ENHANCING TRAFFIC SAFETY: A COMPREHENSIVE APPROACH THROUGH REAL-TIME DATA AND INTELLIGENT TRANSPORTATION SYSTEMS by Khanh Giang LE

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Machine learning of swimming data via wisdom of crowd and regression analysis by Jiang Xie, Junfu Xu, Celine Nie, Qing Nie

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Roma Eterna? Roman rule explains regional well-being divides in Germany by Martin Obschonka, Fabian Wahl, Michael Fritsch, Michael Wyrwich, P. Jason Rentfrow, Jeff Potter, Samuel D. Gosling

“…Additional analyses suggest that Roman investments in economic institutions (e.g., trade infrastructure such as Roman roads, markets, and mines) were crucial in creating this long-term effect. …”
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Air pollution Dynamics: The role of meteorological factors in PM10 concentration patterns across urban areas by Carolina Girotti, Luiz Fernando Kowalski, Tiago Silva, Ezequiel Correia, Alessandra R. Prata Shimomura, Fernando Akira Kurokawa, António Lopes

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Surveillance experiences of extinction rebellion activists and police: Unpacking the technologization of Dutch protest policing by Majsa Storbeck, Gabriele Jacobs, Marc Schuilenburg, Robin van den Akker

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LAND SUITABILITY ASSESSMENT FOR SOYBEAN (GLYCINE MAX) IN BARDAGHAT MUNICIPALITY USING GIS TECHNIQUES by Birendra Tiwar, Amrit Dumre, Mahesh Jaishi

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LAND SUITABILITY ANALAYSIS OF SESAME CROP USING GIS TECHNIQUES by Ramesh Kumar Ghimire, Anuj Dhakal, Amir Pandit, Pragyan Ghimire, Sagar Pokharel

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Multivariate Deep Learning Approach for Electric Vehicle Speed Forecasting by Youssef Nait Malek, Mehdi Najib, Mohamed Bakhouya, Mohammed Essaaidi

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Multi-Scale Feature Fusion Model for Bridge Appearance Defect Detection by Rong Pang, Yan Yang, Aiguo Huang, Yan Liu, Peng Zhang, Guangwu Tang

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IMPACT ON THE LIVELIHOOD OF RURAL FARMERS DUE TO THE ADOPTION OF MODERN AGRICULTURAL TOOLS AND TECHNOLOGY IN NEPAL by Binisha Paudel, Sunil Regmi

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PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data by Shan Zhang, Qinkai Jiang, Hao Li, Bin Cao, Jing Fan

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Erratum to “Development and validation of machine learning based prediction model for postoperative pain risk after extraction of impacted mandibular third molars” [Heliyon 9(12),... by Dongsheng Yu, Zifeng Liu, Weijie Zhuang, Kechen Li, Yaxin Lu

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Application of Customer Segmentation for Electronic Toll Collection: A Case Study by Chao Qian, Meng Yang, Peiqi Li, Shuguang Li

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Blind Corner Propagation Model for IEEE 802.11p Communication in Network Simulators by Sanchai Jaktheerangkoon, Kulit Na Nakorn, Kultida Rojviboonchai

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Assessing nighttime artificial light pollution from the perspective of an unmanned aerial vehicle tilt by Jiejie Wu, Liang Zhou, Deping Li, Daoquan Zhang, Tingting Jiang, Chengzhi Zong

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Graph neural network driven traffic prediction technology:review and challenge by Yi ZHOU, Shuting HU, Wei LI, Nan CHENG, Ning LU, Xuemin(Sherman) SHEN

“…With the rapid development of Internet of things and artificial intelligence technology, accurate analysis and prediction of traffic data have become the primary target of intelligent transportations.In recent years, the method of traffic forecasting has gradually changed from the classical model-driven type to the data-driven type.However, how to effectively analyze the spatial-temporal characteristics of road networks through big data is one of the key issues in the traffic prediction process.Spatiotemporal big data analysis is a powerful tool for the traffic prediction.The traffic network can be modeled as a graph network, while the deep learning method can be extended on the graph network.Utilizing graph neural networks, we can build the spatiotemporal prediction model, and obtain the spatial-temporal correlation between the sensor nodes in road networks effectively by using graph convolution, which can significantly improve the accuracy of traffic prediction models.The traffic forecasting technology driven by graph neural networks was explored, and two kinds of traffic prediction models based on the analysis of deep spatial-temporal characteristics were extracted.The actual cases were analyzed and evaluated to discuss the technical advantages and key challenges of graph neural networks in the traffic prediction.The potential issues of graph neural network driven prediction mechanisms were also excavated.…”
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Estimation of Hourly Traffic Flows from Floating Car Data for Vehicle Emission Estimation by Yun Jiang, Guohua Song, Zeyu Zhang, Zhiqiang Zhai, Lei Yu

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Uncertainty Analysis of Knowledge Reductions in Rough Sets by Ying Wang, Nan Zhang

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Santarém, entre la Amazonia de los ríos y la Amazonia de las carreteras by Rubén Valbuena

“…Santarém, a historical city with a strategic localization between the two greater Amazonian urban centres, has been the theatre of enormous transformations since the construction of the road. The connection, via the Cuibá-Santarém road, with the Center-West region of Brazil and, via Transamazonian road, with the Brazilian North-East and Western Amazonia, started to play a significant role as articulator of the Northern area. …”
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Reservoir water level decline accelerates ground subsidence: InSAR monitoring and machine learning prediction of surface deformation in the Three Gorges Reservoir area by Jiaer Yang, Jiaer Yang, Pinglang Kou, Pinglang Kou, Xu Dong, Xu Dong, Ying Xia, Qinchuan Gu, Yuxiang Tao, Jiangfan Feng, Qin Ji, Weizao Wang, Ram Avtar

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