Driving ambulances safely: findings of ten years of Japanese ambulance crash data

Motor vehicle crash countermeasures often are selected after an extensive data analysis of the crash history of a roadway segment. The value of this analysis depends on the accuracy or precision with which the crash itself is located. yet this crash location only is as accurate as the estimate of the police officer. Global Positioning System (GPS) technology may have the potential to increase data accuracy and decrease the time spent to record crash locations. Over 10 months, 32 motor vehicle crash locations were determined by using both conventional methods and hand-held GPS receivers, and the timeliness and precision of the methods were compared. Local crash data analysts were asked how the improved precision affected their consideration of potential crash countermeasures with regard to five crashes selected from the sample. On average, measuring a crash location by using GPS receivers added up to 10 extra minutes, depending on the definition of the crash location, the technology employed, and how that technology was applied. The average difference between conventional methods of measuring the crash location and either GPS or a wheel ranged from 5 m (16 ft) to 39 m (130 ft), depending on how one defined the crash location. Although there are instances in which improved precision will affect the evaluation of crash countermeasures, survey respondents and the literature suggest that problems with conventional crash location methods often arise from human error, not a lack of precision inherent in the technology employed.

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Evaluating the Safety Risk of Rural Roadsides Using a Bayesian Network Method

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph16071166 ◽

2019 ◽

Vol 16 (7) ◽

pp. 1166 ◽

Cited By ~ 3

Author(s):

Tianpei Tang ◽

Senlai Zhu ◽

Yuntao Guo ◽

Xizhao Zhou ◽

Yang Cao

Keyword(s):

Bayesian Network ◽

Low Cost ◽

Road Networks ◽

Access Point ◽

Safety Risk ◽

Crash Data ◽

Risk Levels ◽

The Road ◽

Run Off ◽

Point Density

Evaluating the safety risk of rural roadsides is critical for achieving reasonable allocation of a limited budget and avoiding excessive installation of safety facilities. To assess the safety risk of rural roadsides when the crash data are unavailable or missing, this study proposed a Bayesian Network (BN) method that uses the experts’ judgments on the conditional probability of different safety risk factors to evaluate the safety risk of rural roadsides. Eight factors were considered, including seven factors identified in the literature and a new factor named access point density. To validate the effectiveness of the proposed method, a case study was conducted using 19.42 km long road networks in the rural area of Nantong, China. By comparing the results of the proposed method and run-off-road (ROR) crash data from 2015–2016 in the study area, the road segments with higher safety risk levels identified by the proposed method were found to be statistically significantly correlated with higher crash severity based on the crash data. In addition, by comparing the respective results evaluated by eight factors and seven factors (a new factor removed), we also found that access point density significantly contributed to the safety risk of rural roadsides. These results show that the proposed method can be considered as a low-cost solution to evaluating the safety risk of rural roadsides with relatively high accuracy, especially for areas with large rural road networks and incomplete ROR crash data due to budget limitation, human errors, negligence, or inconsistent crash recordings.

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Exploratory Analysis of Pedestrian Road Trauma in Finland

Sustainability ◽

10.3390/su13126715 ◽

2021 ◽

Vol 13 (12) ◽

pp. 6715

Author(s):

Steve O’Hern ◽

Roni Utriainen ◽

Hanne Tiikkaja ◽

Markus Pöllänen ◽

Niina Sihvola

Keyword(s):

Cluster Analysis ◽

Motor Vehicle ◽

Seasonal Effects ◽

Unique Element ◽

Driver Behaviour ◽

Crash Data ◽

Safety Issues ◽

Key Issues ◽

Development Goals ◽

Pedestrian Crashes

In Finland, all fatal on-road and off-road motor vehicle crashes are subject to an in-depth investigation coordinated by the Finnish Crash Data Institute (OTI). This study presents an exploratory and two-step cluster analysis of fatal pedestrian crashes between 2010 and 2019 that were subject to in-depth investigations. In total, 281 investigations occurred across Finland between 2010 and 2019. The highest number of cases were recorded in the Uusimaa region, including Helsinki, representing 26.4% of cases. Females (48.0%) were involved in fewer cases than males; however, older females represented the most commonly injured demographic. A unique element to the patterns of injury in this study is the seasonal effects, with the highest proportion of crashes investigated in winter and autumn. Cluster analysis identified four unique clusters. Clusters were characterised by crashes involving older pedestrians crossing in low-speed environments, crashes in higher speed environments away from pedestrian crossings, crashes on private roads or in parking facilities, and crashes involving intoxicated pedestrians. The most common recommendations from the investigation teams to improve safety were signalisation and infrastructure upgrades of pedestrian crossings, improvements to street lighting, advanced driver assistance (ADAS) technologies, and increased emphasis on driver behaviour and training. The findings highlight road safety issues that need to be addressed to reduce pedestrian trauma in Finland, including provision of safer crossing facilities for elderly pedestrians, improvements to parking and shared facilities, and addressing issues of intoxicated pedestrians. Efforts to remedy these key issues will further Finland’s progression towards meeting Vision Zero targets while creating a safer and sustainable urban environment in line with the United Nations sustainable development goals.

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Can crowdsourcing create the missing crash data?

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies ◽

10.1145/3378393.3402264 ◽

2020 ◽

Author(s):

Sveta Milusheva ◽

Robert Marty ◽

Guadalupe Bedoya ◽

Elizabeth Resor ◽

Sarah Williams ◽

...

Keyword(s):

Crash Data

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Are Older Drivers Safe on Interchanges? Analyzing Driving Errors Causing Crashes

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211031232 ◽

2021 ◽

pp. 036119812110312

Author(s):

Denis Elia Monyo ◽

Henrick J. Haule ◽

Angela E. Kitali ◽

Thobias Sando

Keyword(s):

Latent Class ◽

Time Of Day ◽

Older Drivers ◽

Contributing Factors ◽

Lighting Condition ◽

Crash Data ◽

Traffic Characteristics ◽

Road Users ◽

Traffic Conflicts ◽

Penalized Logistic Regression

Older drivers are prone to driving errors that can lead to crashes. The risk of older drivers making errors increases in locations with complex roadway features and higher traffic conflicts. Interchanges are freeway locations with more driving challenges than other basic segments. Because of the growing population of older drivers, it is vital to understand driving errors that can lead to crashes on interchanges. This knowledge can assist in developing countermeasures that will ensure safety for all road users when navigating through interchanges. The goal of this study was to determine driver, environmental, roadway, and traffic characteristics that influence older drivers’ errors resulting in crashes along interchanges. The analysis was based on three years (2016–2018) of crash data from Florida. A two-step approach involving a latent class clustering analysis and the penalized logistic regression was used to investigate factors that influence driving errors made by older drivers on interchanges. This approach accounted for heterogeneity that exists in the crash data and enhanced the identification of contributing factors. The results revealed patterns that are not obvious without a two-step approach, including variables that were not significant in all crashes, but were significant in specific clusters. These factors included driver gender and interchange type. Results also showed that all other factors, including distracted driving, lighting condition, area type, speed limit, time of day, and horizontal alignment, were significant in all crashes and few specific clusters.

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Analysis of Severe Injuries in Crashes Involving Large Trucks Using K-Prototypes Clustering-Based GBDT Model

Safety ◽

10.3390/safety7020032 ◽

2021 ◽

Vol 7 (2) ◽

pp. 32

Author(s):

Syed As-Sadeq Tahfim ◽

Chen Yan

Keyword(s):

Injury Severity ◽

Unobserved Heterogeneity ◽

The United States ◽

Clustering Methods ◽

Severe Injuries ◽

Traffic Crash ◽

Crash Data ◽

Boosted Decision Trees ◽

The Individual ◽

Crash Characteristics

The unobserved heterogeneity in traffic crash data hides certain relationships between the contributory factors and injury severity. The literature has been limited in exploring different types of clustering methods for the analysis of the injury severity in crashes involving large trucks. Additionally, the variability of data type in traffic crash data has rarely been addressed. This study explored the application of the k-prototypes clustering method to countermeasure the unobserved heterogeneity in large truck-involved crashes that had occurred in the United States between the period of 2016 to 2019. The study segmented the entire dataset (EDS) into three homogeneous clusters. Four gradient boosted decision trees (GBDT) models were developed on the EDS and individual clusters to predict the injury severity in crashes involving large trucks. The list of input features included crash characteristics, truck characteristics, roadway attributes, time and location of the crash, and environmental factors. Each cluster-based GBDT model was compared with the EDS-based model. Two of the three cluster-based models showed significant improvement in their predicting performances. Additionally, feature analysis using the SHAP (Shapley additive explanations) method identified few new important features in each cluster and showed that some features have a different degree of effects on severe injuries in the individual clusters. The current study concluded that the k-prototypes clustering-based GBDT model is a promising approach to reveal hidden insights, which can be used to improve safety measures, roadway conditions and policies for the prevention of severe injuries in crashes involving large trucks.

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Examining Driver Injury Severity in Single-Vehicle Road Departure Crashes Involving Large Trucks

Transportation Research Record Journal of the Transportation Research Board ◽

10.1177/03611981211010178 ◽

2021 ◽

pp. 036119812110101

Author(s):

Mehdi Hosseinpour ◽

Kirolos Haleem

Keyword(s):

Rural Areas ◽

Injury Severity ◽

Factors Affecting ◽

Crash Data ◽

Natural Logarithm ◽

Concrete Barriers ◽

Local Roads ◽

Single Vehicle ◽

Safety Countermeasures ◽

Injury Severities

Road departure (RD) crashes are among the most severe crashes that can result in fatal or serious injuries, especially when involving large trucks. Most previous studies neglected to incorporate both roadside and median hazards into large-truck RD crash severity analysis. The objective of this study was to identify the significant factors affecting driver injury severity in single-vehicle RD crashes involving large trucks. A random-parameters ordered probit (RPOP) model was developed using extensive crash data collected on roadways in the state of Kentucky between 2015 and 2019. The RPOP model results showed that the effect of local roadways, the natural logarithm of annual average daily traffic (AADT), the presence of median concrete barriers, cable barrier-involved collisions, and dry surfaces were found to be random across the crash observations. The results also showed that older drivers, ejected drivers, and drivers trapped in their truck were more likely to sustain severe single-vehicle RD crashes. Other variables increasing the probability of driver injury severity have included rural areas, dry road surfaces, higher speed limits, single-unit truck types, principal arterials, overturning-consequences, truck fire occurrence, segments with median concrete barriers, and roadside fixed object strikes. On the other hand, wearing seatbelt, local roads and minor collectors, higher AADT, and hitting median cable barriers were associated with lower injury severities. Potential safety countermeasures from the study findings include installing median cable barriers and flattening steep roadside embankments along those roadway stretches with high history of RD large-truck-related crashes.

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GLM-Based Flexible Monitoring Methods: An Application to Real-Time Highway Safety Surveillance

Symmetry ◽

10.3390/sym13020362 ◽

2021 ◽

Vol 13 (2) ◽

pp. 362

Author(s):

Arshad Jamal ◽

Tahir Mahmood ◽

Muhamad Riaz ◽

Hassan M. Al-Ahmadi

Keyword(s):

Real Time ◽

Control Charts ◽

Negative Binomial ◽

Highway Safety ◽

Monitoring Methods ◽

Count Data Models ◽

Crash Data ◽

Safety Surveillance ◽

Study Results ◽

Best Fit

Statistical modeling of historical crash data can provide essential insights to safety managers for proactive highway safety management. While numerous studies have contributed to the advancement from the statistical methodological front, minimal research efforts have been dedicated to real-time monitoring of highway safety situations. This study advocates the use of statistical monitoring methods for real-time highway safety surveillance using three years of crash data for rural highways in Saudi Arabia. First, three well-known count data models (Poisson, negative binomial, and Conway–Maxwell–Poisson) are applied to identify the best fit model for the number of crashes. Conway–Maxwell–Poisson was identified as the best fit model, which was used to find the significant explanatory variables for the number of crashes. The results revealed that the road type and road surface conditions significantly contribute to the number of crashes. From the perspective of real-time highway safety monitoring, generalized linear model (GLM)-based exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are proposed using the randomized quantile residuals and deviance residuals of Conway–Maxwell (COM)–Poisson regression. A detailed simulation-based study is designed for predictive performance evaluation of the proposed control charts with existing counterparts (i.e., Shewhart charts) in terms of the run-length properties. The study results showed that the EWMA type control charts have better detection ability compared with the CUSUM type and Shewhart control charts under small and/or moderate shift sizes. Finally, the proposed monitoring methods are successfully implemented on actual traffic crash data to highlight the efficacy of the proposed methods. The outcome of this study could provide the analysts with insights to plan sound policy recommendations for achieving desired safety goals.

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Autonomous Vehicles and Vulnerable Road-Users—Important Considerations and Requirements Based on Crash Data from Two Countries

Behavioral Sciences ◽

10.3390/bs11070101 ◽

2021 ◽

Vol 11 (7) ◽

pp. 101

Author(s):

Andrew Paul Morris ◽

Narelle Haworth ◽

Ashleigh Filtness ◽

Daryl-Palma Asongu Nguatem ◽

Laurie Brown ◽

...

Keyword(s):

Autonomous Vehicles ◽

Red Light ◽

Road User ◽

Red Light Running ◽

Crash Data ◽

Passenger Vehicles ◽

Road Users ◽

Vulnerable Road User ◽

Vulnerable Road Users ◽

Road Intersections

(1) Background: Passenger vehicles equipped with advanced driver-assistance system (ADAS) functionalities are becoming more prevalent within vehicle fleets. However, the full effects of offering such systems, which may allow for drivers to become less than 100% engaged with the task of driving, may have detrimental impacts on other road-users, particularly vulnerable road-users, for a variety of reasons. (2) Crash data were analysed in two countries (Great Britain and Australia) to examine some challenging traffic scenarios that are prevalent in both countries and represent scenarios in which future connected and autonomous vehicles may be challenged in terms of safe manoeuvring. (3) Road intersections are currently very common locations for vulnerable road-user accidents; traffic flows and road-user behaviours at intersections can be unpredictable, with many vehicles behaving inconsistently (e.g., red-light running and failure to stop or give way), and many vulnerable road-users taking unforeseen risks. (4) Conclusions: The challenges of unpredictable vulnerable road-user behaviour at intersections (including road-users violating traffic or safe-crossing signals, or taking other risks) combined with the lack of knowledge of CAV responses to intersection rules, could be problematic. This could be further compounded by changes to nonverbal communication that currently exist between road-users, which could become more challenging once CAVs become more widespread.

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