Methodology for development of drought Severity-Duration-Frequency (SDF) Curves

Drought monitoring and early warning are essential elements impacting drought sensitive sectors such as primary production, industrial and consumptive water users. A quantitative estimate of the probability of occurrence and the anticipated severity of drought is crucial for the development of mitigating strategies. The overall aim of this study is to develop a methodology to assess drought frequency and severity and to advance the understanding of monitoring and predicting droughts in the future. Seventy (70) meteorological stations across Victoria, Australia were selected for analysis. To achieve the above objective, the analysis was initially carried out to select the most applicable meteorological drought index for Victoria. This is important because to date, no drought indices are applied across Australia by any Commonwealth agency quantifying drought impacts. An evaluation of existing meteorological drought indices namely, the Standardised Precipitation Index (SPI), the Reconnaissance Drought Index (RDI) and Deciles was first conducted to assess their suitability for the determination of drought conditions. The use of the Standardised Precipitation Index (SPI) was shown to be satisfactory for assessing and monitoring meteorological droughts in Australia. When applied to data, SPI was also successful in detecting the onset and the end of historical droughts. Temporal changes in historic rainfall variability and the trend of SPI were investigated using non-parametric trend techniques to detect wet and dry periods across Victoria, Australia. The first part of the analysis was carried out to determine annual rainfall trends using Mann Kendall (MK) and Sen’s slope tests at five selected meteorological stations with long historical records (more than 100 years), as well as a short sub-set period (1949-2011) of the same data set. It was found that different trend results were obtained for the sub-set. For SPI trend analysis, it was observed that, although different results were obtained showing significant trends, SPI gave a trend direction similar to annual precipitation (downward and upward trends). In addition, temporal trends in the rate of occurrence of drought events (i.e. inter-arrival times) were examined. The fact that most of the stations showed negative slopes indicated that the intervals between events were becoming shorter and the frequency of events was temporally increasing. Based on the results obtained from the preliminary analysis, the trend analyses were then carried out for the remaining 65 stations. The main conclusions from these analyses are summarized as follows; 1) the trend analysis was observed to be highly dependent on the start and end dates of analysis. It is recommended that in the selection of time period for the drought, trend analysis should consider the length xvi of available data sets. Longer data series would give more meaningful results, thus improving the understanding of droughts impacted by climate change. 2) From the SPI and inter-arrival drought trends, it was observed that some of the study areas in Victoria will face more frequent dry period leading to increased drought occurrence. Information similar to this would be very important to develop suitable strategies to mitigate the impacts of future droughts. The main objective of this study was the development of a methodology to assess drought risk for each region based on a frequency analysis of the drought severity series using the SPI index calculated over a 12-month duration. A novel concept centric on drought severity-duration-frequency (SDF) curves was successfully derived for all the 70 stations using an innovative threshold approach. The methodology derived using extreme value analysis will assist in the characterization of droughts and provide useful information to policy makers and agencies developing drought response plans. Using regionalisation techniques such as Cluster analysis and modified Andrews curve, the study area was separated into homogenous groups based on rainfall characteristics. In the current Victorian application the study area was separated into six homogeneous clusters with unique signatures. A set of mean SDF curves was developed for each cluster to identify the frequency and severity of the risk of drought events for various return periods in each cluster. The advantage of developing a mean SDF curve (as a signature) for each cluster is that it assists the understanding of drought conditions for an ungauged or unknown station, the characteristics of which fit existing cluster groups. Non-homogeneous Markov Chain modelling was used to estimate the probability of different drought severity classes and drought severity class predictions 1, 2 and 3 months ahead. The non-homogeneous formulation, which considers the seasonality of precipitation, is useful for understanding the evolution of drought events and for short-term planning. Overall, this model predicted drought situations 1 month ahead well. However, predictions 2 and 3 months ahead should be used with caution. Many parts of Australia including Victoria have experienced their worst droughts on record over the last decade. With the threat of climate change potentially further exacerbating droughts in the years ahead, a clear understanding of the impact of droughts is vital. The information on the probability of occurrence and the anticipated severity of drought will be helpful for water resources managers, infrastructure planners and government policy-makers with future infrastructure planning and with the design and building of more resilient communities

Modeling, Simulation and Prediction of Vehicle Crashworthiness in Full Frontal Impact

Vehicle crashworthiness assessment is critical to help reduce road accident fatalities and ensure safer vehicles for road users. Techniques to assess crashworthiness include physical tests and mathematical modeling and simulation of crash events, the latter is preferred as mathematical modeling is generally cheaper to perform in comparison with physical testing. The most common mathematical modeling technique used for crashworthiness assessment is nonlinear Finite Element (FE) modeling. However, a problem with the use of Finite Element Model (FEM) for crashworthiness assessment is inaccessibility to individual researchers, public bodies, small universities and engineering companies due to need for detailed CAD data, software licence costs along with high computational demands. This thesis investigates modeling strategies which are affordable, computationally and labour inexpensive, and could be used by the above-mentioned groups. Use of Lumped Parameter Models (LPM) capable of capturing vehicle parameters contributing to vehicle crashworthiness has been proposed as an alternative to adopting FEM, while the later have been used to validate LPMs developed in this thesis. The main crash scenario analysed is a full frontal impact against a rigid barrier. Front-end deformation which can be used to measure crash energy absorption and pitching which could lead to occupant injuries in a frontal crash event are parameters focused on. The thesis investigates two types of vehicles; vehicle with initial structure intact is defined as baseline vehicle, while a vehicle that underwent unprofessional repairs on its structural members made of Ultra High Strength Steel (UHSS) is defined as a modified vehicle. The proposed novel LPM for a baseline vehicle impact is inspired by pendulum motion and expresses the system using Lagrangian formulation to predict the two phases of impact: front-end deformation and vehicle pitching. Changes in crashworthiness performance of a modified vehicle were investigated with a FEM; tensile tests on UHSS coupons were conducted to generate material inputs for this FEM. Further, a full scale crash test was conducted to validate the FE simulations. An LPM to conduct crashworthiness assessment of a modified vehicle has been proposed, it is based on a double pendulum with a torsional spring representing the vehicle undergoing a full frontal impact.publishedVersio

Validation of the SAFER Human Body Model Kinematics in Far-Side Impacts

Human Body Models are essential for real-world occupant protection assessment. With the overall purpose to create a robust human body model which is biofidelic in a variety of crash situations, this study aims to evaluate the biofidelity of the SAFER human body model in far-side impacts. The pelvis, torso and the upper and lower extremities of the SAFER human body model were updated. In addition, the shoulder area was updated for improved shoulder belt interaction in far-side impacts. The model was validated using kinematic corridors based on published human subject test data from two far-side impact set-ups, one simplified and one vehicle-based. The simplified far-side set-up included six configurations with different parameter settings, and the vehicle-based included two configurations: with and without far-side airbag, respectively. The updated SAFER HBM was robust and in general the model predicted the published human subject responses (kinematic CORA score > 0.65) for all configurations in both test set-ups. An exception was a 90 degree far-side impact with the D-ring in the forward position, in the simplified set-up. Here the model could not predict the shoulder belt retention, resulting in a low CORA score. Based on the overall results, the model is considered valid to be used for assessment of far-side impact countermeasures

Ontology based Scene Creation for the Development of Automated Vehicles

The introduction of automated vehicles without permanent human supervision demands a functional system description, including functional system boundaries and a comprehensive safety analysis. These inputs to the technical development can be identified and analyzed by a scenario-based approach. Furthermore, to establish an economical test and release process, a large number of scenarios must be identified to obtain meaningful test results. Experts are doing well to identify scenarios that are difficult to handle or unlikely to happen. However, experts are unlikely to identify all scenarios possible based on the knowledge they have on hand. Expert knowledge modeled for computer aided processing may help for the purpose of providing a wide range of scenarios. This contribution reviews ontologies as knowledge-based systems in the field of automated vehicles, and proposes a generation of traffic scenes in natural language as a basis for a scenario creation.Comment: Accepted at the 2018 IEEE Intelligent Vehicles Symposium, 8 pages, 10 figure

Hybrid Low-Order Modeling for Conceptual Vehicle Design

Design freedom, and particularly the freedom to incorporate innovative designs and strategies, is greatest at the very beginning of vehicle conceptual design. Conversely, this is when the least knowledge of the product exists. As product content decisions are made the level of freedom in the design decreases and the design becomes locked in. The majority of vehicle lifecycle cost is also set by the end of vehicle conceptual design. This makes it critical to make well-informed and validated decisions in the concept design phase to avoid expensive iterations and redesign in the detailed design phase. Parametric vehicle modeling permits rapid iteration and optimization of vehicles in the conceptual design phase. A significant portion of vehicle design can be optimized parametrically without knowing specific Computer Aided Design (CAD) based details. Many overall vehicle characteristics such as curb mass, center of gravity location, key dimensions, occupant packaging and cargo volume can all be assessed and improved at the parametric level. Key vehicle performance measures can also be determined to a high level of confidence. In developing vehicle dimensions for a parametric model it is recommended to build up a vehicle using an inside-out approach centered on effective, knowledge-based occupant packaging. This work develops a continuum of dimensional parameters which tie vehicle internal and external dimensions together; it employs a combination of industry standard and author-defined component dimensions which make up overall vehicle outside dimensions. An effective continuum of functional parameters is also developed. In order to develop and optimize models for a desired vehicle type and size class, a knowledge base of vehicle typical values for key dimensional parameters has been compiled using a combination of data sources and field measurements. These values provide a useful starting point for the vehicle design optimization process. They also increase optimization effectiveness and ensure that the optimization begins within a valid design space. This work also develops a parametric modeling, scenario builder and optimization software framework which provides a design and optimization tool for vehicle design with trade-off evaluation tools. These parametric design methods improve design maturity prior to beginning vehicle detailed design

CAE - PROCESS AND NETWORK : A methodology for continuous product validation process based on network of various digital simulation methods

CAE ProNet methodology is to develop CAE network considering interdependencies among digital validations. Utilizing CAE network and considering industrial requirements, an algorithm is applied to execute a product, vehicle development phase, and load case priority oriented CAE process. Major advantage of this research work is to improve quality of simulation results, reducing time-to-market and decreasing dependencies on hardware prototype

Passenger kinematics in evasive maneuvers

In situations that might lead to a vehicle crash, drivers often perform an evasive maneuver, such as braking or steering, in an attempt to avoid a crash. If a crash was not avoided, the maneuver could influence the injury outcome by altering the occupant’s position. Occupants use their muscles in response to a maneuver, and because the typical accelerations are low during maneuvers, the muscle activity can influence the kinematics. Thus, it is important to include the response to these potential maneuvers before the crash when predicting occupant injuries in a crash. The response to maneuvers could be evaluated by adding active musculature to existing evaluation tools, such as human body models. Furthermore, in volunteer studies, the head and torso displacements during maneuvers vary between occupants, but the cause for this variability remains to be identified. Two aims were defined for this thesis, addressed in two parts. The first aim was to advance the active neck and lumbar muscle controllers in the SAFER HBM to predict average response to maneuvers. The second aim was to further understand why such variability is seen in occupant response to evasive maneuvers.Three muscle controller concepts were evaluated in this thesis, two of which were aimed at emulating the reflexes responding to input from the vestibular system that control the head position in space, and one controller that emulated reflexes that respond to lengthening of muscles. For the first aim, the active muscle controllers in the SAFER HBM were updated to allow for simulations with large vehicle yaw rotations, and the predictive capabilities were evaluated in braking, steering, and combinations. In a subsequent study, the updated controllers were tuned to volunteer kinematics in braking and steering, and the model performance was evaluated in the same conditions. It was concluded that the SAFER HBM, with the updated and tuned controllers, could predict passenger head kinematics in braking and steering with good to excellent results.The occupant variability was addressed by statistical analysis of volunteer kinematics in six different vehicle maneuvers. In two subsequent studies, the Active Human Body Model developed within the first aim was used to analyze the model sensitivity to Human Body Model and boundary condition characteristics in braking. From the analysis of volunteer kinematics, it was concluded that the belt system was the most influential predictor for head and torso displacements across all maneuvers, while other characteristics such as sex, stature, age, and body mass index were less influential. In the subsequent studies, the seat forward/rearward position and spinal curvature were found to be most influential in braking

운전자 행동과 차량 종류가 차로 변경 시 안전에 미치는 영향 분석

학위논문(석사) -- 서울대학교대학원 : 공과대학 건설환경공학부, 2022. 8. 김동규.Lane changes are critical contributors to road traffic safety on highways. Among the safety indexes aimed to evaluate the risks of these lane changes, the lane-change risk index (LCRI) is used to determine the potential collision probability of a lane-changing vehicle group in lane-change situations. This paper estimates the impact of driver behavior and vehicle type on the LCRI, using individual vehicle trajectory data. I defined a subject vehicle and its surrounding vehicles (i.e., lead, lag, front and rear vehicles) as a lane-changing vehicle group in a lane change situation. Each of their vehicle type (i.e., truck, bus, car, and motorcycle) and driver behavior (i.e., aggressive, ordinary, and timid) are categorized for regression analysis. Driver behavior is classified through time-space deviations between each vehicle’s trajectories and expected trajectories from Newell’s car-following model. In addition, to consider the heterogeneity among the lanes, this paper uses a linear mixed model, which reflects fixed and random effects. And the latent class analysis was used to classify the lane-changing vehicle group into a number of groups reflecting the characteristics of vehicle groups. Three unique findings of the present study are that (i) I quantified and analyzed the complex interaction between vehicle type and driver behavior within the lane-changing vehicle group in the situation of changing lanes, (ii) I found that the influence of the vehicle type and driver behavior in the lane-changing vehicle group had great heterogeneity depending on the lane, using the random parameter model, and (iii) when the lane-changing vehicle group was classified, most of the variables were observed to be statistically significant within two distinct classes. The findings of this study are expected to provide detailed lane-change strategies for autonomous vehicles as well as to evaluate the causative factors for lane-change risk.고속도로에서 주행을 하면서 운전자가 빈번하게 수행하는 차로 변경은 도로 교통안전과 교통 흐름에 큰 영향을 미치는 행위라고 할 수 있다. 차로 변경 시 위험을 평가하기 위한 안전 지표들 중에서 Lane Change Risk Index(LCRI)는 차로 변경 상황에서 차로를 변경하는 차량 그룹(lane-changing vehicle group)의 잠재적 충돌 가능성을 결정하기 위해서 사용했다. 본 연구의 목표는 개별 차량 궤적 데이터를 사용하여 LCRI에 대한 운전자 행동 및 차량 종류가 미치는 영향을 추정하는 것이다. 이를 위하여 차로 변경을 실시하는 차량과 그 주변 차량(차로변경 전 선행, 후행 차량과, 차로변경 후 선행, 후행 차량)들을 차로를 변경하는 차량 그룹으로 정의했다. 각각의 차량 종류(트럭, 버스, 자동차 및 오토바이)와 운전자 행동(공격적인, 보통 및 소심한) 변수들은 회귀 분석을 위해 분류되었다. 이 때 운전자 행동은 각 차량의 궤적과 Newell의 차량추종모델의 예상 궤적 간의 시공간 편차를 통해 분류했다. 또한 본 연구는 차로 그룹 간의 이질성을 고려하기 위해 고정 효과와 임의 효과를 반영할 수 있는 선형 혼합 모델을 사용하였다. 그리고 잠재 계층 분석법을 이용하여 차로를 변경하는 차량 그룹을 차량 그룹의 특성을 반영하여 여러 그룹으로 분류하였다. 본 연구의 결과는 다음과 같다. 먼저 차로 변경 상황에서 차로를 변경하는 차량 그룹 내의 차량 종류와 운전자 행동 사이의 복잡한 상호 작용을 정량화하고 분석했다. 또한 Random parameter model을 사용하여 차로를 변경하는 차량 그룹에서 차량 종류와 운전자 행동의 영향이 차로에 따라 큰 이질성을 가지는 것을 발견했다. 끝으로 차로를 변경하는 차량 그룹들을 분류했을 때, 대부분의 변수들은 두 개의 별개 집단 내에서 통계적으로 유의한 것을 관찰했다. 이러한 발견들은 자율주행차의 세부적인 차로 변경 전략을 제시하고 차로 변경 시 위험의 원인 요인을 평가하는 데 상당한 기여를 할 수 있음을 시사한다.Chapter 1. Introduction 1 Chapter 2. Methodology 6 2.1 Data 6 2.2 Lane-Changing Risk Index (LCRI) 10 2.3 Driver Behavior Measurement 13 2.4 Linear Mixed Model (LMM) 17 2.5 Latent Class Analysis (LCA) 19 Chapter 3. Results 21 3.1 Linear regression model and Linear mixed model 21 3.1.1 Linear Mixed Model (Fixed Effect) 22 3.1.2 Linear Mixed Model (Random Effect) 26 3.2 Latent Class Analysis (LCA) 28 3.2.1 LMM classified by LCA 31 Chapter 4. Conclusions 32 Bibliography 36 Abstract in Korean 41석

Delays, Inaccuracies and Anticipation in Microscopic Traffic Models

We generalize a wide class of time-continuous microscopic traffic models to include essential aspects of driver behaviour not captured by these models. Specifically, we consider (i) finite reaction times, (ii) estimation errors, (iii) looking several vehicles ahead (spatial anticipation), and (iv) temporal anticipation. The estimation errors are modelled as stochastic Wiener processes and lead to time-correlated fluctuations of the acceleration. We show that the destabilizing effects of reaction times and estimation errors can essentially be compensated for by spatial and temporal anticipation, that is, the combination of stabilizing and destabilizing effects results in the same qualitative macroscopic dynamics as that of the respectively underlying simple car-following model. In many cases, this justifies the use of simplified, physics-oriented models with a few parameters only. Although the qualitative dynamics is unchanged, multi-anticipation increase both spatial and temporal scales of stop-and-go waves and other complex patterns of congested traffic in agreement with real traffic data. Remarkably, the anticipation allows accident-free smooth driving in complex traffic situations even if reaction times exceed typical time headways.Comment: Major revision of the model and the simulations. Particularly, the number of model parameters has been reduce