jin Yoon

Evaluation of motorcycle safety strategies using the severity of injuries

The growth of motorcycle fatalities in California has been especially prominent, specifically with regard to the 24 and under age group and those aged 45-54. This research quantitatively examined factors associated with motorcyclist fatalities and assessed strategies that could improve motorcyclist safety, specifically focusing on the two age groups mentioned above. Severity of injury was estimated separately for both age groups with multinomial logit models and pseudo-elasticity using motorcycle-related collision data that was collected between 2005 and 2009. The results were compared with motorcyclists aged 35-44, a group that shows a consistent trend of fatalities. This research found that lack or improper use of helmets, victim ejection, alcohol/drug effects, collisions (head-on, broadside, hit-object), and truck involvement were more likely to result in fatal injuries regardless of age group. Weekend and non-peak hour activity was found to have a strong effect in both the younger and older age groups. Two factors, movement of running off the road preceding a collision and multi-vehicle involvement, were found to be statistically significant factors in increasing older motorcyclist fatalities. Use of street lights in the dark was found to decrease the probability of severe injury for older motorcyclists. Driver type of victim, at-fault driver, local road, and speed violation were significant factors in increasing the fatalities of younger motorcyclists. Road conditions and collision location factors were not found to be statistically significant to motorcyclist fatalities. Based on the statistically significant factors identified in this research, the following safety strategies appear to be effective methods of reducing motorcyclist fatalities: public education of alcohol use, promoting helmet use, enforcing heavy vehicle and speed violations, improving roadway facilities, clearer roadway guidance and street lighting systems, and motorcyclist training.

Algorithms for bottom-up maintenance optimisation for heterogeneous infrastructure systems

This paper presents a methodology for maintenance optimisation for heterogeneous infrastructure systems, i.e., systems composed of multiple facilities with different characteristics such as environments, materials, and deterioration processes. We present a bottom-up approach: facility-level optimal maintenance policies are first found; these policies are then combined with budget constraints in the system-level optimisation. In the first step, optimal and near-optimal maintenance policies for each facility are found and used as inputs for the system-level optimisation. In the second step, the problem is formulated as a constrained combinatorial optimisation problem, where the best combination of facility-level optimal and near-optimal solutions is identified. Two heuristics, pattern search heuristic (PSH) and evolutionary algorithm (EA), are adopted to solve the combinatorial optimisation problem. Their performance is evaluated using a hypothetical system of pavement sections. Comparison result with real optimal solutions for 20 facilities showed that both algorithms give near-optimal solutions (within less than 0.1% difference from the optimal solution) in 978 (PSH) and 966 (EA) cases out of 1000 executions. The EA performs better in terms of processing time than the PSH. Numerical experiments show the potential of the proposed algorithms to solve the maintenance optimisation problem for realistic heterogeneous systems.

Application of classification algorithms for analysis of road safety risk factor dependencies

Transportation continues to be an integral part of modern life, and the importance of road traffic safety cannot be overstated. Consequently, recent road traffic safety studies have focused on analysis of risk factors that impact fatality and injury level (severity) of traffic accidents. While some of the risk factors, such as drug use and drinking, are widely known to affect severity, an accurate modeling of their influences is still an open research topic. Furthermore, there are innumerable risk factors that are waiting to be discovered or analyzed. A promising approach is to investigate historical traffic accident data that have been collected in the past decades. This study inspects traffic accident reports that have been accumulated by the California Highway Patrol (CHP) since 1973 for which each accident report contains around 100 data fields. Among them, we investigate 25 fields between 2004 and 2010 that are most relevant to car accidents. Using two classification methods, the Naive Bayes classifier and the decision tree classifier, the relative importance of the data fields, i.e., risk factors, is revealed with respect to the resulting severity level. Performances of the classifiers are compared to each other and a binary logistic regression model is used as the basis for the comparisons. Some of the high-ranking risk factors are found to be strongly dependent on each other, and their incremental gains on estimating or modeling severity level are evaluated quantitatively. The analysis shows that only a handful of the risk factors in the data dominate the severity level and that dependency among the top risk factors is an imperative trait to consider for an accurate analysis.

GIS-Based Analysis on Vulnerability of Ambulance Response Coverage to Traffic Condition: A Case Study of Seoul

Travel time uncertainty due to traffic condition is an important factor in planning Emergency Medical Services (EMS). In the study, we analyzed the district-wide service capability of dispatch centers with various descriptive statistics and variability measures to determine the vulnerability of a specific region to the traffic condition. We introduced Response Time Variability (RTV) index to evaluate such vulnerability. To derive RTV, time-varying speeds are defined for each street segment within a street network based on the historic traffic speed data. We generated a 5-minute travel time contour for fire and emergency dispatch centers in Seoul and calculated the corresponding time-dependent coverage areas for three time periods on each day of week. RTV index, which is unitless, has implications that higher RTV means that the region under evaluation has higher vulnerability to the traffic condition, while lower RTV means that the region is less prone to the travel time uncertainty. We find that Gangbuk district shows the lowest RTV, while Seocho district shows the highest RTV.

The impact of advanced technology on Next Generation Air Transportation: A case study of Required Navigation Performance (RNP)

This paper presents an overview and case studies of Performance -based System capability of Next Generation Aviation Transportation System (NGATS), specifically the case of Required Navigation Performance. The Required Navi gation Performance (RNP), one of the key capabilities of Performance -based system, defines lateral navigational precision level, which provides the opportunity to reduce fuel, negative environmental effect, airline operation disruptions, and to increase ru nway utilization and safety. In the early U.S. cases, airlines took a leading role in development of RNP, based on the strong economical motivation and clear business objectives. Reduction in diversions, cancellation, and fuel consumption translated int o significant cost saving and better service quality. There are also cases that RNP increased payload and runway utilization rate. Selective decision process to deploy RNP at the locations where most suitable, showed a great potential of Performance Base d Navigation, yet the implementation process was still limited by system provider ’s capability and the authorization process . In this paper, we will present three early RNP cases in US, including Alaska Airlines, Jetblue Airway, Continental Airlines, and discuss what RNP and Performance Based Navigation can truly deliver and how future development should be carried out.

Improving the Energy Saving Process with High-Resolution Data: A Case Study in a University Building

In this paper, we provide findings from an energy saving experiment in a university building, where an IoT platform with 1 Hz sampling sensors was deployed to collect electric power consumption data. The experiment was a reward setup with daily feedback delivered by an energy delegate for one week, and energy saving of 25.4% was achieved during the experiment. Post-experiment sustainability, defined as 10% or more of energy saving, was also accomplished for 44 days without any further intervention efforts. The saving was possible mainly because of the data-driven intervention designs with high-resolution data in terms of sampling frequency and number of sensors, and the high-resolution data turned out to be pivotal for an effective waste behavior investigation. While the quantitative result was encouraging, we also noticed many uncontrollable factors, such as exams, papers due, office allocation shuffling, graduation, and new-comers, that affected the result in the campus environment. To confirm that the quantitative result was due to behavior changes, rather than uncontrollable factors, we developed several data-driven behavior detection measures. With these measures, it was possible to analyze behavioral changes, as opposed to simply analyzing quantitative fluctuations. Overall, we conclude that the space-time resolution of data can be crucial for energy saving, and potentially for many other data-driven energy applications.

Analysis of Loss of Control Parameters for Aircraft Maneuvering in General Aviation

A rapid increase in the occurrence of loss of control in general aviation has raised concern in recent years. Loss of control (LOC) pertains to unique characteristics in which external and internal events act in conjunction. The Federal Aviation Administration (FAA) has approved an Integrated Safety Assessment Model (ISAM) for evaluating safety in the National Airspace System (NAS). ISAM consists of an event sequence diagram (ESD) with fault trees containing numerous parameters, which is recognized as casual risk model. In this paper, we outline an integrated risk assessment framework to model maneuvering through cross-examining external and internal events. The maneuvering is in the critical flight phase with a high number of LOC occurrences in general aviation, where highly trained and qualified pilots failed to maintain aircraft control irrespective of the preventive nature of the events. Various metrics have been presented for evaluating the significance of these parameters to identify the most important ones. The proposed sensitivity analysis considers the accident, fatality, and risk reduction frequencies that assist in the decision-making process and foresees future risks from a general aviation perspective.

Elderly road collision injury outcomes associated with seat positions and seatbelt use in a rapidly aging society—A case study in South Korea

Introduction Aging has long been regarded as one of the most critical factors affecting crash injury outcomes. In South Korea, where the elderly population is projected to reach 35.9% by 2050, the implications of an increasing number of elderly vehicle users on road safety are evident. In this research, the confounding effect of occupant age in a vehicle in terms of seat position and seatbelt use was investigated. In addition, elderly occupants were divided into a younger-old group aged between 65 and 74 years and an older-old group aged 75 years and older in an effort to assess whether the conventional elderly age standard of 65 years should be reconsidered. Methods A multinomial logit framework was adopted to predict two-level injury severity using collision data between 2008 and 2015. Predictor variables included gender, age group, seat position, seatbelt, road type, road slope, road surface, road line, and type of vehicle. Five models, a base model with no interactions and four interaction models which were combinations of age group, seatbelt use and seat position, were devised and evaluated. Results With no interacting term, age was the most prominent predictor. Elderly occupants were most likely to suffer from severe injury without a seatbelt in all seat positions, and the use of a seatbelt reduced this likelihood the most in the elderly group as well. Front passenger seats had the highest risk to elderly occupants, while the driver seat was statistically insignificant. When the elderly group was divided into the younger-old group and the older-old group, the older-olds were found to be much more vulnerable compared to the younger-olds. In particular, older drivers were five times more likely to suffer a severe injury without a seatbelt. Conclusions The degree of injury severity of elderly occupants was reduced the most with the use of a seatbelt, demonstrating the importance of using seat restraints. The sharp increase in the risk of injury of the older-old group suggests that the age standard of 65 years as the elderly group with regard to traffic safety may require reconsideration due to the growing number of elderly vehicle users on the road. Our results provide practical evidence with which to formulate new safety policies, including mandatory seatbelt use, driving age limits and insurance pricing.

Characterizing the influence of transportation infrastructure on Emergency Medical Services (EMS) in urban area—A case study of Seoul, South Korea

In highly urbanized area where traffic condition fluctuates constantly, transportation infrastructure is one of the major contributing factors to Emergency Medical Service (EMS) availability and patient outcome. In this paper, we assess the impact of traffic fluctuation to the EMS first response availability in urban area, by evaluating the k-minute coverage under 21 traffic scenarios. The set of traffic scenarios represents the time-of-day and day-of-week effects, and is generated by combining road link speed information from multiple historical speed databases. In addition to the k-minute area coverage calculation, the k-minute population coverage is also evaluated for every 100m by 100m grid that partitions the case study area of Seoul, South Korea. In the baseline case of traveling at the speed limit, both the area and population coverage reached nearly 100% when compared to the five-minute travel time national target. Employing the proposed LoST (Loss of Serviceability due to Traffic) index, which measures coverage reduction in percentage compared to the baseline case, we find that the citywide average LoST for area and population coverage are similar at 34.2% and 33.8%. However, district-wise analysis reveals that such reduction varies significantly by district, and the magnitude of area and population coverage reduction is not always proportional. We conclude that the effect of traffic variation is significant to successful urban EMS first response performance, and regional variation is evident among local districts. Complexity in the urban environment requires a more adaptive approach in public health resource management and EMS performance target determination.

Prediction of Galloping Accidents in Power Transmission Line Using Logistic Regression Analysis

Galloping is one of the most serious vibration problems in transmission lines. Power lines can be extensively damaged owing to aerodynamic instabilities caused by ice accretion. In this study, the accident probability induced by galloping phenomenon was analyzed using logistic regression analysis. As former studies have generally concluded, main factors considered were local weather factors and physical factors of power delivery systems. Since the number of transmission towers outnumbers the number of weather observatories, interpolation of weather factors, Kriging to be more specific, has been conducted in prior to forming galloping accident estimation model. Physical factors have been provided by Korea Electric Power Corporation, however because of the large number of explanatory variables, variable selection has been conducted, leaving total 11 variables. Before forming estimation model, with 84 provided galloping cases, 840 non-galloped cases were chosen out of 13 billion cases. Prediction model for accidents by galloping has been formed with logistic regression model and validated with 4-fold validation method, corresponding AUC value of ROC curve has been used to assess the discrimination level of estimation models. As the result, logistic regression analysis effectively discriminated the power lines that experienced galloping accidents from those that did not.