Which set of factors contribute to increase the likelihood of pedestrian fatality in road crashes?

Pedestrian crash causation analysis and active safety system calibration

Over 20 % of global crash fatalities involve pedestrians, but pedestrian crash causation and pedestrian protection systems have not been thoroughly developed or reliably tested. To understand the causation characteristics of pedestrian crashes, 398 pedestrian crashes were extracted from the China in-depth accident study (CIDAS), and most of these crashes were aggregated into five scenarios. The two scenarios with the highest proportion of crashes were analyzed by the driving reliability and error analysis method (DREAM) to identify high-risk causation patterns. From these patterns, three main contributing factors were identified: 1) extremely environmental light disturbance; 2) distracted driving caused by drivers' own thoughts; 3) drivers violating pedestrian yield law. Based on these patterns and factors, a pedestrian protection system was designed. It consists of a forward vision sensor and radar to sense the environment and the three-stage autonomous emergency braking (AEB) algorithm to automatically avoid pedestrian collisions. Crash scenarios from CIDAS data were recreated in MATLAB Simulink to test the pedestrian protection system proposed in this study. This system was found to reduce pedestrian crashes by more than 90 %. The optimal parameters for three AEB stages were obtained, with decelerations of 0.2 g, 0.3 g, and 0.6 g. This study designed an active safety system based on causation analysis of the vehicle-pedestrian crashes and calibrated the AEB algorithm of it, thus providing reference and insight for further development of pedestrian protection systems.

A matched case-control approach to identify the risk factors of fatal pedestrian crashes on a six-lane rural highway in India

Globally, the increase in pedestrian fatalities due to road traffic crashes (RTCs) on transport networks has been a major concern. In low- and middle-income countries (LMICs), pedestrians face a high risk due to RTCs on the rural highway network. The safety evaluation methods, such as observational before-after, empirical Bayes, full Bayes, and cross-sectional methods have been used to identify risk factors of RTCs. However, these methods are data-intensive and have associated limitations. Thus, this study employed a matched case-control method to identify the risk factors of fatal pedestrian crashes. This study utilized crash, traffic volume, speed, geometric, and roadside environment data of a 175 km six-lane rural highway in India. The identified major risk factors, such as clear zone width, the presence of habitation, service roads, and horizontal curve sections, increase the likelihood of a fatal pedestrian crash. This study provides specific insights for modifying the speed limit of highway sections passing through habitation. On such highway sections, designers should shift focus to pedestrian safety. It also suggests that the service road design needs to be reconsidered from a pedestrian safety viewpoint. The proposed method can be used in any other setting having similar traffic and socio-economic conditions.

Stability of factors influencing walking-along-the-road pedestrian injury severity outcomes under different lighting conditions: A random parameters logit approach with heterogeneity in means and out-of-sample predictions

Pedestrians walking along the road's edge are more exposed and vulnerable than those on designated crosswalks. Often, they remain oblivious to the imminent perils of potential collisions with vehicles, making crashes involving these pedestrians relatively unique compared to others. While previous research has recognized that the surrounding lighting conditions influence traffic crashes, the effect of different lighting conditions on walking-along-the-road pedestrian injury severity outcomes remains unexplored. This study examines the variations in the impact of risk factors on walking-along-the-road pedestrian-involved crash injury severity across various lighting conditions. Preliminary stability tests on the walking-along-the-road pedestrian-involved crash data obtained from Ghana revealed that the effect of most risk factors on injury severity outcomes is likely to differ under each lighting condition, warranting the estimation of separate models for each lighting condition. Thus, the data were grouped based on the lighting conditions, and different models were estimated employing the random parameter logit model with heterogeneity in the means approach to capture different levels of unobserved heterogeneity in the crash data. From the results, heavy vehicles, shoulder presence, and aged drivers were found to cause fatal pedestrian walking-along-the-road severity outcomes during daylight conditions, indicators for male pedestrians and speeding were identified to have stronger associations with fatalities on roads with no light at night, and crashes occurring on Tuesdays and Wednesdays were likely to be severe on lit roads at night. From the marginal effect estimates, although some explanatory variables showed consistent effects across various lighting conditions in pedestrian walking-along-the-road crashes, such as pedestrians aged < 25 years and between 25 and 44 years exhibited significant variations in their impact across different lighting conditions, supporting the finding that the effect of risk factors are unstable. Further, the out-of-sample simulations underscored the shifts in factor effects between different lighting conditions, highlighting that enhancing visibility could play a pivotal role in significantly reducing fatalities associated with pedestrians walking along the road. Targeted engineering, education, and enforcement countermeasures are proposed from the interesting insights drawn to improve pedestrian safety locally and internationally.

Factors explaining pedestrian-involved fatality crashes on National Highways in India

Pedestrians continue to face high risk of getting involved in fatal and serious injury crashes all over the world. In many high-income countries, pedestrian involvement in fatal crashes occur mostly in urban areas. However, in many low- and middle-income countries in Asia and Africa, pedestrian involvement in fatal crashes occur on intercity highways too. This research analyses fatal pedestrian crash characteristics, and identifies probable contributory factors to pedestrian involvement in fatal crashes using logistic regression for two-, four-, and six-lane National Highways. The fatal pedestrian crash density is found to be the highest at 1.37 crashes/km/year on six-lane divided NH-1. The binary logistic regression estimation results for pedestrian involvement in the fatal crash model revealed that the predictors: "number of lanes" and "time of crash" are found to be significant at 95% level. The model results for the variable "number of lanes" highlights the need to study pedestrian crossing behaviour on highways in detail. The design standards for pedestrian crossing facilities in urban areas may not be suitable for National Highways in particular multi-lane highways. In-depth research is required to understand the suitability of various traffic calming measures and other possible interventions which can ensure pedestrian safety on highways.

Factors Influencing Fatalities or Severe Injuries to Pedestrians Lying on the Road in Japan: Nationwide Police Database Study

To help reduce the number of pedestrians lying on the road suffering fatal or severe injuries as a result of vehicle collisions, we investigated the influencing factors. We conducted an analysis of the records of the Institute for Traffic Accident Research and Data Analysis Japan between 2012 and 2018; we found that 2452 pedestrians lying on the road were involved in collisions (797 fatalities, 784 severely injured, 871 mildly injured). Multivariate logistic regression analysis identified the following as major factors that positively influenced the fatalities: head or neck injuries (odds ratio [OR], 90.221); trunk injuries (OR, 71.040); trucks as offending vehicle (OR, 2.741); collision velocity of 10–20 km/h (OR, 31.794), 20–30 km/h (OR, 2.982), 30–40 km/h (OR, 8.394), 40–50 km/h (OR, 16.831), and >50 km/h (OR, 18.639); and hit-and-run cases (OR, 1.967). The following had a positive influence on severe injuries: trunk injuries (OR, 4.060); collision velocity of 10–20 km/h (OR, 2.540), 20–30 km/h (OR, 3.700), 30–40 km/h (OR, 5.297), 40–50 km/h (OR, 5.719), and ≥50 km/h (OR, 5.244); and hit-and-run cases (OR, 2.628). Decreasing the collision velocity, avoiding collisions to the head and neck or trunk, and preventing hit-and-run cases would be effective in reducing fatal or severe injuries to pedestrians lying on the road.

Modelling bus-pedestrian crash severity in the state of Victoria, Australia

Understanding the relationship between bus-pedestrian crash severity and factors contributing to such crashes is important. However, there exists a dearth of research on the factors affecting bus-pedestrian crash severity. This study aims to fulfil this gap by investigating the factors affecting the severity of pedestrian injuries. A data set of bus-pedestrian crashes in the State of Victoria, Australia was analysed over the period of 2006 - 2019. Through the results of association rule discovery method, the factors that increase the risk of pedestrian fatality are darkness, pedestrian walking on carriageway with traffic, intersections, high speed zone, old pedestrian, young bus driver and weekend holidays. Furthermore, co-occurrence of factors that increase the risk of a pedestrian fatality were extracted. To reduce the injuries of bus-pedestrian crashes, we recommend improving the light conditions, reducing the jaywalking behaviour of pedestrians, implementing speed bumps in high speed zones and installing pedestrian detection systems on buses.13 years of bus-pedestrian crashes in Victoria, Australia was analyzed.Association rules discovery was used for modeling pedestrian fatality.Darkness, pedestrian movement, zone speed and age effect the rate of fatality.Pattern of pedestrian fatality in collision with bus was extracted.

A state-of-the-art review of factors that predict mortality among traumatic injury patients following a road traffic crash

BACKGROUND

Traffic related injuries are a major public health problem worldwide with millions of people dying every year. The objective of this state-of-the-art review was to identify the factors reported in the literature as being associated with mortality for trauma patients following road traffic crashes.

METHOD

A systematic search was undertaken of PubMed/MEDLINE, EMBASE, CINAHL, Web of Science, and Cochrane Library databases to identify articles published in the past two decades (2000-2020). Of 8257 records, 4507 remained for title, abstract and full text screening after duplicates were removed. The level of evidence of selected studies was assessed using The National Health and Medical Research Council (NHMRC) guideline.

RESULTS

This review included eighty primary research studies examining mortality risk factors following a road traffic crash. The study identified factors in five categories; (i) demographic factors; (ii) behavioural factors; (iii) crash characteristics; (iv) environmental and timing factors; (v) injury severity and pre-injury/condition. The primary studies are summarised in a matrix. Included studies included level II to level IV levels of evidence based on the NHMRC criteria.

CONCLUSION

This study shows that there are a large number of factors associated with increased risk of mortality following diverse types of traffic crashes. Understanding these wide-ranging factors can strengthen injury and mortality prevention by guiding decision makers about where to focus strategy implementation.

Data mining approach to model bus crash severity in Australia

INTRODUCTION

Buses are different vehicles in terms of dimensions, maneuverability, and driver's vision. Although bus traveling is a safe mode to travel, the number of annual bus crashes cannot be neglected. Moreover, limited studies have been conducted on the bus involved in fatal crashes. Therefore, identification of the contributing factors in the bus involved fatal crashes can reduce the risk of fatality.

METHOD

Data set of bus involved crashes in the State of Victoria, Australia was analyzed over the period of 2006-2019. Clustering of crash data was accomplished by dividing them into homogeneous categories, and by implementing association rules discovery on the clusters, the factors affecting fatality in bus involved crashes were extracted.

RESULTS

Clustering results show bus crashes with all vehicles except motor vehicles and weekend crashes have a high rate of fatality. According to the association rule discovery findings, the factors that increase the risk of bus crashes with non-motor vehicles are: old bus driver, collision with pedestrians at signalized intersections, and the presence of vulnerable road users. Likewise, factors that increase the risk of fatality in bus involved crashes on weekends are: darkness of roads in high-speed zones, pedestrian presence at highways, bus crashes with passenger car by a female bus driver, and the occurrence of multi-vehicle crashes in high-speed zones. Practical Applications: The study provides a sequential pattern of factors, named rules that lead to fatality in bus involved crashes. By eliminating or improving one or all of the factors involved in rules, fatal bus crashes may be prevented. The recommendations to reduce fatality in bus crashes are: observing safe distances with the buses, using road safety campaigns to reduce pedestrians' distracted behavior, improving the lighting conditions, implementing speed bumps and rumble strips in high-speed zones, installing pedestrian detection systems on buses and setting special bus lanes in crowded areas.

A new approach in analyzing the accident severity of pedestrian crashes using structural equation modeling

Background:

According to official statistics in Iran, there were 17000 fatalities in road traffic crashes in 2018 that 25% of all crash fatalities belong to pedestrians. In most of the researches related to pedestrians’ safety, one aspect of the traffic crash (e.g. the injury or crash severity) is almost considered for the investigation. In order to perform a complete study of the crash, accident size can be utilized which involves different aspects of the crash. Accident size is described in terms of the number of fatalities and injured individuals and the number of dam-aged and involved vehicles in a crash.

Methods:

According to the fact that accident size has multiple indicators and it is not measured directly, traditional methodologies cannot be applied. So, in the present study the effective factors on the accident size of pedestrian crashes are investigated through structural equation modeling. For the purpose of this study, 3718 pedestrian-involved crash data occurred in Isfahan province is used for the modeling. The independent variables are weather conditions, road surface conditions, time, horizontal and vertical alignments, road type and location, driver’s gender and age, vehicle type, pedestrian’s age, gender and clothing color.

Results:

The results indicated that highways, the pedestrians’ invisibility, female and old-aged pedestrians, heavy vehicles, old-aged and female drivers are related to the increase of the accident size in pedestrian crashes. These results denote that the mentioned variables are associated with the higher number of injuries, fatalities, the higher number of involved and damaged vehicles in a crash.

Conclusions:

Present study shows the importance of considering safety improvement measures in highways, educating the people in the society about the traffic safety, the separation of pedestrian and motor vehicle traffic flow and considering the old people in policies and programs for mitigating the accident size.

An investigation of the relationship between demographic variables, driving behaviour and crash involvement risk of bus drivers: a case study from Iran

The aim of this study was to model bus drivers' crash involvement risk as a function of their demographic factors and driving behaviour. 177 bus drivers completed the bus driver risk index questionnaire. 19.2% of respondents had been involved in at least one at-fault crash during the past 3 years. In addition, age group and having a second job were found to affect the crash involvement rate. Smoker drivers reported significantly lower scores in patient driving and anxious driving factors. Results also indicated that vehicle ownership could improve hazard monitoring and thrill-seeking behaviours of bus drivers. Logistic regression suggested that drivers ≥55 years old are 5.4 times more likely to be involved in crashes, compared with drivers <35 years old. Model results also implied that the odds of crash involvement would decrease by 39% for each one-unit improvement in the scores of hazard monitoring behaviour. Implications are further discussed.