions of automation and digitalization of all industries, increasing the level of predictability of the technological process of a rail system transportation is a necessary requirement to reduce the cost of supply chains [1]. The operating model of railroad systems without following the schedule of freight trains possesses a considerable degree of uncertainty in the cargo transportation process. In such systems, the possibility of predicting the duration of operations involving cargo dispatch along a transportation

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The Use of a Machine Learning Method to Predict the Real-Time Link Travel Time of Open-Pit Trucks

Cited by 19 publications

References 42 publications

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

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

Truck Traffic Speed Prediction Under Non-Recurrent Congestion: Based on Optimized Deep Learning Algorithms and GPS Data

Predicting the estimated time of cargo dispatch from a marshaling yard

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