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Reliable motion prediction of surrounding vehicles is the key to safe and efficient driving of autonomous vehicles, especially at urban intersections with complex traffic environments. This study models driving intentions and future driving speeds at urban intersections and improves model prediction performance by considering traffic environment characteristics. Key feature parameters including environmental characteristics are first extracted through driving behavior analysis and existing research experience. Then models with different input combinations are constructed to explore the effectiveness of different factors in predicting driving intention and future speed. In particular, in vehicle speed modeling, a target detection algorithm is used to identify traffic participants. Based on the identified traffic participant and vehicle position information, a new method for speed prediction that can reflect the dynamic interaction characteristics between the driver and the traffic environment is proposed. Models are trained and tested using natural driving data from China. Finally, the models with the simplest input and the best effect are determined. The driving intention recognition model can accurately predict the driving maneuvers of straight-Ahead, stopping, turning left and right 4 seconds before reaching the intersection. The speed prediction model can significantly improve the speed prediction accuracy, and shows stronger robustness and adaptability than existing models. This research provides important technical support for developing intelligent driving systems suitable for complex urban traffic environments., QC 20240709

In order to improve the performance of speed prediction in the state of free driving at urban intersections, a new method for speed prediction that considers the interaction characteristics of the host vehicle with other traffic participants is proposed. First, a vehicle target classification method is proposed to distinguish the driving direction of other vehicles relative to the host vehicle, and the target detection algorithm YOLOv5 is used to identify potential traffic conflicts and vulnerable traffic participants. Then, the identified traffic participant and historical speed are combined to establish a speed prediction model based on long short-term memory network. The effectiveness of traffic participant information in improving speed prediction performance is verified in three different driving scenarios, i.e. left turn, right turn and straight. The results show that compared with the baseline model that only takes historical speed as input, the speed prediction model considering traffic participants shows better performance. It solves the problem of the gradual decline in the accuracy of the prediction model in a prediction domain, and shows stronger adaptability to the complex traffic environment of urban intersections., QC 20230829

Road link speed is often employed as an essential measure of traffic state in the operation of an urban traffic network. Not only real-time traffic demand but also signal timings and other local planning factors are major influential factors. This paper proposes a short-term traffic speed prediction approach, called PL-WGAN, for urban road networks, which is considered an important part of a novel parallel learning framework for traffic control and operation. The proposed method applies Wasserstein Generative Adversarial Nets (WGAN) for robust data-driven traffic modeling using a combination of generative neural network and discriminative neural network. The generative neural network models the road link features of the adjacent intersections and the control parameters of intersections using a hybrid graph block. In addition, the spatial-temporal relations are captured by stacking a graph convolutional network (GCN), a recurrent neural network (RNN), and an attention mechanism. A comprehensive computational experiment was carried out including comparing model prediction and computational performances with several state-of-the-art deep learning models. The proposed approach has been implemented and applied for predicting short-term link traffic speed in a large-scale urban road network in Hangzhou, China. The results suggest that it provides a scalable and effective traffic prediction solution for urban road networks., QC 20221107

Traffic calming measures (TCM) are placed in urban areas to improve road safety, and among them, vertical TCMs are widely employed. Many researches are focused on the influence of the geometry of each measure on speed reduction, but it is demonstrated that drivers forget its effect and speed up after it. Therefore, placing consecutive TCMs can help to maintain a safe area. However, scarce literature can be found about the adequate spacing between them. Hence, the aim of this paper is to analyze the adequate distance between TCMs. Various streets with variable distances and different vertical TCMs were evaluated in Poland and Spain, including raised crosswalks, raised intersections, speed humps and speed cushions. The intermediate point between two TCMs was selected as the place where the maximum speed is achieved. Results showed that there was a good correlation between the speeds at intermediate points and the distance between TCMs, with a determination coefficient around 0.80. For an 85th percentile of the speed under 50 km/h, a maximum distance of 200 m between TCMs is recommended, and for a value of 40 km/h, 75 m.

Offshore oil and gas industry. they transport, install and remove all types of fixed and floating structures. for this purpose, hmc operates four crane vessels of which three are semi-submersibles (sscv). sscvs are characterized by two floaters with three or four columns on each floater supporting a single body upper hull structure. recently, the new vessel, sleipnir, has been built in order to overcome the next generation challenges of the sector, being able to lift 20.000tn with its two cranes. during transits and operations, cargo and equipment are fitted on deck of sscvs or on a barge, if it does not fit. however, nowadays it is becoming a possibility to transport a cargo (jacket, or topsides) while being suspended on the cranes. it is therefore of interest to know how will the crane vessels respond to this situation, as regards to motions and available speed. the objective of this thesis will be to give a prediction of the total resistance and the speed of the new vessel sleipnir at different scenarios. unlike other sscv, sleipnir has been designed so as it can sail by own propulsion so before this craft starts working on future projects, hmc desires to be able of predicting the different speeds that it will get for different scenarios of draught, waves and wind, when transporting a cargo while being suspended. the scope of doing this study is for the company to know how much time will be spending on sailing for different weather and cargo conditions of the vessel, and therefore to be able to predict the timing better. in order to do the calculations for this project, data from the preconstruction studies has been analyzed and cross checked with the speed trials. these studies, performed years ago, did not account on the possibility to perform such transit situations since it has a transit draught of 12m and this one only has been considered at detailed design stage. therefore, these studies did not give a speed prediction for it.

In this paper, the suitability and performance of ANFIS (adaptive neuro-fuzzy inference system), ANFIS-PSO (particle swarm optimization), ANFIS-GA (genetic algorithm) and ANFIS-DE (differential evolution) has been investigated for the prediction of monthly and weekly wind power density (WPD) of four different locations named Mersing, Kuala Terengganu, Pulau Langkawi and Bayan Lepas all in Malaysia. For this aim, standalone ANFIS, ANFIS-PSO, ANFIS-GA and ANFIS-DE prediction algorithm are developed in MATLAB platform. The performance of the proposed hybrid ANFIS models is determined by computing different statistical parameters such as mean absolute bias error (MABE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). The results obtained from ANFIS-PSO and ANFIS-GA enjoy higher performance and accuracy than other models, and they can be suggested for practical application to predict monthly and weekly mean wind power density. Besides, the capability of the proposed hybrid ANFIS models is examined to predict the wind data for the locations where measured wind data are not available, and the results are compared with the measured wind data from nearby stations.

© 2012 Elsevier Ltd. All rights reserved. This work has been partially supported by the Spanish Ministry of Education through Project ECO2010-22065-C03-02, and by Iberdrola Renovables Energía S.A. Special thanks are given to Prof. Emiliano Hernández Martín from Universidad Complutense de Madrid and to Prof. Ricardo Trigo from Universidade de Lisboa., A wind clustering methodology capable of dynamically characterizing and long-term reconstructing daily surface wind series is introduced and tested for six meteorological towers at different wind farms in Spain, for the period 1871–2009. On this basis this paper provides for the first time a centennial surface wind reconstruction with a daily resolution without the need of numerical simulations. Thus, several soft-computing algorithms are developed, with public domain Sea Level Pressure (SLP) Reanalysis data as the only input. These algorithms are constructed by tackling an Euclidean distances’ problem at the geostrophic speeds’ space. Once the wind-independent classifications are obtained, the methodology is calibrated by linking the obtained classifications with observed wind data, thus allowing to estimate and characterize the daily surface wind speed and direction. A cross-validation is then performed in order to obtain several measures of goodness of the method, such as its wind speed estimation uncertainty in terms of Mean Absolute Error (MAE) and Pearson correlation (r) for both the wind module and vectorial values. Regarding previous approaches, this statistic downscaling shows an outstanding performance: Wind speed module estimates produce a MAE of 1.12 m/s (0.32 m/s) in some towers for a daily (monthly) scale, as r reaches values of 0.78 (daily scale) and 0.91 (monthly scale). The wind-independent classifications allowed to perform daily surface wind speed and rose reconstructions in time periods when no wind data are available, which constitutes the main goal of this work. Thus, a 140 year daily wind reconstruction is performed and analyzed for one tower located at central Iberia. There, significant low frequency variations are detected, as well as wind speed oscillations in the 20 y band. Remarkable changes are also identified over reconstructed decadal wind speed frequency distributions and wind rose. Since long-term wind measurements are rarely availab, Ministerio de Economía y Competitividad (MINECO), Iberdrola Renovables Energía S.A., Depto. de Física de la Tierra y Astrofísica, Fac. de Ciencias Físicas, TRUE, pub

In recent years, context-sensitive highway design has been promoted to ensure that designers consider the environmental, scenic, aesthetic, historic, community, and preservation aspects of the road. Context-sensitive design may lead to situations where the current design standards cannot be met because of restricting local conditions. Indiana has road sections designed and built some time ago. In a considerable number of roads with the statutory limit of 55 mph (90 km/h), the road geometry does not meet the current standards. At individual intersections and on curves, advisory speeds are posted together with warning signs. Although this solution increases the safety of road users and allows for traveling at reasonably high speeds outside of these segments, the final solution is to upgrade their geometry to the desirable level. This report presents models that predict user-selected percentile free-flow speeds on two-lane rural and four-lane rural and suburban highways. The percentile speeds are computed as a linear combination of the mean speed and the standard deviation in panel data models with random effects. The developed percentile speed models involve more design variables than typical speed models, and separate the mean speed factors from the speed dispersion factors. These benefits ease the model interpretation and its use in highway design. The study results should help designers bring the predicted speed to the desired speed as close as possible given the budget constraints. Engineering judgment can then be applied to balance safety and construction cost in highway improvement projects.