Errors in accident data, its types, causes and methods of rectification-analysis of the literature

Hazardous traffic scenarios for motorcyclists in Indonesia: a comprehensive insight from police accident data and self-reports

Motorcycle safety remains a concern in low- and middle-income countries. This study addresses this issue by identifying hazardous scenarios for motorcyclists in Indonesia. We conducted a two-step cluster analysis and injury analysis to examine motorcycle accidents based on the police accident dataset (2020-2021) of Brebes Regency, Indonesia. We integrated the findings with accident self-reports from 104 young motorcyclists using a joint display to obtain a more comprehensive insight. As a result, we identified four hazardous traffic scenarios: motorcycle-to-vehicle collisions on median roads, motorcycle-to-vehicle collisions on non-median roads, motorcycle-to-pedestrian collisions, and single-motorcycle collisions. We suggest countermeasures for each scenario and propose a public transport policy as a safer mobility solution. Applying a two-step cluster analysis on accident data and integrating the findings of accident data and self-report analysis proved beneficial in this study. Therefore, we encourage the use of this novel approach in future studies.

The absence of data on driving under the influence of alcohol in road traffic studies: a scoping review of non-randomized studies with vote counting based on the direction of effects of alcohol policies

BACKGROUND

Data on driving under the influence of alcohol (DUIA) are not always available, accurate, or reliable, making it difficult to study the effects of alcohol policies on road traffic outcomes. The objectives of our study were twofold: 1) to describe how road traffic outcomes of alcohol policies are assessed when DUIA data are missing, and 2) to explore the effects of alcohol policies when DUIA data are missing.

METHODS

We conducted a scoping review of non-randomized studies that assessed the road traffic outcomes of alcohol policies when DUIA data are missing. Until November 2021, we searched studies published between 2000 and 2021, in English or French, via MEDLINE, APA PsycInfo, CINAHL, and SocINDEX. We assessed the risk of bias in the included studies with the Quality Assessment Tool for Before-After (Pre-Post) Studies With No Control Group. The selection process, data extraction, and the risk of bias assessment were conducted independently and in duplicate. We used vote counting based on the direction of the effects of alcohol policies as a synthesis method. The protocol for this review was published in PROSPERO under record number CRD42021266744.

RESULTS

Twenty-four eligible studies were included. Regarding objective 1, most studies used uncontrolled interrupted time series designs to assess road traffic fatalities resulting from night-time crashes. The reasons for missing DUIA data were generally not reported. Regarding objective 2, we found evidence for an association between alcohol policies and decreased road traffic fatalities. Subgroup analyses found no evidence for an association between methodological modifiers and positive effect directions for road traffic fatalities.

CONCLUSION

Caution is needed when interpreting road traffic outcomes associated with alcohol policies when DUIA data are missing. Greater efforts should be made to improve the reporting of outcomes assessments. Future studies must address several methodological issues (e.g., more granular data, well-defined intervention and implementation, and controlled designs). Our results should be compared to those from others reviews where DUIA data were available to confirm or recalibrate the associations found in studies where DUIA data were missing.

A data fusion approach of police-hospital linked data to examine injury severity of motor vehicle crashes

Injury severity studies typically rely on police-reported crash data to examine risk factors associated with traffic injuries. The police crash database includes essential information on roadways, crashes and driver-vehicle characteristics but may not contain accurate and sufficient information on traffic injuries. Despite sizable efforts on injury severity modelling, very few studies have employed hospital records to classify injury severities accurately. As such, the inferences drawn from the police-recorded injury severity classifications may be questionable. This study investigates factors affecting road traffic injuries of motor vehicle crashes in two approaches (1) police-reported injury severity data and (2) a data fusion approach linking police and hospital records. Data from 2015 to 2019 were collected from the Abu Dhabi Traffic Police Department and linked with hospital records by the Department of Health, Abu Dhabi. A total of 6,333 casualty crashes were categorised into non-severe, severe, and fatal crashes following police-reported data and non-hospitalised, hospitalised and fatal crashes based on the police-hospital linked data. The state-of-the-art random thresholds random parameters hierarchical ordered Probit models were then employed to examine the differences in factors affecting crash-injury severities between police-reported and police-hospital linked data. While there are similarities between these two approaches, there are numerous notable differences in injury severity factors. For instance, head-on collisions are associated with high crash-injury severities in the model with police-hospital linked data, but they tend to show low injury severities in the model with police-reported data. In addition, the police-reported approach identifies that crashes occurred in remote areas and angle collisions are associated with low injury severities, which is not intuitive. These findings highlight that modelling the misclassified injury severity in police crash data may lead to wrong estimations and misleading inferences. Instead, the data fusion approach of police-hospital linked data provides critical and accurate insights into road traffic injuries and is a valuable approach for understanding traffic injuries.

Reconstruction of a real-world car-to-pedestrian collision using geomatics techniques and numerical simulations

The aim of this study is to provide an improved method for traffic accident reconstruction based on geomatics techniques and numerical simulations. A combination of various techniques was used. First, an unmanned aerial vehicle (UAV), laser scanner and structured-light scanner were used to obtain information on the accident scene, vehicle and victim. The collected traces provided detailed initial impact conditions for subsequent numerical simulations. Then, multi-body system (MBS) simulations were conducted to reconstruct the kinematics of the car-to-pedestrian collision. Finally, a finite element (FE) simulation using the THUMS model was performed to predict injuries. A real-life vehicle-pedestrian collision was used to verify the feasibility and effectiveness of this method. The reconstruction result revealed that the kinematic and injury predictions of the numerical simulations effectively conformed to the surveillance video and investigation of the actual accident. UAV photogrammetry was demonstrated to be more efficient in accident data collection than hand sketch and measurement, and 3D laser scanning enabled an easier and more accurate modeling process of vehicle. The present study shows the feasibility of this method for use in traffic accident reconstruction.

Modelling the relationships between the barriers to implementing machine learning for accident analysis: the Indian petroleum industry

Purpose

This paper aims to identify, prioritise and explore the relationships between the various barriers that are hindering the machine learning (ML) adaptation for analysing accident data information in the Indian petroleum industry.

Design/methodology/approach

The preferred reporting items for systematic reviews and meta-analysis (PRISMA) is initially used to identify key barriers as reported in extant literature. The decision-making trial and evaluation laboratory (DEMATEL) technique is then used to discover the interrelationships between the barriers, which are then prioritised, based on three criteria (time, cost and relative importance) using complex proportional assessment (COPRAS) and multi-objective optimisation method by ratio analysis (MOORA). The Delphi method is used to obtain and analyse data from 10 petroleum experts who work at various petroleum facilities in India.

Findings

The findings provide practical insights for management and accident data analysts to use ML techniques when analysing large amounts of data. The analysis of barriers will help organisations focus resources on the most significant obstacles to overcome barriers to adopt ML as the primary tool for accident data analysis, which can save time, money and enable the exploration of valuable insights from the data.

Originality/value

This is the first study to use a hybrid three-phase methodology and consult with domain experts in the petroleum industry to rank and analyse the relationship between these barriers.

Can the speed limit be used as a surrogate for the striking vehicle's travel speed or delta-V in police-reported rear-end crashes?

Objective: A recent study by Kidd (2022) recommended that organizations evaluating front crash prevention (FCP) systems like automatic emergency braking and forward collision warning increase speed differentials in existing test scenarios from 25 mph to 45 mph to make the tests more representative of police-reported rear-end crashes. Kidd used the posted speed limit as a proxy for the striking vehicle's travel speed prior to the crash. The current study used velocity data from event data recorders (EDRs) in rear-end crashes to evaluate this assumption. These same data were used to replicate another study (Farmer 2003) that showed the speed limit was a poor surrogate for delta-V in rear-end crashes.Method: A total of 11,199 crash records during 2017-2020 were extracted from the Crash Investigation Sampling System database. The analysis was restricted to 436 of these records that involved two vehicles with a front-to-rear manner of collision or rear-end crash configuration with EDR data from the striking vehicle. The relationships between the posted speed limit and striking-vehicle travel speed and between the speed limit and delta-V were modeled using regression.Results: On average, the speed limit overestimated striking-vehicle travel speed by 2 mph, but the relationship between the speed limit and travel speed was not linear. The speed limit reasonably approximated travel speed on roads with speed limits of 30 and 50 mph or higher. It slightly overestimated travel speed on roads with 40-45 mph speed limits and underestimated it on roads with limits of 25 mph or less. The probability that the striking vehicle's travel speed was 25 mph or less on any road was 0.09. In contrast, the probability of the striking vehicle's travel speed being 45 mph or less was 0.54 overall and 0.75 or higher for roads with a speed limit between 25 and 45 mph. As found in prior research, there was no significant relationship between the speed limit and delta-V.Conclusion: The posted speed limit was a reasonable surrogate for the striking vehicle's travel speed prior to police-reported rear-end crashes on roads with a speed limit above 25 mph. It was not a reasonable surrogate for delta-V. Travel speeds on roads with speed limits of 25 mph or less were much higher than the speed limit, which suggests that existing scenarios used to evaluate FCP system performance represent travel speeds in fewer police-reported rear-end crashes than previously thought. Increasing speed differentials in existing FCP test scenarios to 45 mph would reflect the striking vehicle's travel speed in three quarters of rear-end crashes on roads with a speed limit of 45 mph or less.

Effects of precipitation type on crash relative risk estimates in Kansas

Crash relative risk estimates (CRREs) during rain, snow, sleet (i.e., ice pellets), and freezing rain are estimated using a matched-pair analysis in Kansas for the years 1995-2014. Variable-length event periods are defined using both crash reports and nearby Automated Surface Observing System (ASOS) and Automated Weather Observing System (AWOS) observations. Newly developed methods to extract precipitation-type beginning and ending times within ASOS/AWOS observations provide the most accurate account of precipitation type to compute CRREs. Crash relative risk is enhanced during any precipitation type, while a hierarchy of risk based on precipitation type is also evident. For property damage only crashes, CRREs during freezing rain are significantly higher than snow, which in turn are significantly higher than rain. CRREs during sleet, though statistically significantly higher than rain, are not statistically significantly different from snow or freezing rain. For casualty (injury and/or fatality) crashes, CRREs during snow and freezing rain are significantly higher than rain, but the increase in risk during sleet is not statistically significantly higher than rain. These results are consistent with expectations, but are quantified here for the first time. These hierarchies are insensitive to the use of all crashes versus only those with crash-reported precipitation or precipitation-induced roadway surface conditions to compute CRREs. The risk of a single-vehicle crash is higher than a multiple-vehicle crash during snow, sleet, and freezing rain, whereas the opposite was found for rain. This hierarchy is most prominent during weekdays before noon.

Analysis of Traffic Crashes Caused by Motorcyclists Running Red Lights in Guangdong Province of China

Motorcycles are among the primary means of transport in China, and the phenomenon of motorcyclists running red lights is becoming increasingly prevalent. Based on the traffic crash data for 2006–2010 in Guangdong Province, China, fixed- and random-parameter logit models are used to study the characteristics of motorcyclists, vehicles, roads, and environments involved in red light violations and injury severity resulting from motorcyclists’ running red lights in China. Certain factors that affect the probability of motorcyclists running red lights are identified. For instance, while the likelihood of violating red light signals during dark conditions is lower than during light conditions for both car drivers and pedestrians, motorcyclists have significantly increased probability of a red light violation during dark conditions. For the resulting severe casualties in red-light-running crashes, poor visibility is a common risk factor for motorcyclists and car drivers experiencing severe injury. Regarding the relationship between red light violations and the severity of injuries in crashes caused by motorcyclists running red lights, this study indicated that driving direction and time period have inconsistent effects on the probability of red light violations and the severity of injuries. On the one hand, the likelihood of red light violations when a motorcycle rider is turning left/right is higher than when going straight, but this turning factor has a nonsignificant impact on the severity of injuries; on the other hand, reversing, making a U-turn and changing lanes have nonsignificant effects on the probability of motorcyclists’ red light violations in contrast to going straight, but have a very significant impact on the severity of injuries. Moreover, the likelihood of red light violations during the early morning is higher than off-peak hours, but this time factor has a negative impact on the severity of injuries. Measures including road safety educational programs for targeted groups and focused enforcement of traffic policy and regulations are suggested to reduce the number of crashes and the severity of injuries resulting from motorcyclists running red lights.

Developing a National Integrated Road Traffic Injury Registry System: A Conceptual Model for a Multidisciplinary Setting

Introduction

Despite a high burden of traffic injuries, effective integrated or linked injury surveillance systems are rarely available in many low- and middle-income countries (LMICs). The aim of the current study was to define a conceptual model for developing a national integrated traffic injury registry in Iran.

Methods

A mult-method study financially and technically supported by the World Health Organization, Iranian Ministry of Health, Iranian Traffic Police, and the Iranian Legal Medicine Organization was conducted. A theoretical framework, forming the core conceptual components, was developed based on expert reviews. The preliminary conceptual model was developed by a panel of experts and tailored through a national workshop of 50 scientists, authorities and experts from nearly all sectors related to road safety promotion and injury management. It was then sent out to external reviewers in order to assess and improve the content validity of the model.

Results

The conceptual model was developed to have six components. These included 1) aims and core definitions; 2) content and core measurements; 3) data flow; 4) data collection routines; 5) organizational matrix; 6) implementation organization. The Haddon's matrix was adapted to be used as the theoretical framework in defining the content and data flow components of IRTIR. Five subcomponents were defined in the content and core measurements component with each having several subcategories. Each subcomponent/subcategory was finally divided into several item groups to guide defining the final data measurement variables. The data flow component was defined with six data sequence stations. Through the organizational matrix component, five major organizations relevant to road traffic safety were defined as core data production contributors. Some organizations also owned several sub-organizations which contributed in this regard.

Conclusion

It is concluded that the IRTIR conceptual model includes the required six components for developing a national integrated registry for Iran. Its main component called, content and core measurements, leads the researchers in developing final data collection tools in developing the national registry of road traffic injuries in Iran.

Fifty Years of Accident Analysis & Prevention: A Bibliometric and Scientometric Overview

Accident Analysis & Prevention (AA&P) is a leading academic journal established in 1969 that serves as an important scientific communication platform for road safety studies. To celebrate its 50^th anniversary of publishing outstanding and insightful studies, a multi-dimensional statistical and visualized analysis of the AA&P publications between 1969 and 2018 was performed using the Web of Science (WoS) Core Collection database, bibliometrics and mapping-knowledge-domain (MKD) analytical methods, and scientometric tools. It was shown that the annual number of AA&P's publications has grown exponentially and that over the course of its development, AA&P has been a leader in the field of road safety, both in terms of innovation and dissemination. By determining its key source countries and organizations, core authors, highly co-cited published documents, and high burst-strength publications, we showed that AA&P's areas of focus include the "effects of hazard and risk perception on driving behavior", "crash frequency modeling analysis", "intentional driving violations and aberrant driving behavior", "epidemiology, assessment and prevention of road traffic injuries", and "crash-injury severity modeling analysis". Furthermore, the key burst papers that have played an important role in advancing research and guiding AA&P in new directions - particularly those in the fields of crash frequency and crash-injury severity modeling analyses were identified. Finally, a modified Haddon matrix in the era of intelligent, connected and autonomous transportation systems is proposed to provide new insights into the emerging generation of road safety studies.

Global road traffic injury statistics: Challenges, mechanisms and solutions

High-quality data are the foundation to monitor the progress and evaluate the effects of road traffic injury prevention measures. Unfortunately, official road traffic injury statistics delivered by governments worldwide, are often believed somewhat unreliable and invalid. We summarized the reported problems concerning the road traffic injury statistics through systematically searching and reviewing the literature. The problems include absence of regular data, under-reporting, low specificity, distorted cause spectrum of road traffic injury, inconsistency, inaccessibility, and delay of data release. We also explored the mechanisms behind the problematic data and proposed the solutions to the addressed challenges for road traffic statistics.

Road Safety in Low-Income Countries: State of Knowledge and Future Directions

Road safety in low-income countries (LICs) remains a major concern. Given the expected increase in traffic exposure due to the relatively rapid motorisation of transport in LICs, it is imperative to better understand the underlying mechanisms of road safety. This in turn will allow for planning cost-effective road safety improvement programs in a timely manner. With the general aim of improving road safety in LICs, this paper discusses the state of knowledge and proposes a number of future research directions developed from literature reviews and expert elicitation. Our study takes a holistic approach based on the Safe Systems framework and the framework for the UN Decade of Action for Road Safety. We focused mostly on examining the problem from traffic engineering and safety policy standpoints, but also touched upon other sectors, including public health and social sciences. We identified ten focus areas relating to (i) under-reporting; (ii) global best practices; (iii) vulnerable groups; (iv) disabilities; (v) road crash costing; (vi) vehicle safety; (vii) proactive approaches; (viii) data challenges; (ix) social/behavioural aspects; and (x) capacity building. Based on our findings, future research ought to focus on improvement of data systems, understanding the impact of and addressing non-fatal injuries, improving estimates on the economic burden, implementation research to scale up programs and transfer learnings, as well as capacity development. Our recommendations, which relate to both empirical and methodological frontiers, would lead to noteworthy improvements in the way road safety data collection and research is conducted in the context of LICs.

Investigating injury severities of motorcycle riders: A two-step method integrating latent class cluster analysis and random parameters logit model

Due to the wide existence of heterogeneous nature in traffic safety data, traditional methods used to investigate motorcyclist rider injury severity always lead to masking of some underlying relationships which may be critical for the formulation of efficient safety countermeasures. Instead of applying one single model to the whole dataset or focusing on pre-defined crash types as done in previous studies, the present study proposes a two-step method integrating latent class cluster analysis and random parameters logit model to explore contributing factors influencing the injury levels of motorcyclists. A latent class cluster approach is first used to segment the motorcycle crashes into relatively homogeneous clusters. A mixed logit model is then elaborately developed for each cluster to identify its unique influential factors. The analysis was based on the police-reported crash dataset (2015-2017) of Hunan province, China. The goodness-of-fit indicators and the Receiver Operating Characteristic curves show that the proposed method is more accurate when modeling the riders' injury severities. The heterogeneity found in each homogeneous subgroup supports the application of the random parameters logit model in the study. More importantly, the results demonstrate that segmenting motorcycle crashes into relatively homogeneous clusters as a preliminary step helps to uncover some important influencing factors hidden in the whole-data model. The proposed method is proved to have great potential for accounting for the source of heterogeneity. The injury risk factors identified in specific cases provide more reliable information for traffic engineers and policymakers to improve motorcycle traffic safety.

A Surrogate Video-Based Safety Methodology for Diagnosis and Evaluation of Low-Cost Pedestrian-Safety Countermeasures: The Case of Cochabamba, Bolivia

Due to a lack of reliable data collection systems, traffic fatalities and injuries are often under-reported in developing countries. Recent developments in surrogate road safety methods and video analytics tools offer an alternative approach that can be both lower cost and more time efficient when crash data is incomplete or missing. However, very few studies investigating pedestrian road safety in developing countries using these approaches exist. This research uses an automated video analytics tool to develop and analyze surrogate traffic safety measures and to evaluate the effectiveness of temporary low-cost countermeasures at selected pedestrian crossings at risky intersections in the city of Cochabamba, Bolivia. Specialized computer vision software is used to process hundreds of hours of video data and generate data on road users’ speed and trajectories. We find that motorcycles, turning movements, and roundabouts, are among the key factors related to pedestrian crash risk, and that the implemented treatments were effective at four-legged intersections but not at traditional-design roundabouts. This study demonstrates the applicability of the surrogate methodology based on automated video analytics in the Latin American context, where traditional methods are challenging to implement. The methodology could serve as a tool to rapidly evaluate temporary treatments before they are permanently implemented and replicated.