Truck industry classification from anonymous mobile sensor data using machine learning

Akter, Taslima; Hernandez, Sarah

doi:10.1016/j.ijtst.2021.07.001

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…They used a total of 4,884 images for training the VGG16 model (convolutional neural network). Finally, regarding the logistic context, [48] developed a supervised machine learning classifier (random forest) to predict the industry group (farm products, mining materials, chemicals, manufactured goods, and miscellaneous mixed goods) of the carried goods through the anonymous freight movement data (trip and stop sequences) extracted from global positioning system (GPS) of the transportation vehicle. In this work, we proposed a novel approach, as highlighted by the last row of Table 1.…”

Section: Related Workmentioning

confidence: 99%

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Barros

Cunha²,

Martins³

et al. 2023

IEEE Access

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The emergence of the Industry 4.0 concept and the profound digital transformation of the industry plays a crucial role in improving organisations' supply chain (SC) performance, consequently achieving a competitive advantage. The order fulfilment process (OFP) consists of one of the key business processes for the organization SC and represents a core process for the operational logistics flow. The dispatch workflow process consists of an integral part of the OFP and is also a crucial process in the SC of cement industry organizations. In this work, we focus on enhancing the order fulfilment process by improving the dispatch workflow process, specifically with respect to the cement loading process. Thus, we proposed a machine learning (ML) approach to predict weighing deviations in the cement loading process. We adopted a realistic and robust rolling window scheme to evaluate six classification models in a realworld case study, from which the random forest (RF) model provides the best predictive performance. We also extracted explainable knowledge from the RF classifier by using the Shapley additive explanations (SHAP) method, demonstrating the influence of each input data attribute used in the prediction process.INDEX TERMS Anomaly prediction, Dispatch workflow process, Machine learning, Order fulfilment process, Weighing systems.

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Section: Related Workmentioning

confidence: 99%

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Barros

Cunha²,

Martins³

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Her study found four types of overnight, urban truck tours: one pick-up followed by one delivery, multiple consecutive pick-ups followed by one delivery, one pick-up followed by multiple consecutive deliveries, and multiple consecutive pick-ups followed by consecutive deliveries. Akter and Hernandez developed a supervised machine learning model to predict industry groups from anonymous GPS data ( 22 ). Their model allows large streams of truck movement data to be leveraged for freight travel demand forecasting.…”

Section: Introductionmentioning

confidence: 99%

Freight Operational Characteristics Mined from Anonymous Mobile Sensor Data

Akter¹,

Hernandez

Camargo

2023

Transportation Research Record: Journal of the Transportation R

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Effective transportation performance measurement (TPM) benefits from ubiquitous transportation system monitoring both spatially and temporally. In the context of freight-oriented TPM, traditional devices such as inductive loops, cameras, manual counts, and so forth, may fail to provide comprehensive and high-resolution coverage, providing, for example, only volume counts for a small subset of links across a large network with no indication of trip linkages. New sources of big data from mobile sensors including on-board global positioning system (GPS) devices allow more comprehensive network coverage and insights into trip chaining behaviors. However, to gain actionable insights into system performance from large and noisy streams of mobile sensor data, it is necessary to mine it for relevant operational characteristics of the trucks it represents. Such characteristics include stop locations, stop duration, stop time of day, trip length, and trip duration. To address this methodological need, this paper presents three heuristic algorithms: “stop identification,”“path identification,” and “trip identification.” To address the issue of determining relevant operational characteristics, a multinomial logit (MNL) model approach is applied to determine the commodity carried based on the outputs of the heuristic algorithms. The MNL model is novel in that it relates operational characteristics to commodity carried thus filling a critical data gap that currently limits the development of advanced freight forecasting models. The set of models developed in this paper allow large-scale GPS data to be used for freight planning while maintaining levels of data anonymity that allow such data to be shared with public agencies.

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Spatial allocation of heavy commercial vehicles parking areas through geo-fencing

Wu,

Feng,

Jia

et al. 2024

Journal of Transport Geography

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Truck industry classification from anonymous mobile sensor data using machine learning

Cited by 3 publications

References 28 publications

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Predicting Weighing Deviations in the Dispatch Workflow Process: A Case Study in a Cement Industry

Freight Operational Characteristics Mined from Anonymous Mobile Sensor Data

Spatial allocation of heavy commercial vehicles parking areas through geo-fencing

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