Crash Events Research Articles

Mesoscale Analysis on the Asymmetric Rainband of Typhoon Matmo (2014) and the Related Weather Situations for the GE222 Aircraft Crash Case

Typhoon Matmo (2014) was formed over the Central Pacific Ocean on 18 July 2014, and later, it became moderate in intensity (32.7~50.9 m/s) with maximum wind speed of 38 m/s. After it landed the southeast coast of China, the typhoon circulation showed an asymmetric pattern in dipole wind field based upon the composite mean winds at 925 hPa in use of NCEP/NCAR reanalysis data and NOAA/ESRL atmospheric variables plotting approach. The wind dipole feature was due to the steep gradient of the geopotential height anomaly at the east flank of the typhoon circulation and the friction effect at its lowest levels over land from the synoptic scale point of view. The study focuses on investigating the characteristics of the line echo wave pattern (LEWP) embedded within the typhoon rainband through the multi-scale processes, and try to realize the possible causes of crash event from the meteorological point of view for TransAsia Airways GE222 occurrence at Makung Airport influenced by the LEWPs in use of mesonet surface analysis based on 1-minute interval data measured by Makung Airport weather station and dual-Doppler radar analysis composited from Makung and Chiku weather radar data. The preliminary results delineated that the wave-like echoes organized by deep convections featured a key factor on the development of short duration heavy rainfall, low visibility and significant turbulence from the mesoscale point of view. Conclusively speaking, the aircraft faced multiple and severe weather situations, including the intense crosswind, obvious downdraft and extremely low visibility. Therefore, the impact of deep convections inside LEWPs embedded within the typhoon rainband on aviation safety was remarkable, and this case is a good lesson for flight safety.

A numerical-experimental assessment on a composite fuselage barrel vertical drop test: Induced damage onset and evolution

Crashworthiness is the ability of a structure to withstand a crash event ensuring the safety of its passengers. The aim of this work is to investigate the damage onset and evolution in a composite fuselage barrel undergoing a vertical drop test on a rigid surface. The mechanical behaviour of the barrel has been assessed by means of a Numerical-Experimental study. Indeed, the experimental data from a full scale barrel test, performed at the CIRA facilities, have been used in conjunction with numerical results, obtained by means of an advanced 3D FEM model, to investigate, in detail, the deformations and the damage development during the crash event. The adopted three-dimensional numerical model, based on an explicit FE formulation, uses Continuum Shell elements and CDM to take into account the onset and the evolution of the intra-laminar damages in each subcomponent of the investigated composite fuselage barrel. The obtained numerical results have been compared with experimental data in terms of accelerations, displacements and deformations to provide a preliminary validation of the adopted FEM model. A special attention has been given to sub-components which demonstrated to mainly influence the global mechanical behaviour of the investigated composite fuselage barrel during the experimental test.

Assessing driver behavior using shrp2 adverse weather data

IntroductionThis study explored how drivers adapt to inclement weather in terms of driving speed, situational awareness, and visibility as road surface conditions change from dry to slippery and visibility decreases. The proposed work mined existing data from the SHRP 2 NDS for drivers who were involved in weather-related crash and near-crash events. Baseline events were also mined to create related metadata necessary for behavioral comparisons. Methods: Researchers attempted, to the greatest extent possible, to match non-adverse-weather driving scenarios that are similar to the crash and near-crash event for each driver. The ideal match scenario would be at a day prior to the crash during non-adverse weather conditions having the same driver, at the same time of day, with the same traffic level on the same road on which the crash or near-crash occurred. Once the matched scenarios have been identified, a detailed analysis will be performed to determine how a driver’s behavior changed from normal driving to inclement-weather driving. Results: Data collected indicated that, irrespective of site location (i.e., state), most crashes and near-crashes occurred in rain, with only about 12% occurring in snowy conditions. Also, the number of near-crashes was almost double the number of crashes showing that many drivers were able to avoid a crash by executing an evasive maneuver such as braking or steering. Conclusions: Most types of near crashes were rear-end and sideswipe avoidance epochs, as the drivers may have had a difficult time merging or trying to change lanes due to low visibility or traffic. Hard braking combined with swerving were the most commonly used evasive maneuvers, occurring when drivers did not adjust their speeds accordingly for specific situations. Practical applications: Results from this study are expected to be utilized to educate and guide drivers toward more confident and strategic driving behavior in adverse weather.

Evaluation of speed distribution at horizontal curve on two-lane rural arterial highway under mesopic and photopic scenario

Drivers travel at speed they assumed to be comfortable to achieve prevailing conditions and road geometry. The relationship between road geometrical design and speed of travelling vehicle is very prominent especially when travelling at the horizontal curve of a roadway. Crash events on horizontal curve are higher by the range of 1.5 and 4 times compared to the tangent sections. This is because speed varies according to the perception of constrain imparted to the drivers by the road geometrical design. The purpose of this study is to investigate the relationship between design speeds, operating speeds, and speed limits while drivers negotiate the horizontal curve. Furthermore, this study also investigates the behaviour of driver manoeuvre on the horizontal curve under mesopic and photopic visions. The speed data measurement was based on spot speed data at specific points and locations using the laser gun detector. The study was carried out by analysing the speed characteristics during daytime at three different segments along the horizontal curve (i.e. transition at entering of curve, middle of curve, and transition at leaving of curve). Apart from that, the difference in the mean speed on the contrast sensitivity under mesopic and photopic conditions was determined via t-test. It discovered that vehicles travelling on transition entering the curve tend to travel at higher speed than on the middle of curve and on transition leaving the curve. Based on the preliminary finding, further testing on middle of curve at night time was carried out to find significant effect under mesopic and photopic visual conditions. The research also found that the existing speed limit of the selected road stretches was lower than the 85th percentile speed. The 85th percentile speed is a commonly used measure to decide the speed limit on a road.

Factors influencing side air curtain deployment in rollover motor vehicle accidents

Objective: Recent changes in FMVSS have led to the utilization of side air curtains to provide occupant retention during rollover events. However, the safety advantage provided by the air curtains relies on the vehicle system’s ability to detect the rollover event and deploy the curtains. The purpose of this study is to identify crash and vehicle characteristics in motor vehicle rollovers that influence side air curtain deployment and occupant outcomes. The current study aims to improve the understanding of rollover events and inspire more robust air curtain deployment strategies.Methods: Study data were extracted from rollover cases documented in the NASS-CDS data set from 2011 to 2015. Vehicle model years of 2011 or later with side air curtains installed were examined. The presence of a rollover sensor in each vehicle was determined from vehicle content data available on the Insurance Institute for Highway Safety’s crash rating website. The resulting data set contained 14,003 weighted cases of rollover accidents in which the side air curtain did not deploy (40 raw count) and 23,178 cases of deployment (80 raw count).Results: Several crash event and vehicle characteristics were similar for the nondeployed and deployed groups, including number of quarter turns, primary location of damage, initiating event for the rollover, and vehicle model year. However, the nondeployed group included significantly more passenger vehicle body types (vs. SUV or truck) and had a significantly lower rate of rollover sensor presence. Presence of a rollover sensor increased the odds air curtain deployment by a factor of 36.5 (95% confidence interval [CI], 5.06–265). Cases in which both side air curtains deployed resulted in a higher frequency of injured occupants (Maximum Abbreviated Injury Scale [MAIS] ≥ 3). However, rollover events resulting in these injuries were also associated with higher rates of impact with another object or vehicle and damage to the roof of the vehicle, suggesting a higher energy event.Conclusions: Nondeployment of the side curtain airbags in rollovers occurred more frequently in vehicles without dedicated rollover sensors, which were most frequently passenger vehicles.

О НЕКОТОРЫХ СЛУЧАЯХ ГРАЖДАНСКО-ПРАВОВОЙ ОТВЕТСТВЕННОСТИ ПРИ КАТАСТРОФЕ НА ВОЗДУШНОМ ТРАНСПОРТЕ

The article is devoted to some frequently occurring cases of civil liability in air transport in the event of a plane crash. The article analyzes the norms of air legislation both domestic and foreign. The author makes suggestions on amendments to the legislation of the Russian Federation.

Finite Elements Models of the Head in Craniocerebral Trauma – Review

Head injuries are a major health and socio-economic problem. To better protect the head against various crash, sport, or fall events, the underlying mechanisms and tolerances need to be investigated. Many investigations have been conducted using cadaver heads, animal heads, physical head models, and in vitro models throughout the world. These experiments, together with the development of computational techniques, have subsequently led to the development of numerical head models, especially finite element (FE) models, to allow more in-depth biomechanical studies. A large number of FE head models have been developed during the years and authors are trying to find the best solutions for a correct explanation of the lesional mechanisms. The finite element method (FEM) is based on the energy formulation of the mechanics of the structures and on approximation methods. It consists of approximating the actual structure through a model consisting of finite elements interconnected in points called nodes. By nodes, each item is under compatibility and balance conditions with adjacent elements. The present paper is a literature review, underlying some of the finite element models, which allowed a good explanation of the head trauma mechanisms.

Exploring Contributing Factors of Hazardous Events in Construction Zones Using Naturalistic Driving Study Data

The naturalistic driving study (NDS) data offers a rare opportunity for a first-hand view of crashes and near-crashes (CNC) that occur around work zones. The objective of this study is twofold: 1) evaluate risk of CNC using pre-incident variables using logistical regression models and; 2) classify and predict CNC events using machine learning methods. The regression models found that driving behavior, duration of secondary task and traffic density exerted a high influence on risk of CNC in work zones. Odds of a safety critical event for different risk factors followed similar trend for work zones and non-work zones. Duration of secondary task and traffic density variables contributed to increased crash risk in work zones than non-work zones. For the second objective, four machine learning algorithms: Random forest (RF), Deep Neural Network, Multilayer Feedforward Neural Network, and t-Distributed Stochastic Neighbor Embedding (t-SNE), were applied to work zone events and non-work zone events within NDS data. The RF algorithm performed the best in classifying CNC events occurring in work zones. The prediction accuracy was 86.3% for three classes: crash, near-crash, and baseline and 91.2% for two classes: crash and near-crash. For non-work zone data, the Deep Neural Network model outperformed others in differentiating between crash and near-crash events.

Probabilistic classification of hazardous materials release events in train incidents and cargo tank truck crashes

As two of the main modes of hazmat surface transportation, quantifying conditional probability of release of hazmat from trains in rail incidents and Cargo Tank Trucks (CTTs) in highway crashes is an important component of risk assessment. The objective of this study was identifying computational tools with reliable performance for quantifying probability of hazmat release in train incidents and CTT crashes, based on the type of the decision-making problem. Events of release of hazmat were probabilistically classified by statistical and machine learning methods (Mixed Logistic Regression, Naïve Bayes, Random Forests, and Support Vector Machines) using relevant explanatory variables. The datasets were Federal Railroad Administration rail equipment incident data, and combined Nebraska and Kansas police-reported traffic crash data. Given the rarity of these events, the classification performance of different methods was compared based on precision, recall and area under ROC curves (AUC). Random Forests had the best performance in classifying hazmat release for trains and railcars, based on different criteria. For CTTs, Support Vector Machines and Random Forests had the highest precision, while logistic regression and naïve Bayes performed better based on recall and AUC. The research provides recommendations regarding usage of the methods depending on the purpose of analysis.

Class-imbalanced crash prediction based on real-time traffic and weather data: A driving simulator study

Objective: Crash occurrence prediction has been of major importance in proactively improving traffic safety and reducing potential inconveniences to road users. Conventional statistical crash prediction models frequently suffer from severe data quality issues and require a significant amount of historical data. On the other hand, even though machine learning (ML) based algorithms have proven to be powerful in predicting future outcomes in different fields of applications, they likely fail to provide satisfactory results unless a tuning parameter approach is conducted. The main objective of this article is to develop real-time crash prediction models that will potentially be employed within traffic management systems.Methods: In this study, two highly optimized data-driven models for crash occurrence prediction have been designed based on the popular machine learning techniques, Support Vector Machine (SVM) and deep neural network Multilayer Perceptron (MLP). To ensure that the proposed algorithms produce robust and stable performance, the optimal scheme for models’ construction has been thoroughly examined and discussed. Additionally, the further boost of models’ performance requires the systemic assessment of crash strongest precursors within the driver-vehicle-environment triptych. Therefore, three categories of features, including driver input responses, vehicle kinematics and weather conditions, were measured during the execution of various driving tasks performed on a desktop driving simulator. Moreover, since crash events typically occur in rare instances tending to be underrepresented in the dataset, an imbalance-aware strategy to overcome the issue was adopted using the Synthetic Minority Oversampling TEchnique (SMOTE).Results: The results show that MLP exhibited the best performing prediction results, most particularly, in clear, overcast and snow conditions, in which MLP recall values were above 94%. Higher F1-score values were achieved in overcast and rain weather by MLP and snow conditions by SVM; whereas over 90% of G-mean levels were obtained under fog and rain conditions for MLP and snow condition for SVM.Conclusion: The findings provide new insights into crash events forecasting and may be used to promote enforcement efforts related to designing crash avoidance/warning systems that enhance the effectiveness of the system’s application based on driver input and vehicle kinematics under various weather conditions.

SHIFT: A Highly Realistic Financial Market Simulation Platform

This paper presents a new financial market simulator that may be used as a tool in both industry and academia for research in market microstructure. It allows multiple automated traders and/or researchers to simultaneously connect to an exchange-like environment, where they are able to asynchronously trade several financial assets at the same time. In its current iteration, this order-driven market implements the basic rules of U.S. equity markets, supporting both market and limit orders, and executing them in a first-in-first-out fashion. We overview the system architecture and we present possible use cases. We demonstrate how a set of automated agents is capable of producing a price process with characteristics similar to the statistics of real price from financial markets. Finally, we detail a market stress scenario and we draw, what we believe to be, interesting conclusions about crash events.

Beyond negative and positive: Exploring the effects of emotions in social media during the stock market crash

Recent studies indicate that the stock market is influenced by emotion in social media (ESM) which are embodied in user-generated content. However, the relationship between ESM and the stock market in the event of the market crash has not been fully explored. This study thus explores the effects of ESM on the stock market during the market crash by empirically validating the proposed cognition-based framework of “Emotion-Cognition-Market”. A three-component model is constructed for the framework, which uses sentiment analysis to calculate emotions from two-dimension of valence and arousal, uses Hidden Markov Model (HMM) for market cognition mining, and uses ordered logistic regression for relationship establishment between ESM and market cognition. More than 280,000 Weibo of 34 listed companies during the market crash (the second half-year of 2015 in China) are used. It is confirmed that the impact process of ESM on the stock market is actually the result of constantly changing market cognition influenced by ESM. This study goes beyond the commonly used positive and negative (polar) emotions or sub-categorical emotions, discovering the salient effects of arousal dimension in the market crash, and also quantify the effects. The results show that the increase of one unit high arousal ESM significantly increases the probability of the cognition of a crash state by 9.99 and 17.41% during the in-crash and post-crash period, of which “Fear” is the main risk factor. In addition, positive valence ESM is the driving force of restoring the stability of the market cognition only in the later stage of the market crash. This paper calls on market participants to pay attention to high arousal emotions in emergency situations in advance.

Harnessing ambient sensing & naturalistic driving systems to understand links between driving volatility and crash propensity in school zones – A generalized hierarchical mixed logit framework

With the advent of seemingly unstructured big data, and through seamless integration of computation and physical components, cyber-physical systems (CPS) provide an innovative way to enhance safety and resiliency of transport infrastructure. This study focuses on real-world microscopic driving behavior and its relevance to school zone safety – expanding the capability, usability, and safety of dynamic physical systems through data analytics. Driving behavior and school zone safety is a public health concern. The sequence of instantaneous driving decisions and its variations prior to involvement in safety critical events, defined as driving volatility, can be a leading indicator of safety. By harnessing unique naturalistic data on more than 41,000 normal, crash, and near-crash events featuring over 9.4 million temporal samples of real-world driving, a characterization of volatility in microscopic driving decisions is sought at school and non-school zone locations. A big data analytic methodology is proposed for quantifying driving volatility in microscopic real-world driving decisions. Eight different volatility measures are then linked with detailed event-specific characteristics, health history, driving history/experience, and other factors to examine crash propensity at school zones. A comprehensive yet fully flexible state-of-the-art generalized mixed logit framework is employed to fully account for distinct yet related methodological issues of scale and random heterogeneity, containing multinomial logit, random parameter logit, scaled logit, hierarchical scaled logit, and hierarchical generalized mixed logit as special cases. The results reveal that both for school and non-school locations, drivers exhibited greater intentional volatility prior to safety-critical events. Volatility in positive and negative vehicular jerk in longitudinal and lateral directions associates with increases the probability of unsafe outcomes (crashes or near-crashes) at school zones. A one-unit increase in intentional volatility measured by positive vehicular jerk in longitudinal direction associates with a 0.0528 increase in the probability of crash outcome. Importantly, the effect of negative vehicular jerk (braking) in longitudinal direction on the likelihood of crash outcome is almost double. Methodologically, Hierarchical Generalized Mixed Logit model resulted in best-fit, simultaneously accounting for scale and random heterogeneity. When accounted for separately, more parsimonious models accounting for scale heterogeneity performed comparably to the less parsimonious counterparts accounting for random heterogeneity. Importantly, even after accounting for random heterogeneity, substantial heterogeneity due to a “pure scale-effect” is still observed, underscoring the importance of scale effects in influencing the overall contours of variations in the modeled relationships. The study demonstrates the value of observational study design and big data analytics for understanding extreme driving behaviors in safe vs. unsafe driving outcomes. Implications for designing personalized school zone behavioral countermeasures are discussed.

Improving collapsibility robustness of an EPS-CD by means of simulation and six sigma

Steering columns for column assist Electric Steering are one of the main elements of the passive safety restraint system in a passenger vehicle. It is designed to collapse applying an optimized ride down force to reduce chest and head injuries of the driver during a frontal crash event, making this requirement one of the most important ones for its development. The system crash response is generated by several elements and interfaces due to the fact of many other different product requirements that a steering column must fulfill. This could lead into a complex validation process throughout the development, as it is needed to integrate the product into the vehicle in order to meet the passive safety requirements. As part of ZF lean Validation approach a new method has been developed to improve the system robustness in ZF Active safety division by means of virtual simulation and six sigma disciplines. This resultant method is also applicable for a model based development process and also contributes to improve frontloading development techniques. An analysis of variance has been done using a correlated virtual model for crash simulation, where the main parameters of the system are known (normally the case when same product generation is reused in a new vehicle). Obtaining as a result of the analysis the regression equation of the system response. This equation defines and quantifies the importance of each factor, and consequently is defining the component contribution and how to develop a column from bottom to top. Moreover, as a second step, a Monte Carlo analysis has been applied using the equation obtained generating statistical results for a number of cases (based in the design tolerances) not manageable from the hardware testing perspective neither from the finite elements methods. In that project Monte Carlo analysis has been applied for 100,000 cases generating response confident intervals instead of the classic FEM deterministic response where only the nominal case is considered.

A survey of data recovery on flash memory

In recent years, flash memory has become more widely used due to its advantages, such as fast data access, low power consumption, and high mobility. However, flash memory also has drawbacks that need to be overcome, such as erase-before-write, and the limitations of block deletion. In order to address this issue, the FTL (Flash Translation Layer) has been proposed with useful functionalities like address mapping, garbage collection, and wear-leveling. During the process of using, the data may be lost on power failure in the storage systems. In some systems, the data is very important. Thus recovery of data in the event of the system crash or a sudden power outage is of prime importance. This problem has attracted attention from researchers and many studies have been done. In this paper, we investigate previous studies on data recovery for flash memory from FTL processing solutions to PLR (Power Loss Recovery) solutions that have been proposed by authors in the conference proceeding, patents, or professional journals. This will provide a discussion of the proposed solutions to the data recovery in flash memory as well as an overview.

Crashworthiness of a general aviation fuselage section: 3D FEM numerical modelling and validation

Crashworthiness is defined as the capability of a structure to guarantee its occupants safety during a crash event. In the present work, the crashworthiness of a composite fuselage section has been preliminary investigated. Indeed, the mechanical behavior of the investigated fuselage section has been assessed by means of the FE code ABAQUS/Explicit, considering an impact event on a rigid surface. The proposed numerical model, capable to take into account the matrix and fiber traction and compression damages, uses a 3D Finite Element formulation to better predict the deformations and the damages onset and propagation during the impact event. Two numerical models have been introduced to study the effect of the passenger floor stiffening on the mechanical response to impact of the entire investigated fuselage barrel. The results in terms of displacements and qualitative deformations of the fuselage, for the analyzed numerical models, have been compared.

Estimated crash injury risk and crash characteristics for motorsport drivers

ObjectiveMotorsport crash events are complex and driver restraint systems are unique to the motorsport environment. The National Association for Stock Car Auto Racing, Incorporated (NASCAR®) crash and medical datasets provide an opportunity to assess crash statistics and the relationship between crash characteristics and driver injury. Injury risk curves can estimate driver injury risk and can be developed using vehicle incident data recorder information as inputs. These relationships may provide guidance and insight for at-track emergency response, driver triage and treatment protocols. MethodEight race seasons of crash and medical record data (including Association for the Advancement of Automotive Medicine Abbreviated Injury Scale (AIS) scores) from the Monster Energy NASCAR Cup Series & NASCAR Xfinity Series were processed and analyzed. Multiple logistic regression modeling was used to produce injury risk curves from longitudinal and lateral resultant change in velocity, resultant peak acceleration, principal direction of force and the number of impacts per incident. Results2065 Unique IDR data files were matched with 246 cases of driver injury or sub-injury (severity below AIS 1) and 1819 no-injury cases. Multiple logistic regression modeling showed increasing resultant change in velocity, resultant peak acceleration and the number of impacts during a crash event all increase estimated driver injury risk. After accounting for the other predictors in the model, right lateral impacts were found to have a lower estimated injury risk. The model produced an Area Under the Receiver Operating Characteristics curve of 0.80. Across the eight race seasons in this study the overall average resultant change in velocity was 34.4 kph (21.4 mph) and the average resultant peak acceleration was 19.0 G for an average of 258 crashes per season. For 2011 through 2015, full time drivers experienced 134 times more crashes per mile traveled than passenger vehicles, but experienced 9.3 times fewer injuries per crash. ConclusionMultiple logistic regression was used to estimate AIS 1+ injury only and AIS 1+ with sub-injury risk for motorsport drivers using motorsport-specific crash and medical record databases. The injury risk estimate models can provide future guidance and insight for at-track emergency medical response dispatch immediately following an on-track crash. These models may also inform future driver triage protocols and influence future expenditures on motorsports safety research.

Behavioural Observation as a Means of Assessing Sleepiness Related Driving Impairment in Obstructive Sleep Apnea

This study compared the temporal pattern of collateral behaviours (actions not directly related to task performance) in obstructive sleep apnea (OSA) drivers under monotonous driving conditions following sleep restriction versus normal sleep. Seventeen patients with untreated severe OSA completed a 90-minute driving simulator task during mid-afternoon under two experimental conditions: prior normal habitual sleep (~8h) and prior sleep restriction (4h time in bed). Steering deviation and crash events were identified using a driving simulator. Collateral behaviours (self-centred gestures, non-verbal facial activities, postural adjustments, ludic activities and eye closures) were identified using video recording and a behavioural ethogram. Participants demonstrated increased steering deviation over the drive (p<0.001) and following sleep restriction (p<0.001). All collateral behaviours, except non-verbal facial activities, increased over the drive (all p<0.01). Compared to normal sleep condition, the incidence rate was 2.1 times higher for eye closures (95%CI 1.75-2.60) and 1.5 times higher for postural adjustments (95%CI 1.29-1.72) following sleep restriction, while ludic behaviours reduced by 50% (IRR 0.53, 95%CI 0.36-0.78), all p<0.01. In the 10 minute period prior to simulator crash events, eye closure frequency increased compared to equivalent periods without a crash event 2.1 (95%CI 1.4-3.8, p<0.01). Kaplain-Meier analyses showed a progressive cessation of non-verbal facial activities leading up to crash events (c²=6.2, p=0.013). Although eye-closure appears to be a more sensitive marker of poor vigilance, behavioural observation could provide a novel further method for assessing vigilance failure in OSA and could assist in the development of novel video-based in-car devices for detection of driver sleepiness/fatigue.

Non-Fatal Motorcycle Crash Configuration and Injury Severity at T-Junction in Malaysia

&#x0D; &#x0D; &#x0D; Crash configuration or crash type analysis is one of the techniques for studying and understanding the mechanism of motorcycle crashes occurrence. The process involves the development of crash events with common causal pre-crash characteristics, such as driver behaviour, roadway situations, and movements of the involved vehicles. This study is focusing on nan-fatal motorcyclist on current data (5-year period: 2011- 2015) and injuries pattern among motorcyclist associated with the crash type of the crash. The objectives of the study were to: (i) investigate trends of occurrence of crash configuration involved motorcycle crashes in Malaysia; (ii) identify injuries related to the crash configuration; and (iii) recognise the injuries severity of the corresponding crash configuration. A total of 642 accident cases with injury were found to occur at T-junction. From the 642 cases at T-junction, seven types of crash configuration were identified appropriately with the cases involved. They are approach turn, angle 1/2, rear-end, U-turn, sideswipe, overtaking and both turning. Approach-turn crashes with a passenger car in the process of making a turn while a motorcycle was travelling straight ahead on primary roads was the most common occurrence that causes collision among motorcycle and passenger car at T-junction in the study. Four body regions were the most common region to be injured in every crash type. They are lower extremity, upper extremity, face and head. Also, the study shows that the number of injuries at face region is more than the number of injuries to the head among rider that involved crashes at T-junction. However, in term of injury severity, the most hazardous crash types identified were among Angle 1/2 crashes where motorcycle from a minor road to the primary road.&#x0D; &#x0D; &#x0D;

Analyzing Pedestrian-Involved Crash Hotspots in Hillsborough County, Florida

Pedestrians involved in vehicle accidents are at high risk of injury because they lack protection in the event of a collision. Hillsborough County, Florida is an especially dangerous area for pedestrians, where 1.5 pedestrians per day on average are involved in a motor vehicle accident. To maximize efficiency for planners and increase resulting benefits for pedestrians, this study aims to identify zones or hotspots of pedestrian-involved accidents based on pedestrian-involved crashes on major roads from 2012 to 2016 in Hillsborough County, Florida, USA. Using the network-based Moran’s I index, the Ripley’s K-function, and kernel density estimation as exploratory data analysis tools, this study investigates possible spatial dependence for pedestrian-involved crash events in Hillsborough County and identifies areas of high pedestrian risk. First, this study will apply the Moran’s I Index and Ripley’s K-function to demonstrate that pedestrian-involved accident sites are not randomly distributed in the study area. Next, we proceed with application of kernel density estimation to determine zones where high pedestrian risk might be associated with a high density of accidents. Identification of specific locations of high risk allows infrastructure improvement projects to prioritize resources efficiently, ultimately resulting in increased benefits to pedestrians.