Unstructured road semantic segmentation method based on 3D point cloud of open pit mine by Caiwu LU, Jianan XUE, Meng LI, Shengyu YAN, sai ZHANG, Song JIANG, Runfeng HE

“…In order to solve the problems of small size of publicly available unstructured road datasets, uneven sample distribution, and low segmentation accuracy of mainstream point cloud semantic segmentation algorithms for unstructured roads, a semantic segmentation method for unstructured roads in open-pit mine scenarios is proposed by constructing an open-pit mine point cloud dataset through 3D point cloud reconstruction, as well as by optimizing and improving the PointNet++ algorithm. …”
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Study of the Fatigue Life and Weight Optimization of an Automobile Aluminium Alloy Part under Random Road Excitation by A. Saoudi, M. Bouazara, D. Marceau

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Enhanced Rapid Mangrove Habitat Mapping Approach to Setting Protected Areas Using Satellite Indices and Deep Learning: A Case Study of the Solomon Islands by Hyeon Kwon Ahn, Soohyun Kwon, Cholho Song, Chul-Hee Lim

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Сontrol of Intelligent Transport System in Minsk by D. V. Kapskiy, D. V. Navoy, P. A. Pegin

“…The paper considers algorithms for searching a maximum traffic volume of road vehicles in a traffic light cycle with a distributed intensity pulse and optimization of shifts under coordinated traffic flow control. …”
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Analytical Methods and Determinants of Frequency and Severity of Road Accidents: A 20-Year Systematic Literature Review by Carlos M. Ferreira-Vanegas, Jorge I. Vélez, Guisselle A. García-Llinás

“…By introducing a new approach based on computational algorithms and data visualization, this SLR fills a gap in the area of RTA analysis and provides a clear picture of the current scientific production in the field. …”
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Digital Reconstruction Method for Low-Illumination Road Traffic Accident Scenes Using UAV and Auxiliary Equipment by Xinyi Zhang, Zhiwei Guan, Xiaofeng Liu, Zejiang Zhang

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A Comprehensive Framework for Transportation Infrastructure Digitalization: TJYRoad-Net for Enhanced Point Cloud Segmentation by Zhen Yang, Mingxuan Wang, Shikun Xie

“…Two lightweight surface reconstruction techniques are implemented: (1) algorithmic reconstruction, which delivers a 6.3 mm elevation error at 95% confidence in complex intersections, and (2) template matching, which replaces road markings, poles, and vegetation using bounding boxes. …”
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A 5G Roadside Infrastructure Assisting Connected and Automated Vehicles in Vulnerable Road User Protection by Raffaele Viterbo, Federico Campolo, Mattia Cerutti, Satyesh Shanker Awasthi, Stefano Arrigoni, Mattia Brambilla, Monica Nicoli

“…In this work we propose the design, development, and assessment on road of a 5G roadside infrastructure for VRU protection, performing an in-depth analysis of the single components and their integration into the service. …”
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Distress-Based Pavement Condition Assessment Using Artificial Intelligence: A Case Study of Egyptian Roads by Mostafa M. Radwan, Sundus A. Faris, Ahmed Y. Barakat, Ahmad Mousa

“…This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. …”
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Mathematical model of a lightweight three-axle off-road vehicle construction for Arctic zone of Russia by I. E. Agureev, V. N. Bondarenko

“…The model and results of calculating the smoothness of a light three-axle off-road vehicle for the Arctic zone of Russia are considered. …”
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SegRoadv2: a hybrid deformable self-attention and convolutional network for road extraction with connectivity structure by Zhengbo Yu, Zhe Chen, Keyan Xiao, Xiangqi Lei, Rui Tang, Qiaoran He, Zhongchang Sun, Huadong Guo

“…Road extraction is crucial for navigation, autonomous driving, and smart city development. …”
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An Alternative Fuel Refueling Station Location Model considering Detour Traffic Flows on a Highway Road System by Seong Wook Hwang, Sang Jin Kweon, Jose A. Ventura

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CrackNet: A new deep learning-based strategy for automatic classification of road cracks after earthquakes by Fatih Demir, Erkut Yalcin, Mehmet Yilmaz

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Graphlet decomposition dataset of Tallinn’s road network from January 2020 OpenStreetMap dataGithub by Mahdi Rasoulinezhad, Nasim Eslamirad, Jenni Partanen

“…This paper presents a comprehensive dataset of graphlet decomposition for the road network of Tallinn, Estonia, based on OpenStreetMap (OSM) data representing the road network state as of 1 January 2020. …”
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Optimization of energy management strategies for multi-mode hybrid electric vehicles driven by travelling road condition data by Hongxia Wang, Cheng Chang, Zhiyi Pan, Xuewen Zhai, Hanwu Liu, Supeng Zhang, Yubo Liu

“…Then, an EMS optimized based on road condition information (RC-EMS) was developed according to the operating curves and interval thresholds of motors and engine. …”
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Rethinking Metaheuristics: Unveiling the Myth of “Novelty” in Metaheuristic Algorithms by Chia-Hung Wang, Kun Hu, Xiaojing Wu, Yufeng Ou

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Logistic approach to the regional route network optimization by P. V. Ivanova, M. V. Sukhanova

“…A bus traffic model from federal and regional roads to the bus station down the city's road network has been developed. …”
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Application of Genetic Algorithm to Hexagon-Based Motion Estimation by Chih-Ming Kung, Wan-Shu Cheng, Jyh-Horng Jeng

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Robust Road Surface Classification Using Time Series Augmented Intelligent Tire Sensor Data and 1-D CNN by Seokchan Kim, Yeong-Jae Kim, Dongwook Lee, Hanmin Lee

“…In recent years, there has been a lot of research on using the vibration characteristic of tires to estimate the road surface condition from its features. However, since tire vibration characteristics vary depending on conditions such as tire pressure, load, and driving status, it is still difficult to develop a road surface classification algorithm that is robust to various situations. …”
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Real-time traffic monitoring system using IoT-aided robotics and deep learning techniques by Mohammed Qader Kheder, Aree Ali Mohammed

“…Furthermore, optimal distance was found between the ultrasonic sensors and the obstacles using ultrasonics’ waves time and speed to reduce road accidents. The data, which is collected by sensors and cameras, is processed using various image processing algorithms and it is sent to the cloud to be available for drivers and commuters through a mobile application. …”
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