Comparative risk factor analyses on bi-level injury severity of taxi and private car crashes in Seoul, South Korea.

Objectives: Taxis, one of the main transportation modes that occupy the roadways in Seoul, are semipublic transportation modes for transporting passengers safely and promptly. Considering that one fifth of passenger vehicles on the roads in Seoul are taxis and the crash rate of taxis is double the exposure to traffic, it is important to identify risk factors of taxis from that of private cars. In this paper, crash causes and characteristics in both taxi crashes and private car crashes are investigated to identify the risk factors in accordance with the injury severity.Methods: An eight-year light-vehicle crash dataset was utilized, in which injury levels were defined as severe vs. non-severe. Three binary logit models that estimate the severity of crashes, the injury severity for at-fault drivers, and the injury severity for victims were modeled for taxi crashes and private car crashes. Independent variables were extracted and included in the models to evaluate the odds ratio of each predictor variable.Results: The results indicated that violation of traffic signals and signs was the highest contributor among all violation types for taxi crashes and parties involved (at-fault driver and victims), while driving on the wrong side of the road resulted in the highest increase in the odds ratio for private cars. Head-on collision and nighttime driving increased the likelihood of severe injury risk for all models, while age was the most prominent factor for the injury level of victims. Use of seatbelts had a major impact on the at-fault drivers, especially for taxis.Conclusions: This study identified the risk factors that affect the crash- and party-related severity level when casualties involved taxis and private cars. By employing both crash- and party-level models, the study not only identifies the risk factors among taxis and private car crashes but also provides a comprehensive picture of the injury profile of all vehicular occupants, which helps to devise safety measures that enhance the safety and reduce the injury severity for parties involved in crashes.

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.

Elderly pedestrian safety in a rapidly aging society-Commonality and diversity between the younger-old and older-old.

OBJECTIVES: Changes in the physical and mental abilities of elderly road users have led to an important question of how to define elderly. In this article, both common and diverse contributory factors to elderly pedestrian injuries are investigated, by segmenting the elderly into the younger-old (65-74) and older-old (75+). METHODS: An 8-year collision data set in Seoul, South Korea, was utilized, where injury levels were defined as severe vs. nonsevere. Three binary logit models-single contributory factor; age and single factor; and age and joint factors-were modeled using 17 predictor variables to evaluate odds ratios with middle-aged (14-64) pedestrians as a reference group. RESULTS: In the single contributory factor model, we found that older age was the most critical risk factor leading to severe injury. In the interaction model of age and single contributory factor, higher odds ratios were observed in the older-old than the younger-old for all predictor variables. A set of common contributory factors for both elderly groups was identified, including near overpass crossing, roadside, drunk, and truck. On the other hand, uphill, downhill, nighttime, and sidewalk were found to be a much higher risk to the older-olds. The age and joint factor analysis revealed amplifying effects among risk factors when considered in combination, especially among older-old pedestrians. CONCLUSIONS: The study investigated the commonality and diversity of pedestrian injuries among the elderly by introducing an additional cutoff age of 75. By employing single and interaction binary logit models, the study identified common risk factors for both elderly groups, as well as those that are particularly hazardous to the older-old. With nearly every country experiencing growth in the elderly population, our study strongly suggests that the conventional definition of a single elderly group is no longer relevant and the variety among elderly pedestrians needs to be considered in traffic safety policy.

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.

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.

Contributing factors to vehicle to vehicle crash frequency and severity under rainfall.

INTRODUCTION: This study combined vehicle to vehicle crash frequency and severity estimations to examine factor impacts on Wisconsin highway safety in rainy weather. METHOD: Because of data deficiency, the real-time water film depth, the car-following distance, and the vertical curve grade were estimated with available data sources and a GIS analysis to capture rainy weather conditions at the crash location and time. Using a negative binomial regression for crash frequency estimation, the average annual daily traffic per lane, the interaction between the posted speed limit change and the existence of an off-ramp, and the interaction between the travel lane number change and the pavement surface material change were found to increase the likelihood of vehicle to vehicle crashes under rainfall. RESULTS: However, more average daily rainfall per month and a wider left shoulder were identified as factors that decrease the likelihood of vehicle to vehicle crashes. In the crash severity estimation using the multinomial logit model that outperformed the ordered logit model, the travel lane number, the interaction between the travel lane number and the slow grade, the deep water film, and the rear-end collision type were more likely to increase the likelihood of injury crashes under rainfall compared with crashes involving only property damage. PRACTICAL IMPLICATIONS: As an exploratory data analysis, this study provides insight into potential strategies for rainy weather highway safety improvement, specifically, the following weather-sensitive strategies: road design and ITS implementation for drivers' safety awareness under rainfall.

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.