Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Karballaeezadeh, Nader; Zaremotekhases, Farah; Shamshirband, Shahaboddin; Mosavi, Amir; Nabipour, Narjes; Csiba, Péter; Várkonyi-Kóczy, Annamária R.

doi:10.3390/en13071718

Cited by 47 publications

(23 citation statements)

References 75 publications

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“…Another use of AI-based methods will be to develop intelligent road inspection systems for smart mobility and transportation maintenance systems [76].…”

Section: Limitations Of This Studymentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

141

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Industry 4.0 concepts and technologies ensure the ongoing development of micro- and macro-economic entities by focusing on the principles of interconnectivity, digitalization, and automation. In this context, artificial intelligence is seen as one of the major enablers for Smart Logistics and Smart Production initiatives. This paper systematically analyzes the scientific literature on artificial intelligence, machine learning, and deep learning in the context of Smart Logistics management in industrial enterprises. Furthermore, based on the results of the systematic literature review, the authors present a conceptual framework, which provides fruitful implications based on recent research findings and insights to be used for directing and starting future research initiatives in the field of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in Smart Logistics.

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“…Another use of AI-based methods will be to develop intelligent road inspection systems for smart mobility and transportation maintenance systems [76].…”

Section: Limitations Of This Studymentioning

confidence: 99%

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Woschank¹,

Rauch

Zsifkovits³

2020

Sustainability

141

View full text Add to dashboard Cite

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“…Today there are mathematical optimization techniques as, for example, Newton, Quasi-Newton methods and Gauss-Newton techniques already used or to be further used in relation with transportation, e.g. in assignment models to calibrate the traffic and transit; see: Karballaeezadeh et al (2020), Ticala and Balog (2008), Kamel et al (2019). Optimization.…”

Section: What Is Multimodal Transportation?mentioning

confidence: 99%

Selective Survey: Most Efficient Models and Solvers for Integrative Multimodal Transport

Matei¹,

Erdei²,

Pintea³

2021

Informatica

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In the family of Intelligent Transportation Systems (ITS), Multimodal Transport Systems (MMTS) have placed themselves as a mainstream transportation mean of our time as a feasible integrative transportation process. The Global Economy progressed with the help of transportation. The volume of goods and distances covered have doubled in the last ten years, so there is a high demand of an optimized transportation, fast but with low costs, saving resources but also safe, with low or zero emissions. Thus, it is important to have an overview of existing research in this field, to know what has already been done and what is to be studied next. The main objective is to explore a beneficent selection of the existing research, methods and information in the field of multimodal transportation research, to identify industry needs and research gaps and provide context for future research. The selective survey covers multimodal transport design and optimization in terms of: cost, time, and network topology. The multimodal transport theoretical aspects, context and resources are also covering various aspects. The survey's selection includes currently existing best methods and solvers for Intelligent Transportation Systems (ITS). The gap between theory and real-world applications should be further solved in order to optimize the global multimodal transportation system.

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“…The PCI is a numerical index that evaluates pavement conditions based on pavement surface distresses and demonstrates structural integrity and surface operational conditions. This index's value varies from zero to 100, with zero representing the worst conditions and 100 showing perfect conditions [11,47]. ASTM D6433-18 [48] evaluates pavement condition based on PCI, as displayed in Table 3.…”

Section: Pavement Condition Index (Pci)mentioning

confidence: 99%

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

et al. 2020

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The pavement is a complex structure that is influenced by various environmental and loading conditions. The regular assessment of pavement performance is essential for road network maintenance. International roughness index (IRI) and pavement condition index (PCI) are well-known indices used for smoothness and surface condition assessment, respectively. Machine learning techniques have recently made significant advancements in pavement engineering. This paper presents a novel roughness-distress study using random forest (RF). After determining the PCI and IRI values for the sample units, the PCI prediction process is advanced using RF and random forest trained with a genetic algorithm (RF-GA). The models are validated using correlation coefficient (CC), scatter index (SI), and Willmott’s index of agreement (WI) criteria. For the RF method, the values of the three parameters mentioned were −0.177, 0.296, and 0.281, respectively, whereas in the RF-GA method, −0.031, 0.238, and 0.297 values were obtained for these parameters. This paper aims to fulfill the literature’s identified gaps and help pavement engineers overcome the challenges with the conventional pavement maintenance systems.

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Intelligent Road Inspection with Advanced Machine Learning; Hybrid Prediction Models for Smart Mobility and Transportation Maintenance Systems

Cited by 47 publications

References 75 publications

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics

Selective Survey: Most Efficient Models and Solvers for Integrative Multimodal Transport

Smart Structural Health Monitoring of Flexible Pavements Using Machine Learning Methods

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