The Effects of Speed and Flow Characteristics on Crash Rates for Wasit Multi-lane Highways in Iraq

This study describes traffic crash rates in selected multilane rural highways in Wasit governorate in Iraq. The main objective of this research is to investigate relationships between total, fatal crash rates and their kinds and factors such as hourly traffic flow and average spot speed. The study is based on data collected from two sources: police stations and traffic surveys. Three highways are selected to cover the locations of the accidents. The selection includes Kut – Suwera with five segments, Kut – ShekhSaad with three segments, and Kut – Hay with two segments multilane divided highways. Multiple linear regression analysis is applied to the data by using SPSS software to attain the relationships between the dependent variables and the independent variables in order to identify elements that are strongly correlated with crashes rates and severity. Seven regression models are developed which verify weak and strong statistical relationships between crashes types and average spot speed with hourly traffic flow respectively. As the hourly traffic flow of automobile grows, the need for safe traffic facilities also grown.

Exploring the Relationship of Rush Hour Period and Fatal and Non-Fatal Crash Injuries In The U.S.: A Systematic Review and Meta-Analysis

Road crashes are preventable causes of morbidity and mortality. In the U.S., substantial crashes occur during the rush hour period. The rush hour represents the period of the day during which the density of humans and vehicles in the road environment is highest. In the U.S., the rush hour period is bi-modal, occurring in the morning and the afternoon, at times that vary by state and urban-rural status. This systematic review and meta-analysis aimed to evaluate the association between the rush hour period and fatal and non-fatal crash injuries. Selected articles were limited to peer-reviewed full-text articles that measured crash injury as an outcome and rush hour as either a predictor, covariate, stratification, or a control variable. A total of 13 articles were identified for systematic review and seven articles were included in the meta-analysis. Across the selected studies, the rush-hour period signified the period of "peak traffic flow." During the rush hour period, aggressive driving behavior, truck driving, bicycle riding, and precipitation were associated with increased crash events or crash injuries. Across the seven studies included in the meta-analysis, the effective sample size was 220,471. The rush-hour period was associated with a 41% increased risk of fatal crash injury (Pooled RR: 1.41; 95% CI: 1.35 - 1.48). The morning and afternoon rush hour periods were associated with 40% (Pooled RR: 1.40; 95% CI: 1.13-1.67) and 27% (Pooled RR:1.27; 95% CI: 1.10-1.44) increased crash injury risk, respectively. The rush hour period, though less commonly studied as a predictor of fatal and non-fatal crash injuries, represents an important domain in need of crash injury prevention attention. The knowledge of the pattern of crash injuries, as it varies across countries, states, regions, and county can inform policy and intervention, in the presence of competing public health needs.

Study on Motorcycle Crash Cost in Bandung City

Crash cost is an important component for conducting economic analysis in selecting countermeasures for crash locations. It is used to convert the benefit of crash or fatality reduction into monetary terms. Many research on crash cost have been carried out in Indonesia. Most of the research utilized gross output/human capital approach. However, this approach has been widely criticized for not being able to describe the quality of life of crash casualties and the costs of pain, grief and suffering (i.e. human cost). The concept of Value of Statistical Life (VoSL) has been introduced by InDeV (2016) to calculate the human cost, which is assessed by using willingness to pay approach. To obtain a more reliable estimation of crash cost for Indonesia, it is necessary to conduct a study on crash cost involving motorcycles which incorporates the estimation of human cost. The VoSL is obtained by interviewing motorcycle users for willingness to pay with safety equipment. Based on the analysis, it is obtained that the value of statistical life (VoSL) for fatality in a road crash was estimated to be Rp.2.3 billion. The unit cost of fatal injury is Rp.3.08 billion, serious injury is Rp.333 million and slight injury is Rp.24.9 million. The unit cost of fatal crash on arterial roads is Rp.3.23 billion, serious crash is Rp.451 million and the slight crash is Rp.114 million. The unit cost of fatal crash on collector roads is Rp.3.16 billion, serious crashes is Rp.381 million and minor crash is Rp.69.4 million. The unit cost of fatal crash on local roads is Rp.3.09 billion, serious crash is Rp.338 million, and minor crash is Rp.29.8 million. Keywords: Motorcycle crash cost, gross output approach, human capital approach, willingness to pay approach

Understanding Fatal Crash Reporting Patterns in Bangladeshi Online Media using Text Mining

Traffic crashes are a major public health concern. In 2016, traffic crashes resulted in over 1.35 million deaths worldwide. In Bangladesh alone, the number of reported traffic fatalities was 2,376 in 2016. However, the World Health Organization estimated that the true number of traffic fatalities in Bangladesh ranges between 20,730 and 29,177. Editorial traffic crash reports in Bangladesh, and the number of crashes that are reported, vary widely among different media outlets. This study employed a Google News Alert to collect fatal crash reports from online English daily newspapers. The current study compiled a database of 419 fatal crash-related reports over a six-month period (November 2018–April 2019). The reports contain a total of 81,019 words. The results of this study reveal that online news coverage of traffic fatalities tends to vary from news agency to news agency. Furthermore, these reports do not usually cover key contributing factors of crash occurrences; the geometric features of crash occurrence sites are rarely reported. The findings demonstrate the importance of deciphering media coverage to develop potential safety risk measures in Bangladesh. The current findings provide strong support for the need for guidelines to help media outlets adequately document fatal crash reports.

Analyzing influencing factors of crash injury severity incorporating FARS data

The purpose of this study is to deeply analyze the influencing factors of drivers’ traffic accident casualties and reduce the occurrence of casualties. From the FARS database of the National Highway Safety Administration (NHTSA), 93248 traffic accident data were extracted as analysis samples. On this basis, the Bayesian network and multinomial logit model are established. The constructed model was tested from the perspective of model prediction accuracy and variables importance. Bayesian networks are used to analyze the interrelationships among influencing factors, and multinomial logit models are used to compare and evaluate the impact of different variables on the injury severity under different circumstances. The results show that: the prediction accuracy of the Bayesian network model and multinomial logit model is 64.57% and 65.97%, respectively. The Bayesian network reference analyses indicate that injury severity is affected by the crash factors, and there are various interactions between the various factors. The multinomial logit model analyses indicate that the factors including drivers’ age, female driver, rural roads, drunk driving, drug driving, crash time, side collision accident, etc. could significantly increase injury severity. Airbags are more effective in reducing fatal crash than injury crash. The probability of accidents caused by drug driving drivers is greater than drunk driving, drunk driving drivers are 1.79 times and 2.34 times more likely to suffer an injury severity and fatal injury severity in crashes as compared to a no injury severity, respectively, and drug driving is 1.93 times and 2.6 times, respectively. Seat belts may avoid 92.2% of fatalities. Roadside guardrail reduces the incidence of fatal crash better than injury crash. Fatal injuries severity and injury severity are 1.124 times and 1.141 times more likely to occur during the 0 : 00 to 6 : 00 as compared to no injuries, respectively, etc.

Topic Models from Crash Narrative Reports of Motorcycle Crash Causation Study

The Motorcycle Crash Causation Study (MCCS) is a matched case-control study that contains a very wide list of crash contributing factors associated with motorcycle crash occurrences. It contains information such as motorcycle information, rider information, and associated trip information. This study also provides crash narrative information that presents an in-depth narrative discussion of the crash causation. Because of the plethora of information, it is critical to investigate MCCS-related data. Some studies examined the structured information in MCCS datasets. There is no in-depth study that has examined the unstructured textual contents in the MCCS data. This study aims to mitigate this research gap by applying different natural language processing tools (e.g., text mining, topic modeling). Fatal and non-fatal crash narratives are clustered separately to gain insights pertaining to the injury level. The findings of this study will contribute to the ongoing studies on MCCS to better understand the crash causation mechanism associated with motorcycle crashes.