Severity analysis of tree and utility pole crashes: Applying fast and frugal heuristics

A roadway departure (RwD) crash is defined as a crash that occurs after a vehicle crosses an edge line or a center line, or otherwise leaves the designated travel path. RwD crashes account for approximately 50% of all traffic fatalities in the U.S. Additionally, crashes related to roadside fixed objects such as trees, utility poles, or other poles (TUOP) make up 12–15% of all fatal RwD crashes in the U.S. Data spanning over seven years (2010–2016) shows that TUOP crashes account for approximately 22% of all fatal crashes in Louisiana, which is significantly higher than the national statistic. This study aims to determine the effect of crash, geometric, environmental, and vehicle characteristics on TUOP crashes by applying the fast and frugal tree (FFT) heuristics algorithm to Louisiana crash data. FFT identifies five major cues or variable threshold attributes that contribute significantly to predicting TUOP crashes. These cues include posted speed limit, primary contributing factor, highway type, weather, and locality type. The balanced accuracy is around 56% for both training and test data. The current model shows higher accuracies compared to machine learning models (e.g., support vector machine, CART). The present findings emphasize the importance of a comprehensive understanding of factors that influence TUOP crashes. The insights from this study can help data-driven decision making at both planning and operation levels.     

Modeling two-way stop-controlled intersection crashes with zero-inflated models on Louisiana rural two-lane highways

Intersection safety continues to be a crucial issue throughout the United States. In 2016, 27% of the 37,461 traffic fatalities on U.S. roadways occurred at or near intersections. Nearly 70% of intersection-related fatalities occurred at unsignalized intersections. At such intersections, vehicles stopping or slowing to turn create speed differentials between vehicles traveling in the same direction. This is particularly problematic on two-lane highways. Research was performed to analyze safety performance for intersections on rural, two-lane roadways, with stop control on the minor roadway. Roadway, traffic, and crash data were collected from 4148 stop-controlled intersections of all 64 Parishes (counties) statewide in Louisiana, for the period of 2013 to 2017. Four count approaches, Poisson, Negative Binomial (NB), Zero-inflated Poisson (ZIP) and Zero-inflated Negative Binomial (ZINB) were used to model the number of intersection crashes for different severity levels. The results indicate that ZIP models provide a better fit than all other models. In addition to traffic volume, larger curve radii of major and minor roads and wider lane widths of major roads led to significantly smaller crash occurrences. However, higher speed limits of major roads led to significantly greater crash occurrences. Four-leg stop-controlled intersections have 35% greater total crashes, 49% greater fatal and injury crashes, and 25% greater property damage only (PDO) crashes, relative to three-leg intersections.     

Investigating fatal and injury crash patterns of teen drivers with unsupervised learning algorithms

Teenagers have been emphasized as a critical driver population class because of their overrepresentation in fatal and injury crashes. The conventional parametric approaches rest on few predefined assumptions, which might not always be valid considering the complicated nature of teen drivers' crash characteristics that are reflected by multidimensional crash datasets. Also, individual attributes may be more speculative when combined with other factors. This research employed joint correspondence analysis (JCA) and association rule mining (ARM) to investigate the fatal and injury crash patterns of at-fault teen drivers (aged 15 to 19 years) in Louisiana. The unsupervised learning algorithms can explore meaningful associations among crash categories without restricting the nature of variables. The analyses discover intriguing associations to understand the potential causes and effects of crashes. For example, alcohol impairment results in fatal crashes with passengers, daytimes severe collisions occur to unrestrained drivers who have exceeded the posted speed limits, and adverse weather conditions are associated with moderate injury crashes. The findings also reveal how the behavior patterns connected with teen driver crashes, such as distracted driving in the morning hours, alcohol intoxication or using cellphone in pickup trucks, and so on. The research results can lead to effectively targeted teen driver education programs to mitigate risky driving maneuvers. Also, prioritizing crash attributes of key interconnections can help to develop practical safety countermeasures. Strategy that covers multiple interventions could be more effective in curtailing teenagers' crash risk.     

Extremely serious crashes on urban roadway networks: Patterns and trends

Extremely serious traffic crashes, defined as having a death toll of two and greater than two, have become a serious safety concern on urban roadways in Louisiana. This study examined the different contributing factors of these crashes to determine significant trends and patterns. We collected traffic crash data from Louisiana during the period of 2013 to 2017 and found that a total of 72 extremely serious crashes (around 2% of all traffic fatalities) occurred on Louisiana urban roadway networks. As crash data contain an enormous list of contributing factors, there was an issue of ‘more features than data points’ in solving the research problem. Most of these variables are categorial in nature. We selected a dimension reduction tool called Taxicab Correspondence Analysis (TCA) to investigate the complex interaction between multiple factors under a two-dimensional map. Findings of the study reveal several key clusters of attributes that show patterns of association between different crash attributes. The conclusions of this study are exploratory, and the results can help in better visualizing the association between key attributes of crashes. The findings have potentials in designing suitable countermeasures to reduce extremely serious crashes.     

Using machine leaning techniques for evaluation of motorcycle injury severity

There is a growing interest in the application of the machine learning techniques in predicting the motorcycle crash severity. This is partly due to a progress in autonomous vehicles technology, and machine learning technique, which as a main component of autonomous vehicle could be implemented for traffic safety enhancement. Wyoming's motorcycle crash fatalities constitute a concern since the count of riders being killed in motorcycle crashes in 2014 was 11% of the total road fatalities in the state. The first step of crash reduction could be achieved through identification of contributory factors to crashes. This could be accomplished by using a right model with high accuracy in predicting crashes. Thus, this study adopted random forest, support vector machine, multivariate adaptive regression splines and binary logistic regression techniques to predict the injury severity outcomes of motorcycle crashes. Even though researchers applied all the aforementioned techniques to model motorcycle injury severities, a comparative analysis to assess the predictive power of such modeling frameworks is limited. Hence, this study contributes to the road safety literature by comparing the performance of the discussed techniques. In this study, Wyoming's motorcycle crash injury severities are modeled as functions of the characteristics that give rise to crashes. Before conducting any analyses, feature reduction was used to identify a best number of predictors to be included in the model. Also to have an unbiased estimation of the performance of different machine learning techniques, 5-fold cross-validation was used for model performance evaluation. Two measure, Area under the curve (AUC), and confusion matrix were used to compare different models' performance. The machine learning results indicate that random forest model outperformed the other models with the least misclassification and higher AUC. It was also revealed that a dichotomous response variable, with fatality and incapacitation injury in one category, along with all other categories in another group would result in a lower misclassification rate than a polychotomous response variable. This might result from the nature of motorcycle crashes, lacking a protection compared with passenger cars, preventing machine learning technique to get trained properly. Moreover, the most important variables identified by the random forest model are those related to the operating speed, resentful other party, traffic volume, truck traffic volume, riding under the influence, horizontal curvature, wide roadway with more than two lanes and rider's age.     

Safety evaluation of unsignalized and signalized restricted crossing U-turn (RCUT) intersections in rural and suburban areas based on prior control type

The focus of this paper is on evaluating the safety effectiveness of restricted crossing U-turn (RCUT) intersections in rural and suburban areas based on prior control type. Both, unsignalized and signalized RCUT intersections were evaluated using the Empirical Bayes (EB) before-after evaluation method. The 42 RCUT intersections selected for this research were converted from a two-way stop-controlled (TWSC) intersection or signalized intersection in the rural and suburban areas. The results show a 70.63% reduction in the total number of crashes and a 76.10% reduction in the number of fatal and injury crashes at unsignalized stop-controlled RCUT intersections in the rural area. Also, an 89.25% reduction in the total number of crashes and a 94.42% reduction in the number of fatal and injury crashes was observed at offset three-legged unsignalized stop-controlled RCUT intersections converted from four-legged TWSC intersections in rural areas. In the suburban areas, a 64.86% reduction in the total number of crashes and a 73.39% reduction in the number of fatal and injury crashes was observed at unsignalized stop-controlled RCUT intersections. Further, a 10.15% and a 31.08% reduction in the total number of crashes, and an 84.26% and 41.31% reduction in the number of fatal and injury crashes was observed at a signalized RCUT intersection in the rural and suburban areas, respectively. The safety effectiveness of unsignalized RCUT intersections in the rural areas with a larger sample size was found to be higher than was observed by researchers in the past. While unsignalized RCUT intersections in the suburban areas are effective, there is not enough evidence to support the installation of signalized RCUT intersections. These findings help researchers and practitioners in making informed decisions and installing RCUT intersections from a safety perspective.     

Influence of segment length on the fitness of multivariate crash prediction models applied to a Brazilian multilane highway

Road safety modeling enables the development of crash prediction models and the investigation of which factors contribute to crash occurrence. Developing multivariate response models is also valuable, but such models are currently under-exploited. Machine learning techniques, especially artificial neural networks (ANN), have been presented as possible alternatives. Furthermore, selecting a proper roadway segmentation is one of the first tasks in the standard crash modeling workflow. However, this is a challenging task, especially in terms of choosing a segment length. This article presents a study of the influence of segment length on the development of multivariate response models (i.e., three response variables: property damage only crashes, injured victims crashes, and fatal crashes). The models use ANN for a road segment of a Brazilian divided multilane highway. The highway to be modeled was divided into segments with 10 different fixed lengths. The model characterization included geometric and operational data available for the years from 2011 to 2017. The models were evaluated in terms of errors and by residual plot analysis. The 5-km segment of the northbound carriageway and the 4.5-km segment of the southbound carriageway presented the smallest errors and the highest values of R². The residual analyses confirmed the trend to improve the model with the greater segment lengths. This was clear by the residues' distribution around zero, except for the output “Fatal crashes”. The better performance of the longer segments models was expected because these models aggregate more crashes into one segment. The reduction of no crash observations also facilitated the improvement of the models' goodness-of-fit. The use of ANNs also revealed its potential value. However, it is still important to seek strategies to deal with the excess of zeros in fatal crashes; a problem that also occurs in the traditional statistical modeling process.     

Road characteristics and environment factors associated with motorcycle fatal crashes in Malaysia

This study aims to determine risk factors contributing to traffic crashes in 9,176 fatal cases involving motorcycle in Malaysia between 2010 and 2012. For this purpose, both multinomial and mixed models of motorcycle fatal crash outcome based on the number of vehicle involved are estimated. The corresponding model predicts the probability of three fatal crash outcomes: motorcycle single-vehicle fatal crash, motorcycle fatal crash involving another vehicle and motorcycle fatal crash involving two or more vehicles. Several road characteristic and environmental factors are considered including type of road in the hierarchy, location, road geometry, posted speed limit, road marking type, lighting, time of day and weather conditions during the fatal crash. The estimation results suggest that curve road sections, no road marking, smooth, rut and corrugation of road surface and wee hours, i.e. between 00.00 am to 6 am, increase the probability of motorcycle single-vehicle fatal crashes. As for the motorcycle fatal crashes involving multiple vehicles, factors such as expressway, primary and secondary roads, speed limit more than 70 km/h, roads with non-permissible marking, i.e. double lane line and daylight condition are found to cause an increase the probability of their occurrence. The estimation results also suggest that time of day (between 7 pm to 12 pm) has an increasing impact on the probability of motorcycle single-vehicle fatal crashes and motorcycle fatal crashes involving two or more vehicles. Whilst the multinomial logit model was found as more parsimonious, the mixed logit model is likely to capture the unobserved heterogeneity in fatal motorcycle crashes based on the number of vehicles involved due to the underreporting data with two random effect parameters including 70 km/h speed limit and double lane line road marking.     

Using on-board video data for safety analysis – An analysis of right hook crashes involving large buses at signalized intersections

Bus right hook (BRH) crashes at intersections are one of the most common types of crashes for bus carriers, which accounted for as high as 16% of fatal and injury crashes involving large buses at intersections in Taiwan. A BRH crash occurs when a bus and another vehicle traveling in the same direction head into an intersection, but the bus driver makes a right turn across the path of the through-moving vehicle, and both vehicles collide. This study responds to the research needs to identity factors associated with BRH crashes by utilizing in-vehicle data recorder (IVDR) data. A four step analysis procedure was developed, including (1) video data coding, (2) crash sequence analysis to identify crash contributing factors, (3) a case-control study to examine the relationship between the crash contributing factors and crash occurrence, and (4) modeling crash risk in terms of the crash contributing factors to better understand the crash generating process. This study first identified the existence of driver unattended time as the time between when the driver last checked the right back mirror to finally steering for a right turn, indicating the time period wherein the driver did not track the through vehicle on the right side using the right back mirror. It was found that BRH crashes could be attributed to the concurrence of unattended time and the speed difference between the bus and through vehicle. Several recommendations are discussed based on the results to further develop countermeasures to reduce this type of crash.     

Pedestrian injury outcomes in the developing urban metropolis: Econometric models for assessing risk factors

Pedestrian fatality and injury is one of the most concerning issues around the globe. The predictors for such mishaps have been investigated in the developed countries through econometric models and are proven useful techniques. Such studies in the context of developing countries, especially for urban cities, are however still very scarce. Using five years reported pedestrian crash data, this study looks into the performance of three statistical models - Multinomial Logit (MNL), Ordered Logit (OL) and Partial Proportional Odds (PPO) model while examining the impact of various attributes related to pedestrian crashes severity outcomes for Dhaka metropolitan city in Bangladesh. The comparative analysis reveals that the performance of the PPO model is relatively better for the available dataset in terms of identifying critical risk factors. Undivided roadway, heavy vehicles, unfit vehicles, adult drivers with no seat belt use, young and older pedestrians, pedestrian road crossing action are found to be associated with higher probability of fatal injuries. In contrast, one-way traffic movement, daytime, motorcycles and mid-aged pedestrians decrease the likelihood of fatal injury. Based on these identified risk factors, a combined 3-E approach has been suggested to reduce the severity levels of pedestrian in the event of crash occurrence.     

山区高速公路隧道路段与开放路段的事故影响因素分析

高速公路隧道构造特殊且通行环境复杂,因而通常事故多发。为探究高速公路隧道路段与开放路段事故影响因素和严重程度致因机理的差异,采集沪昆高速邵怀段2011—2016年期间1 537起事故为研究样本;以事故发生路段为响应变量构建逻辑回归模型,解释各种风险因素对事故发生路段倾向性的影响差异;分别针对隧道路段与开放路段建立模型研究事故伤害严重程度的影响因素。建立二元Logit回归模型分析事故的发生倾向性和2类路段的事故严重程度的影响因素;采用随机参数Logit模型以反映异质性条件对参数的影响。统计表明:与疲劳驾驶、未保持安全距离相关的事故发生在隧道路段的概率更高,其事故发生概率分别是开放路段的2.373和2.482倍;与隧道路段事故严重程度正相关的因素包括下坡（坡度2%以上）、夏季和超速行驶,其中下坡（坡度2%以上）段的严重事故发生的概率为上坡（坡度2%以上）的3.397倍,夏季的严重事故发生概率为秋季的3.951倍,超速行驶相关的严重事故发生概率为其他不当驾驶行为的4.242倍;与开放路段事故严重程度正相关的因素包括超速行驶和疲劳驾驶,其中超速行驶相关的严重事故概率是其他不当驾驶行为的2.713倍,疲劳驾驶相关的严重事故概率是其他不当驾驶行为的4.802倍。研究表明,山区高速公路隧道路段与开放路段的事故发生概率及其严重程度的影响因素存在一定的差异性,研究结论可为山区高速公路差异管理方案制定提供依据。      

Run-off-road and recovery – state estimation and vehicle control strategies

Despite many advances in vehicle safety technology, traffic fatalities remain a devastating burden on society. With over two-thirds of all fatal single-vehicle crashes occurring off the roadway, run-off-road (ROR) crashes have become the focus of much roadway safety research. Current countermeasures, including roadway infrastructure modifications and some on-board vehicle safety systems, remain limited in their approach as they do not directly address the critical factor of driver behaviour. It has been shown that ROR crashes are often the result of poor driver performance leading up to the crash. In this study, the performance of two control algorithms, sliding control and linear quadratic control, was investigated for use in an autonomous ROR vehicle recovery system. The two controllers were simulated amongst a variety of ROR conditions where typical driver performance was inadequate to safely operate the vehicle. The sliding controller recovered the fastest within the nominal conditions but exhibited large variability in performance amongst the more extreme ROR scenarios. Despite some small sacrifices in lateral error and yaw rate, the linear quadratic controller demonstrated a higher level of consistency and stability amongst the various conditions examined. Overall, the linear quadratic controller recovered the vehicle 25% faster than the sliding controller while using 70% less steering, which combined with its robust performance, indicates its high potential as an autonomous ROR countermeasure.     

Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

Predicting downgrade crash frequency with the random-parameters negative binomial model: Insights into the impacts of geometric variables on downgrade crashes in Wyoming

Road deaths, injuries and property damage place a huge burden on the economy of most nations. Wyoming has a high crash rate on mountain passes. The crash rates observed in the state is as a result of many factors mainly related to the challenging mountainous terrain in the state, which places extra burden on drivers in terms of requiring higher levels of alertness and driving skill. This study was conducted to investigate factors leading to crashes on Wyoming downgrades, with a focus on geometric variables. Traditionally, crash frequency analysis is conducted using count models such as Poisson or negative binomial models. However, factors that affect crash frequency are known to vary across observations. The use of a methodology that fails to take into account heterogeneity in observed and unobserved effects relating to roadway characteristics can lead to biased and inconsistent estimates. Inferences made from such parameter estimates may be misleading. This study employed the random-parameters negative binomial regression models to evaluate the impact of geometric variables on crash frequency. Five separate models were estimated for total, fatal/injury, property damage only (PDO), truck, and non-truck crash frequencies. Several geometric and traffic variables were found to influence the frequency of crashes on downgrades. These included segment length, vertical grade, shoulder width, lane width, presence of downgrade warning sign, vertical curve length, presence of a passing lane, percentage of trucks, number of lanes and AADT. The results suggest that segment length, lane width, presence of a passing lane, presence of a downgrade warning sign, vertical grade, and percentage of trucks are best modeled as random parameters. The findings of this study will provide transportation agencies with a better understanding of the impact of geometric variables on downgrade crashes.     

Macroscopic analysis on the frequency and severity of pedestrian crashes on National Roads in Metro Manila,Philippines

Pedestrians are the most vulnerable road users; thus, understanding the primary factors that lead to pedestrian crashes is a chief concern in road safety. However, owing to the limitations of crash data in developing countries, only a few studies have evaluated the comprehensive characteristics of pedestrian crashes, specifically on different road types. This study attempted to develop pedestrian crash frequency and severity models on national roads by using the road characteristics and built environment parameters, based on the road crash data (2016–2018) that involved pedestrians in Metro Manila, Philippines. Remarkable findings included primary roads, presence of footbridges, road sections with bad surface conditions, and increased fractions of commercial, residential, and industrial roads, which exhibited a greater likelihood of pedestrian crashes. Crashes involving elderly pedestrians, heavier vehicles, late-night hours, fair surface conditions, and open spaces were associated with increased likelihoods of fatal outcomes. Essentially, this study provides a macroscopic perspective in understanding the factors associated with the severity and frequency of pedestrian crashes, and it would aid the authorities in identifying proper countermeasures.     

An analysis of motorcyclists' injury severities in work-zone crashes with unobserved heterogeneity

Work-zone related crashes in Florida are increasing steadily in recent years. With such growing concern, work-zone is recognized in Florida Strategic Highway Safety Plan. The severity and complexity of motorcycle crashes in work-zones is critically important and worth investigating. However, the resulting effect of work-zone on motorcyclists' injuries in work-zone crashes is not fully understood. The purpose of study is to identify the contributing factors of motorcyclists' injury severity sustained in the work-zone crashes in Florida. Recognizing the relatively higher risk of motorcyclists in work-zones with respective to non-work-zones, this study further uncovers the contributing factors for single- and multi-vehicle motorcycle crashes in Florida work-zones. This study investigated motorcyclists' injury severity applying random parameter multinomial logit with possible heterogeneity in means and variances of the random parameters for single-motorcycle and multi-vehicle motorcycle crashes. This study utilizes the Crash Analysis Reporting (CAR) system in Florida over a period of five years from 2012 to 2016 (inclusive). The model result indicates a complex relationship between dark condition, old-aged motorcyclist (50–65), requirement and absence of endorsement, partial ejection, straight roadway segment, shoulder width (up to 1.22 m (4 ft), and 2.74–3.66 m (9–12 ft), urban interstate, activity area, and lane closure and work on shoulder-median work-zone types. The effect of work-zone on single-motorcycle crashes tends to have much more in resulting injury severities relative to multi-vehicle motorcycle crashes. It is more important to investigate the injury severity by single- and multi-vehicle crashes involving motorcycles in work-zones. These risk factors identified in the study are expected to provide more insights for the countermeasures specific to engineering (roadway design) and policy (motorcycle training), which can be considered to improve motorcycle safety in Florida.     

Inclusion of speed and weather measures in safety performance functions for rural roadways

The research on relationships among vehicle operating speed, roadway design elements, weather, and traffic volume on crash outcomes will greatly benefit the road safety profession in general. If these relationships are well understood and characterized, existing techniques and countermeasures for reducing crash frequencies and crash severities could potentially improve, and the opportunity for new methodologies addressing and anticipating crash occurrence would naturally ensue. This study examines the prevailing operating speeds on a large scale and determines how traffic speeds and different speed measures interact with roadway characteristics and weather condition to influence the likelihood of crashes. This study used three datasets from Washington and Ohio: 1) Highway Safety Information System (HSIS), 2) the National Performance Management Research Dataset (NPMRDS), and 3) National Oceanic and Atmospheric Administration (NOAA) weather data. State-based conflated databases were developed using the linear conflation of HSIS and NPMRDS. The results show that certain speed measures were found to be beneficial in quantifying safety risk. Annual-level crash prediction models show that increased variability in hourly operating speed within a day and an increase in monthly operating speeds within a year are both associated with a higher number of crashes. Safety practitioners can benefit from the current study in addressing the issue of speed and weather in crash outcomes.     

Sampling bias and weight factors for in-depth motorcycle crash data in Thailand

Motorcycle crashes are documented in Thailand's national records but are underreported and lacking detail. In-depth motorcycle crash data, collected by Thailand Accident Research Center (TARC), contains a smaller number of motorcycle crashes but more detail. However, to draw conclusions at a national level, representativeness of the TARC in-depth data is currently unknown, and the correction of sampling biases may be required. In this study, the Capture-recapture method was used to examine the underreporting in the national crash data (from the government insurance company). It was found that 69% of fatal and 70% of non-fatal injuries were underreported, respectively. The in-depth crash data was found to be biased. The weighting methods post-stratification and iterative proportional fitting were applied to compensate for the bias and are shown to improve the representativeness of the in-depth motorcycle crash data. Weighted in-depth crash data appears to be suitable to draw conclusions on motorcyclist safety in Thailand.     

Effects of the autonomous vehicle crashes on public perception of the technology

In March 2018, an Uber-pedestrian crash and a Tesla's Model X crash attracted a lot of media attention because the vehicles were operating under self-driving and autopilot mode respectively at the time of the crash. This study aims to conduct before-and-after sentiment analysis to examine how these two fatal crashes have affected people's perceptions of self-driving and autonomous vehicle technology using Twitter data. Five different and relevant keywords were used to extract tweets. Over 1.7 million tweets were found within 15 days before and after the incidents with the specific keywords, which were eventually analyzed in this study. The results indicate that after the two incidents, the negative tweets on “self-driving/autonomous” technology increased by 32 percentage points (from 14% to 46%). The compound scores of “pedestrian crash”, “Uber”, and “Tesla” keywords saw a 6% decrease while “self-driving/autonomous” recorded the highest change with an 11% decrease. Before the Uber-incident, 19% of the tweets on Uber were negative and 27% were positive. With the Uber-pedestrian crash, these percentages have changed to 30% negative and 23% positive. Overall, the negativity in the tweets and the percentage of negative tweets on self-driving/autonomous technology have increased after their involvement in fatal crashes. Providing opportunities to interact with this developing technology has shown to positively influence peoples' perception.     

Non-fatal spine injuries resulting from motorcycle crashes

This study aims to determine spinal injury patterns and identify crash factors commonly associated with serious spinal injury as a result of motorcycle crashes. Data was retrospectively collected from motorcyclists sustaining spinal injuries from road crashes treated at Kuala Lumpur Hospital, Malaysia, over the 5-year period from 2005 to 2009. Each patient's injuries were analyzed by reviewing his or her medical records for radiographic imaging and computed tomography scans.A total of 151 patients were included in this study, of which, males accounted for over 87%. The first lower lumbar (L1) was the most commonly injured vertebral level, followed by the adjacent thoracic vertebra (T12). Fracture to the vertebral body without dislocation was found to be the most frequently observed spinal injury pattern. Injury severities for a majority of patients (65%) were measured at Maximum Abbreviated Injury Scale (MAIS) of 2. Serious spinal injury was associated with thorax or upper-extremity injury.Prevalence of lumbar spinal injury in the study reflects a predominantly low-speed crash among the motorcyclist in the region. Motorcyclists are at greater odd to sustain severe spinal injury when directly striking an object compare to striking the ground during the crash event.