Stochastic method based on copulas for predicting severe road traffic interactions

Abstract. A method is developed to identify load-driving events based on filtered flow acceleration, regardless of the event-generating mechanism or specific temporal signature. Low-pass filtering enables calculation of acceleration statistics per characteristic turbine response time; this circumvents the classic problem of small-scale noise dominating observed accelerations or extremes, while providing a way to deal with different turbines and controllers. Not only is the flow acceleration physically meaningful, but its use also removes the need for de-trending. Through consideration of the 99th percentile (P99) of filtered acceleration per each 10 min period, we avoid assumptions about distributions of fluctuations or turbulence and derive statistics of load-driving accelerations for offshore conditions from “fast” (10 and 20 Hz) measurements spanning more than 15 years. These statistics depend on low-pass-filter frequency (the reciprocal of turbine response time) but vary in a nontrivial manner with height due to the influence of the atmospheric boundary layer's capping inversion, as well as the surface. We find long-term probability distributions of 10 min P99 of filtered accelerations, which drive loads ranging from fatigue to ultimate; this also includes joint distributions of the P99 with the 10 min mean wind speed (U) or standard deviation of horizontal wind speed fluctuations (σs). The long-term mean and mode of the P99 of streamwise acceleration, conditioned on σs and U, are found to vary monotonically with σs and U, respectively; this corroborates the International Electrotechnical Commission (IEC) 61400-1 prescriptions for fatigue design load cases. An analogous relationship is also seen between lateral (directional) acceleration and the standard deviation of direction, particularly for submesoscale fluctuations. The largest (extreme) P99 of filtered accelerations are seen to be independent of 10 min mean speeds and have only limited connection to 10 min σs; traditional 10 min statistics cannot be translated into extreme load-driving acceleration statistics. From measurement heights of 100 and 160 m, time series of the 10 most extreme acceleration events per 1 m s−1 wind speed bin were further investigated; events of diverse character were found to arise from numerous mechanisms, ranging from nonturbulent to turbulent flow regimes and also depending on the filter scale. Different behaviors were noted in the lateral and streamwise directions at different heights, although a small fraction of these events exhibited extreme amplitudes for both horizontal acceleration components and/or were observed at both heights within a given 10 min window. Via fits to the tails of the marginal P99 distributions, curves of offshore extreme P99 of filtered acceleration for return periods up to 50 years were calculated for three characteristic turbine response times (filter scales) at the observation heights of 100 and 160 m. To drive aeroelastic simulations, Mann-model parameters were also calculated from the time series of the most extreme events, allowing constrained simulations embedding the recorded events. To facilitate this for typical industrial measurements that lack three-dimensional anemometry, a new technique for obtaining Mann-model turbulence parameters was also created; this was employed to find the parameters corresponding to the background flow behind the extremes identified and their time series. Further, a method was created to use the extreme acceleration statistics in stochastic simulations for application to loads, including interpretation within the context of the IEC 61400-1 standard. Preliminary parallel work has documented aeroelastic simulations conducted using the extreme event time series identified here, as well as Monte Carlo simulations based on the extreme statistics and new method for stochastic generation of acceleration events.

Abstract. Severe convective storms are responsible for large amount of damage each year around the world. They form an important part of the climate system by redistributing heat, moisture, and trace gases, as well as producing large quantities of precipitation. As these extreme and rare events are in mesoscale there is many uncertainty in predicting them and we can’t rely on just models. On the other hand, remote sensing has a large application in Meteorology and near real time weather forecasting, especially in rare and extreme events like convective storms that might be difficult to predict with atmospheric models. On second of June 2014, near 12UTC a sudden and strong convective storm occurred in Tehran province that was not predicted, and caused economic and human losses. In This research we used satellite observations along with synoptic station measurements to predict and monitor this storm. Results from MODIS data show an increase in the amount of cloudiness and also aerosol optical depth and sudden decrease in cloud top temperature few hours before the storm occurs. EUMETSAT images show the governing of convection before the storm occurs. With combining the observation data that shows Lake of humidity and high temperature in low levels with satellite data that reveals instability in high levels that together caused this convective, we could track the storm and decrease the large amount of damage.

Understanding interactions between Automated Road Transport Systems and other road users: A video analysis

For signalized locations, delay is one of the key parameters in assessing the level of service and redesigning the traffic signals. Around the world, Highway Capacity Manual (HCM) and Webster delay models are commonly used in calculating the vehicle delay at signalized intersections, notwithstanding that these models assume homogeneous traffic conditions and lane discipline. Furthermore, it is worth noting that previous studies have mostly focused on developing delay models for signalized intersections, whereas signalized midblock crosswalks were overlooked by past researchers. In view of this, the current study presents a modified vehicle delay model for signalized midblock crosswalks (SMCs) under mixed traffic conditions. Mixed traffic conditions refers to diverse vehicles, complex interactions, and no lane discipline on the road—conditions which are usually noticed in developing nations. The HCM vehicle delay model is modified in the current study by incorporating factors related to mixed traffic conditions and SMCs. To test the accuracy of the proposed model, it was validated with the field delay using data collected at other SMCs in Hyderabad, India. Field delay was calculated by plotting the measured queue length against the signal cycle length. The performance metrics of root mean square error (RMSE), mean absolute percentage error, Nash–Sutcliffe efficiency, RMSE-observations standard deviation ratio, and percent bias were used to test the accuracy of the proposed model. Results revealed that the proposed model yielded the lowest error rates and the highest correlation coefficients between modeled and actual vehicle delay. The maximum error between the field delay and the modeled delay was found to be less than 5%.

The large proportions of pedestrian fatalities led researchers to make the improvements of pedestrian safety at intersections. Thus, this paper proposes a methodology to evaluate crosswalk safety at signalized intersections using Surrogate Safety Measures (SSM) under mixed traffic conditions. The required pedestrian, traffic, and geometric data were extracted based on the videographic survey conducted at signalized intersections in Mumbai (India). Post Encroachment Time (PET) for each pedestrian were segregated into three categories for estimating pedestrian–vehicle interactions and Cumulative Frequency Distribution (CDF) was plotted to calculate the threshold values for each interaction severity level. The Cumulative Logistic Regression (CLR) model was developed to predict the pedestrian mean PET values in the cross-walk at signalized intersections. The proposed model was validated with a new signalized intersection and the results were shown that the proposed PET ranges and model appropriate for Indian mixed traffic conditions. To assess the suitability of model framework, model transferability was carried out with data collected at signalized intersection in Kolkata (India). Finally, this study can be helpful to rank the severity level of pedestrian safety in the crosswalk and improve the existing facilities at signalized intersections.

We apply extreme value theory (EVT) to study the daily precipitation and temperature extremes in the Calabria region (southern Italy) mainly considering a long-term observational dataset (1990–2020) and also investigating the possible use of the ERA5 (ECMWF Reanalysis v5) fields. The efficiency of the EVT applied on the available observational dataset is first assessed—both through a punctual statistical analysis and return-level maps. Two different EVT methods are adopted, namely the peak-over-threshold (POT) approach for the precipitation and the block-maxima (BM) approach for the temperature. The proposed methodologies appear to be suitable for describing daily extremes both in quantitative terms, considering the punctual analysis in specific points, and in terms of the most affected areas by extreme values, considering the return-level maps. Conversely, the analysis conducted using the reanalysis fields for the same time period highlights the limitations of using these fields for a correct quantitative reconstruction of the extremes while showing a certain consistency regarding the areas most affected by extreme events. By applying the methodology on the observed dataset but focusing on return periods of 50 and 100 years, an increasing trend of daily extreme rainfall and temperature over the whole region emerges, with specific areas more affected by these events; in particular, rainfall values up to 500 mm/day are predicted in the southeastern part of Calabria for the 50-year-return period, and maximum daily temperatures up to 40 °C are expected in the next 100 years, mainly in the western and southern parts of the region. These results offer a useful perspective for evaluating the exacerbation of future extreme weather events possibly linked to climate change effects.

Extraction of stochastic and deterministic components from empirical data-necessary for the reconstruction of the dynamics of the system-is discussed. We determine both components using the Kramers-Moyal expansion. In our earlier papers, we obtained large fluctuations in the magnitude of both terms for rare or extreme valued events in the data. Calculations for such events are burdened by an unsatisfactory quality of the statistics. In general, the method is sensitive to the binning procedure applied for the construction of histograms. Instead of the commonly used constant width of bins, we use here a constant number of counts for each bin. This approach-the fixed mass method-allows to include in the calculation events, which do not yield satisfactory statistics in the fixed bin width method. The method developed is general. To demonstrate its properties, here, we present the modified Kramers-Moyal expansion method and discuss its properties by the application of the fixed mass method to four representative heart rate variability recordings with different numbers of ectopic beats. These beats may be rare events as well as outlying, i.e., very small or very large heart cycle lengths. The properties of ectopic beats are important not only for medical diagnostic purposes but the occurrence of ectopic beats is a general example of the kind of variability that occurs in a signal with outliers. To show that the method is general, we also present results for two examples of data from very different areas of science: daily temperatures at a large European city and recordings of traffics on a highway. Using the fixed mass method, to assess the dynamics leading to the outlying events we studied the occurrence of higher order terms of the Kramers-Moyal expansion in the recordings. We found that the higher order terms of the Kramers-Moyal expansion are negligible for heart rate variability. This finding opens the possibility of the application of the Langevin equation to the whole range of empirical signals containing rare or outlying events. Note, however, that the higher order terms are non-negligible for the other data studied here and for it the Langevin equation is not applicable as a model.

Lessons from the Oroville dam.

Pedestrian crossings are important for accessing the adjacent land use facilities in daily life. Improperly designed crossing facilities cause discomfort to pedestrians and may even jeopardize their safety as well as being a hindrance to the smooth flow of vehicle traffic. These problems exist especially in developing countries such as India, where mixed traffic conditions prevail and road discipline is essentially absent. Thus, the design of efficient crossing facilities considering the flow of both pedestrians and vehicles is of utmost importance in these countries. In this regard, the objective of the current study was to propose warrants for pedestrian crossing facilities in midblock sections under mixed traffic conditions. To achieve this objective, pedestrian–vehicle ( PV2) conflict analysis was carried out at eight different unprotected midblock crosswalks in Mumbai, India. Videographic surveys were conducted and vehicle and pedestrian volumes were extracted, along with pedestrian-crossing characteristics such as crossing difficulty, safety and traffic, and vehicle characteristics. The initial computed PV2-values were adjusted by different factors, and k-means cluster analysis was carried out to determine the threshold PV2-values for selection of a suitable crosswalk facility. The study also highlighted the importance of taking into consideration site characteristics for evaluation of a midblock crosswalk facility. The results of this study are expected to have a wide range of applications in the design of new crosswalk facilities or in the improvement of existing pedestrian crosswalk facilities under mixed traffic conditions.

Analysis of Demand Responsive Pelican Crossing in Mixed Traffic Conditions

Shared spaces aim to reduce the dominance of motor vehicles by promoting pedestrian/cyclist activity and minimizing segregation between road users.Despite the intended scope to improve the safety of vulnerable road users, only few works in the literature focused on before-after safety evaluations, mainly analyzing changes in users’ trajectories and speeds, traffic volumes, and conflict counts, which, while useful, cannot univocally quantify road safety.Here, we propose a more advanced methodology, based on surrogate measures of safety and Extreme Value Theory, to assess road safety before and after the implementation of a shared space. The aim is to produce a crash risk estimation in different scenarios, obtaining a quantitative and comprehensive indicator, useful to practitioners for evaluating the safety of urban design solutions.A real-world case study illustrates the proposed procedure. Video data were collected on two separate days, before and after a shared space implementation, and were semi-automatically processed to extract road users’ trajectories. Analysis of traffic volumes, trajectories, speeds and yield ratios allowed to understand the spatial behavior of road users in the two scenarios. Traffic conflicts, identified with an innovative surrogate measure of safety called “time to avoided collision point” (TTAC), were then used to estimate a Lomax distribution, and therefore to model the probabilistic relationship between conflicts and crashes, eventually retrieving a crash risk estimate. Results show that the analyzed shared space was able to significantly reduce the risk of crashes, and these findings are consistent with the observed changes in users speed and spatial behavior. The analyzed case study and its limitations were useful in highlighting the methodology main features and suggesting practical prescriptions for practitioners.

Trends in extreme 100-y events of temperature and rainfall amounts in the continental United States are estimated, to see effects of climate change. This is a nontrivial statistical problem because climate change effects have to be extracted from "noisy" weather data within a limited time range. We use nonparametric Bayesian methods to estimate the trends of extreme events that have occurred between 1979 and 2019, based on data for temperature and rainfall. We focus on 100-y events for each month in [Formula: see text] geographical areas looking at hourly temperature and 5-d cumulative rainfall. Distribution tail models are constructed using extreme value theory (EVT) and data on 33-y events. This work shows it is possible to aggregate data from spatial points in diverse climate zones for a given month and fit an EVT model with the same parameters. This surprising result means there are enough extreme event data to see the trends in the 41-y record for each calendar month. The yearly trends of the risk of a 100-y high-temperature event show an average 2.1-fold increase over the last 41 y of data across all months, with a 2.6-fold increase for the months of July through October. The risk of high rainfall extremes increases in December and January 1.4-fold, but declines by 22% for the spring and summer months.

<p>Intense precipitation events often occur in Mediterranean regions. These phenomena depend on the presence of mountainous and hilly reliefs combined with masses of humidity caused by the proximity of the sea. Floods are the most significant natural hazards in the region that may cause widespread devastation. Therefore, a proper characterization of these extreme precipitation events is crucial.</p><p>Extreme Value Theory (EVT) is a branch of statistics that provides a suitable framework for the statistical modelling of extreme events. Owing to the spatial heterogeneity of the Mediterranean area, the distribution of extreme precipitation events is non-stationary in space. To take non-stationarity into account, the parameters of the distribution can be viewed as functions of covariates that convey information on the spatial heterogeneity. Such functions may be implemented as a generalized linear model (GLM) or with more flexible non-parametric non-linear models such as Artificial Neural Networks (ANN).</p><p>In this work, we aim at evaluating and comparing several statistical models that allow to interpolate spatially the distribution of intense precipitation events. The statistical models combine the distribution of extremes with a GLM and an ANN for the spatial interpolation of distribution parameters. Key issues are the proper selection of the complexity level of the ANN (i.e. the number of hidden units) and the proper selection of geographical covariates.</p><p>Three sites that form a north-south aridity gradient are included in our study : a region in the French Mediterranean, the Cap Bon area in North-East Tunisia and the Merguellil catchment in central Tunisia. The comparative analyses aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.</p><ul><li><strong>KEYWORDS:</strong> Intense precipitation events, non-stationarity in space, extreme value theory, spatial interpolation.</li> </ul>

A model based on the extreme value theory and copula functions is proposed to assess the probability of potential damages to transmission lines and towers during ice storms. Generalized Pareto distributions of freezing precipitation and wind speed are first derived. Copula functions are then employed to derive the joint probability distribution of ice and wind loads on both transmission lines and towers. Based on the proposed model, probabilities of transmission-line damage and tower collapse can be calculated. The validity and accuracy of the proposed model are verified by comparing the numerical results given by the model with historical data of a real transmission system during two serious ice storms in China. The proposed model will be highly useful for assessing efficacy of power systems emergency controls during ice storms.

<div class="section abstract"><div class="htmlview paragraph">The current approach for new Advanced Driver Assistance System (ADAS) and Connected and Automated Driving (CAD) function development involves a significant amount of public road testing which is inefficient due to the number miles that need to be driven for rare and extreme events to take place, thereby being very costly also, and unsafe as the rest of the road users become involuntary test subjects. A new development, evaluation and demonstration method for safe, efficient, and repeatable development, demonstration and evaluation of ADAS and CAD functions called Vehicle-in-Virtual –Environment (VVE) was recently introduced as a solution to this problem. The vehicle is operated in a large, empty, and flat area during VVE while its localization and perception sensor data is fed from the virtual environment with other traffic and rare and extreme events being generated as needed. The virtual environment can be easily configured and modified to construct different testing scenarios on demand. This paper focuses on the VVE approach and introduces the coordinate transformations needed to sync pose (location and orientation) in the virtual and physical worlds and handling of localization and perception sensor data using the highly realistic 3D simulation model of a recent autonomous shuttle deployment site in Columbus, Ohio as the virtual world. As a further example that uses multiple actors, the use of VVE for Vehicle-to-VRU communication based Vulnerable Road User (VRU) safety is presented in the paper using VVE experiments and real pedestrian(s) in a safe and repeatable manner. VVE experiments are used to demonstrate the efficacy of the method.</div></div>