Relevant bibliographies by topics / Traffic load prediction

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'Traffic load prediction'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Traffic load prediction"

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Alnadish, Adham Mohammed, Madhusudhan Bangalore Ramu, Abdullah O. Baarimah, and Aawag Mohsen Alawag. "Development of Enhanced Stress Prediction Models for Fixed Traffic Loads on Flexible Pavements Based on Response Surface Methodology (RSM) and Machine Learning (ML) Techniques." Applied Sciences 15, no. 3 (2025): 1623. https://doi.org/10.3390/app15031623.

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Pavement design is influenced by traffic load, which affects its lifespan. Traditional methods classify traffic load into fixed traffic, fixed vehicle, variable traffic, and vehicle/axle loads. In fixed traffic, pavement thickness is based on the maximum expected wheel load without considering load repetitions. Meanwhile, in fixed vehicle scenarios, it is calculated by the repetitions of a standard axle load. For nonstandard axle loads, the equivalent axle load is determined by multiplying repetitions by the corresponding equivalent load factor. In variable traffic, each axle and its repetitio
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Mahdy, Basma, Hazem Abbas, Hossam Hassanein, Aboelmagd Noureldin, and Hatem Abou-zeid. "A Clustering-Driven Approach to Predict the Traffic Load of Mobile Networks for the Analysis of Base Stations Deployment." Journal of Sensor and Actuator Networks 9, no. 4 (2020): 53. http://dx.doi.org/10.3390/jsan9040053.

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Mobile network traffic is increasing in an unprecedented manner, resulting in growing demand from network operators to deploy more base stations able to serve more devices while maintaining a satisfactory level of service quality. Base stations are considered the leading energy consumer in network infrastructure; consequently, increasing the number of base stations will increase power consumption. By predicting the traffic load on base stations, network optimization techniques can be applied to decrease energy consumption. This research explores different machine learning and statistical metho
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Zhang, Yangan, Xiaoyu Zhang, Peng Yu, and Xueguang Yuan. "Machine Learning with Adaptive Time Stepping for Dynamic Traffic Load Prediction in 6G Satellite Networks." Electronics 12, no. 21 (2023): 4473. http://dx.doi.org/10.3390/electronics12214473.

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The rapid development of sixth-generation (6G) mobile broadband networks and Internet of Things (IoT) applications has led to significant increases in data transmission and processing, resulting in severe traffic congestion. To better allocate network resources, predicting network traffic has become crucial. However, satellite networks face global imbalances in IoT traffic demand, with substantial variations in satellite density and load distribution within the same constellation. These disparities render traditional traffic prediction algorithms inadequate for dynamically changing satellite n
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Long, Hao, Feng Hu, and Lingjun Kong. "Enhanced Link Prediction and Traffic Load Balancing in Unmanned Aerial Vehicle-Based Cloud-Edge-Local Networks." Drones 8, no. 10 (2024): 528. http://dx.doi.org/10.3390/drones8100528.

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With the advancement of cloud-edge-local computing, Unmanned Aerial Vehicles (UAVs), as flexible mobile nodes, offer novel solutions for dynamic network deployment. However, existing research on UAV networks faces substantial challenges in accurately predicting link dynamics and efficiently managing traffic loads, particularly in highly distributed and rapidly changing environments. These limitations result in inefficient resource allocation and suboptimal network performance. To address these challenges, this paper proposes a UAV-based cloud-edge-local network resource elastic scheduling arch
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Wu, Fuzhang, Fan Zhang, Jianlin Tang, and Fang Chi. "Prediction of the Spatial-Temporal Distribution of Charging Loads Considering Travel Behaviors of Electric Vehicles." Journal of Physics: Conference Series 3012, no. 1 (2025): 012050. https://doi.org/10.1088/1742-6596/3012/1/012050.

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Abstract The analysis of the spatial-temporal distribution of electric vehicle (EV) charging loads serves as the cornerstone for estimating charging resource supply and demand, as well as scheduling charging loads. The travel behavior of EVs is intrinsic to the charging load, and therefore, it is essential to consider this behavior in predicting charging loads. In this paper, we propose a novel charging load prediction method that takes into account the travel behavior of EVs. Firstly, we establish an EV travel decision model for users based on regret theory, which enables us to obtain the cho
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Yao, Chuting, Chenyang Yang, and Chih-Lin I. "Data-driven resource allocation with traffic load prediction." Journal of Communications and Information Networks 2, no. 1 (2017): 52–65. http://dx.doi.org/10.1007/s41650-017-0005-y.

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Kharat, J. P. "Comparative Study of Various Neural Network Architectures for MPEG-4 Video Traffic Prediction." International Journal of Advances in Applied Sciences 6, no. 4 (2017): 283. http://dx.doi.org/10.11591/ijaas.v6.i4.pp283-292.

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<p>Network traffic as it is VBR in nature exhibits strong correlations which make it suitable for prediction. Real-time forecasting of network traffic load accurately and in a computationally efficient manner is the key element of proactive network management and congestion control. This paper comments on the MPEG-4 video traffic predictions evaluated by different types of neural network architectures and compares the performance of the same in terms of mean square error for the same video frames. For that three types of neural architectures are used namely Feed forward, Cascaded Feed fo
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Tu, Teng, Yihao Ji, and Xuanyu Chen. "Prediction of Electric Load Neural Network Prediction Model for Big Data." Highlights in Science, Engineering and Technology 68 (October 9, 2023): 47–54. http://dx.doi.org/10.54097/hset.v68i.11934.

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The problem of traffic enforcement scheduling has become one of the key concerns of the government in recent years. How to carry out scientific and efficient speed measurement activities to ensure the safety of road traffic has therefore become the direction of the article's research. The paper selects accident-prone and speeding-prone locations as checkpoints, dividing them into three zones. The optimal speeding check time period is determined to be 18:00-23:00. To increase officers' efficiency, the problem is transformed into a TSP and solved using the Immunity Algorithm. The article defines
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Feng, Jiangpeng, Xiqiang Chang, Yanfang Fan, and Weixiang Luo. "Electric Vehicle Charging Load Prediction Model Considering Traffic Conditions and Temperature." Processes 11, no. 8 (2023): 2256. http://dx.doi.org/10.3390/pr11082256.

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The paper presents a novel charging load prediction model for electric vehicles that takes into account traffic conditions and ambient temperature, which are often overlooked in conventional EV load prediction models. Additionally, the paper investigates the impact of disordered charging on distribution networks. Firstly, the paper creates a traffic road network topology and speed-flow model to accurately simulate the driving status of EVs on real road networks. Next, we calculate the electric vehicle power consumption per unit kilometer by considering the effects of temperature and vehicle sp
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Jia, Miao, Jinliang Xu, Chao Gao, Minghao Mu, and Guangxun E. "Long-Term Cross-Slope Variation in Highways Built on Soft Soil under Coupling Action of Traffic Load and Consolidation." Sustainability 15, no. 1 (2022): 33. http://dx.doi.org/10.3390/su15010033.

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The variation in road cross slope with service life affects the pavement drainage and has an adverse effect on vehicle operation safety. This paper describes a cross-slope variation prediction method influenced by the coupling effect of traffic load and soil consolidation, considering characteristics of embankment to cover the shortage for insufficient consideration of compacted embankment. First, the traffic load-induced settlement equation of a highway on soft soil foundation was introduced, which considers the effects of traffic load stress, confining pressure, soil structure, strength weak
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Dissertations / Theses on the topic "Traffic load prediction"

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Wallentinsson, Emma Wallentinsson. "Multiple Time Series Forecasting of Cellular Network Traffic." Thesis, Linköpings universitet, Statistik och maskininlärning, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-154868.

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The mobile traffic in cellular networks is increasing in a steady rate as we go intoa future where we are connected to the internet practically all the time in one wayor another. To map the mobile traffic and the volume pressure on the base stationduring different time periods, it is useful to have the ability to predict the trafficvolumes within cellular networks. The data in this work consists of 4G cellular trafficdata spanning over a 7 day coherent period, collected from cells in a moderately largecity. The proposed method in this work is ARIMA modeling, in both original formand with an ex
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Xirouchakis, Michail. "Traffic Load Predictions Using Machine Learning : Scale your Appliances a priori." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-254906.

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Layer 4-7 network functions (NF), such as Firewall or NAPT, have traditionally been implemented in specialized hardware with little to no programmability and extensibility. The scientific community has focused on realizing this functionality in software running on commodity servers instead. Despite the many advancements over the years (e.g., network I/O accelerations), software-based NFs are still unable to guarantee some key service-level objectives (e.g., bounded latency) for the customer due to their reactive approach to workload changes. This thesis argues that Machine Learning techniques
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Book chapters on the topic "Traffic load prediction"

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Tran, Khai Phan, Weitong Chen, and Miao Xu. "Improving Traffic Load Prediction with Multi-modality." In Lecture Notes in Computer Science. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-97546-3_21.

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Nyaramneni, Sarika, Md Abdul Saifulla, and Shaik Mahboob Shareef. "ARIMA for Traffic Load Prediction in Software Defined Networks." In Evolutionary Computing and Mobile Sustainable Networks. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5258-8_75.

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Bernacki, Jarosław, Kelton A. P. Costa, and Katarzyna Nieszporek. "Evaluating Neural Network Models for Accurate Prediction of Network Traffic Load." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84353-2_1.

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Pyl, L., G. Degrande, G. Lombaert, and W. Haegeman. "Experimental validation of a numerical prediction model for traffic induced vibrations by in situ experiments." In Wave propagation Moving load – Vibration Reduction. CRC Press, 2021. http://dx.doi.org/10.1201/9781003211372-27.

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Yamamoto, K., and H. Tachibana. "Road traffic noise prediction model “ASJ Model 1998” proposed by the Acoustical Society of Japan." In Wave propagation Moving load – Vibration Reduction. CRC Press, 2021. http://dx.doi.org/10.1201/9781003211372-33.

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Silva, Cláudio A. D., Carlos Grilo, and Catarina Silva. "Server Load Prediction on Wikipedia Traffic: Influence of Granularity and Time Window." In Proceedings of the Tenth International Conference on Soft Computing and Pattern Recognition (SoCPaR 2018). Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-17065-3_21.

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Jiang, Jichen, Xi Li, Hong Ji, and Heli Zhang. "Self-organized Resource Allocation Based on Traffic Prediction for Load Imbalance in HetNets with NOMA." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-78078-8_6.

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Pouzols, Federico Montesino, Diego R. Lopez, and Angel Barriga Barros. "Predictive Models of Network Traffic Load." In Studies in Computational Intelligence. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18084-2_3.

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Skrypnyk, Rostyslav, Jens Nielsen, Björn Pålsson, and Magnus Ekh. "Prediction of Long-Term Damage in Railway Crossings Accounting for Variability in Dynamic Traffic Loads." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-38077-9_44.

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Trapsilawati Fitri, Chen Chun-Hsien, and Khoo Li Pheng. "An Investigation into Conflict Resolution and Trajectory Prediction Aids for Future Air Traffic Control." In Advances in Transdisciplinary Engineering. IOS Press, 2016. https://doi.org/10.3233/978-1-61499-703-0-503.

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The continuously increasing air traffic density has become a major challenge in air traffic control (ATC) due to the current ATC systems are approaching maximum capacity. To deal with the problem, an automated conflict resolution aid (CRA) and a trajectory prediction aid (TPA) have been proposed to serve as additional safety layers in the ATC systems. However, whether the proposed automation aids are worth to be applied in the current ATC workplace and could better support air traffic controllers (ATCOs) remain unknown. This study aims to investigate the effects of the proposed automations on
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Conference papers on the topic "Traffic load prediction"

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Chen, Jiabao, Shuiqiang Li, Jinyi Chen, Jiachen Sun, Dongyang Wang, and Chao Fang. "Gated Recurrent Unit-Based Network Traffic Prediction for Load Balance." In 2024 16th International Conference on Communication Software and Networks (ICCSN). IEEE, 2024. https://doi.org/10.1109/iccsn63464.2024.10793379.

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Zou, Yu, Yufei Song, Hong Shen, Tianyi Shi, and Tiankui Zhang. "Spatial-Temporal Traffic Prediction Based Load Balancing in Cellular Networks." In 2024 IEEE 24th International Conference on Communication Technology (ICCT). IEEE, 2024. https://doi.org/10.1109/icct62411.2024.10946474.

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Su, Huan, Jie Huang, Ming Zhao, and Jinkang Zhu. "Rate Grouping Based Non-Orthogonal Random Access Aided by Online Traffic Load Prediction." In 2024 16th International Conference on Wireless Communications and Signal Processing (WCSP). IEEE, 2024. https://doi.org/10.1109/wcsp62071.2024.10827029.

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Zheng, Hongrui, Fei Jiang, Ming Liu, et al. "A spatio-temporal prediction method for charging load considering metric traffic flow simulation." In 2025 6th International Conference on Electrical, Electronic Information and Communication Engineering (EEICE). IEEE, 2025. https://doi.org/10.1109/eeice65049.2025.11034063.

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Dhanalakshmi, K. S., Kambati Lakshmi Deepak, Chinnam Sai Chandu, Palam Venkata Krishna Reddy, Nagireddy Vishnu Vardhan Reddy, and Kesanasetty Naga Manikanta. "Real-Time Traffic Load Prediction in Cellular Networks Using Cutting-Edge Machine Learning Approaches." In 2025 3rd International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT). IEEE, 2025. https://doi.org/10.1109/idciot64235.2025.10914824.

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Huang, Jiabin. "Data Center Application Traffic Load Prediction Based on DeTiDE: Decomposition Time-Series Dense Encoder." In 2024 4th International Conference on Digital Society and Intelligent Systems (DSInS). IEEE, 2024. https://doi.org/10.1109/dsins64146.2024.10992233.

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He, Mu. "Spatial-Temporal Prediction of Electric Vehicle Charging Load Based on Multi-Time Step Semi-Dynamic Traffic Equilibrium." In 2024 5th International Conference on Clean Energy and Electric Power Engineering (ICCEPE). IEEE, 2024. https://doi.org/10.1109/iccepe62686.2024.10931759.

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Shukla, Dhwanil, Nandeesh Hiremath, and Narayanan Komerath. "Aerial Commuter Architecture Using Slung Loads." In Vertical Flight Society 72nd Annual Forum & Technology Display. The Vertical Flight Society, 2016. http://dx.doi.org/10.4050/f-0072-2016-11361.

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Evading traffic congestion by personal flying vehicle is still a far fetched dream. Recent advancements in predicting divergence speeds of slung loads using the Continuous Rotation Method (CRM) of airloads measurement has made is possible to obtain complete aerodynamic load maps of objects. In turn this enables on-the-fly system identification and dynamics predictions to ensure safety and smooth rides with slung loads. A concept is proposed for an air-lift service which can transport people with their personal road vehicles over congested areas. QFD and OEC analyses are used to compare differe
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Shinohara, Keyla J. C., Paulo Junges, Roberto C. A. Pinto, Valter Z. Tani, Amir M. Valente, and Wellington L. Repette. "Evaluation on the Load-Bearing Capacity of a Reinforced Concrete Bridge Using Non-Destructive Testing." In IABSE Symposium, Tokyo 2025: Environmentally Friendly Technologies and Structures: Focusing on Sustainable Approaches. International Association for Bridge and Structural Engineering (IABSE), 2025. https://doi.org/10.2749/tokyo.2025.2536.

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<p>The increasing vehicle loads require enhanced safety monitoring in concrete structures, as they accelerate deterioration and reduce fatigue life. Strain gauges, effective in assessing structural conditions and monitoring traffic, were installed on a reinforced concrete bridge and monitored for 42 days. The sensors were attached to a Bridge Weigh-In-Motion (B-WIM) system. Traffic-induced bending moments were analysed, comparing its real influence lines to the theoretical ones. In some cases, theoretical predictions exceeded B-WIM results by about 40%, indicating potential overestimatio
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Al-Hijazeen, Asseel, and Kálmán Koris. "Smart Health Monitoring of Concrete Bridges Using Digital Twin and Ai Applications." In Concrete Structures and Technology 2024. Trans Tech Publications Ltd, 2025. https://doi.org/10.4028/p-ac7nhw.

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Safety and sustainability of reinforced concrete bridges may be increased by observing their condition during operation and thus accurately predicting their behaviour under various load conditions. This can be achieved through a monitoring system and automatic error detection based on the measured data. By detecting potential issues early on, significant damages can be prevented before they occur. Despite extensive data collection from many monitored bridges, this data often remains unprocessed and uninformative in its raw form. We aim to transform this data into a format that can help to esti
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Reports on the topic "Traffic load prediction"

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Rushing, John, Lulu Edwards, Haley Bell, and Margarita Ordaz. Rapid assessment tools for estimating trafficability of low volume roads. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49560.

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Rapid assessment of low-volume road surfaces remains a challenge when attempting to forecast allowable vehicle crossings. Variations in soil type, compaction effort, and moisture content of the soil can greatly affect trafficability, and predictive equations for soil deformation under vehicle loads often have reduced reliability for low-strength materials. Portable tools to characterize soil stiffness and corresponding relationships to load-induced deformation are needed. In this effort, researchers performed comparative testing of multiple rapid assessment tools as potential devices for givin
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Albrecht, Jochen, Andreas Petutschnig, Laxmi Ramasubramanian, Bernd Resch, and Aleisha Wright. Comparing Twitter and LODES Data for Detecting Commuter Mobility Patterns. Mineta Transportation Institute, 2021. http://dx.doi.org/10.31979/mti.2021.2037.

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Local and regional planners struggle to keep up with rapid changes in mobility patterns. This exploratory research is framed with the overarching goal of asking if and how geo-social network data (GSND), in this case, Twitter data, can be used to understand and explain commuting and non-commuting travel patterns. The research project set out to determine whether GSND may be used to augment US Census LODES data beyond commuting trips and whether it may serve as a short-term substitute for commuting trips. It turns out that the reverse is true and the common practice of employing LODES data to e
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