International Journal of Injury Control and Safety Promotion最新文献

A well-developed road network plays a crucial role in fostering social and economic progress within a region. However, road crashes resulting in massive injuries and deaths profoundly affect socioeconomic development. There is a need therefore to identify working approaches used in road safety strategic management which provide evidence and a foundation to achieve safer road transport. This may be achieved through a systematic literature review considering both state-of-the-art technologies and best practice. Such a review is presented in this paper. The review involved searching twenty-six bibliographic databases and twenty-four websites of road-related organizations. Following the EPPI-Reviewer methodology, the researchers identified 30 studies that demonstrated various methods employed in the strategy development process. The review highlighted the prevalence of information technology in crash data analysis, particularly concerning big data applications. Moreover, existing resource allocation methods primarily focus on local countermeasures prioritization and ranking based on benefit cost analysis. However, the review identified a gap in comprehensive crash database understanding, and only a few single-objective optimization methods have been developed for strategy development, while there is a need for data mining methods and multi-objective optimisation methods supported by expert knowledge.

As the popularity of electric bicycles (e-bikes) continues to surge, the number of accidents involving them has commensurately increased. A significant factor contributing to the high fatality rate in these accidents is the low usage of helmets among e-bike riders. Helmets have been proven to reduce the severity of injuries, yet their usage remains unexpectedly low. This issue is particularly pronounced among college students, the primary buyer group for e-bikes. Regrettably, there is a lack of research exploring their intentions to wear helmets. Understanding determinants of their intentions to wear helmets is crucial in promoting safe e-bike travel. Therefore, the present study aims to develop an integrated theoretical model that combines the Theory of Planned Behavior (TPB) and the Health Belief Model (HBM) to examine the factors influencing e-bike riders' helmet-wearing intentions among college students. Additionally, two variables-descriptive norms and law enforcement-are incorporated. The results indicate that the integrated model accounts for 76% of the variance in helmet-wearing intention, surpassing single-theory models. Specifically, the TPB accounts for 65%, while the HBM explains 53%. Notably, law enforcement emerges as the most influential factor, highlighting the crucial role of enforcing regulations and promoting awareness. Other significant factors include subjective and descriptive norms, attitudes, perceived benefits, perceived susceptibility, perceived barriers, and perceived severity. These findings provide valuable insights for policy development and targeted interventions aimed at improving helmet wear rates among e-bike riders, especially among the college student population.

This study aims to classify motorcycle (MC) following distance based on trajectory traffic data and identify the risks associated with MC following distances to prevent rear-end collisions. A total of 8,223 events of a MC following a vehicle were investigated in Pathum Thani, Thailand, and 41 cases of MC rear-end crashes were analyzed between 2017 and 2021. Time headway (TH), safe stopping distance (SSD) and time to collision (TTC) were applied to the proposed concept to determine safe following distance (SFD). Speed and following distance for actual rear-end crashes were applied to validate SFD. Results showed that the proposed SFD model identified the causes of MC rear-end collision events as mostly due to longitudinal critical area (38 cases, 92.68%), implying insufficient MC rider reaction and decision time for evasive action. The longitudinal warning area had relatively few chances for rear-end collisions to occur, with only 3 cases recorded. VDO clip extracts from MC rear-end crashes illustrated 11 cases (26.83%) of rider fatality. The study findings revealed that the SFD concept can help to prevent MC rear-end collision events by developing reminder systems when the rider reached the following distances of both warning and critical areas.

This study simultaneously modelled the injury severity of motorcycle riders and their pillion passengers and determine the associated risk factors. The analysis is based on motorcycle crashes data in Ashanti region of Ghana spanning from 2017 to 2019. The study implemented bivariate ordered probit model to identify the possible risk factors under the premise that the injury severity of pillion passenger is endogenously related to that of the rider in the event of crash. The model provides more efficient estimates by considered the common unobserved factors shared between rider and pillion passenger. The result shows a significant positive relationship between the two injury severities with a correlation coefficient of 0.63. Thus, the unobservable factors that increase the probability of the rider to sustain more severe injury in the event of crash also increase that of their corresponding pillion passenger. The rider and their pillion passenger injury severities have different propensity to some of the risk factors including passengers' gender, day of week, road width and light condition. In addition, the study found that time of day, weather condition, collision type, and number of vehicles involved in the crash jointly influence the injury severity of both rider and pillion passenger significantly.

Taxis play a crucial role in urban public transportation, but the traffic safety situation of taxi drivers is far from optimistic, especially considering the introduction of ride-hailing services into the taxi industry. This study conducted a comparative analysis of risk factors in crashes between traditional taxi drivers and ride-hailing taxi drivers in China, including their demographic characteristics, working conditions, and risky driving behaviors. The data was collected from 2,039 traditional taxi drivers and 2,182 ride-hailing taxi drivers via self-reported questionnaires. Four XGBoost models were established, taking into account different types of taxi drivers and crash types. All models showed acceptable performance, and SHAP explainer was used to analyze the model results. The results showed that for both taxi drivers, risk factors related to risky driving behaviors are more important in predicting property damage (PD) crashes, while risk factors related to working conditions are more important in predicting person injury (PI) crashes. However, the relative importance of each risk factor varied depending on the type of crashes and the type of taxi drivers involved. Furthermore, the results also validated certain interactions among the risk factors, indicating that the combination of certain factors generated a greater impact on crashes compared to individual factors alone. These findings can provide valuable insights for formulating appropriate measures to enhance road safety for taxi driver.

In-lane street hawking is the intermittent entry of signalized intersections by traders to sell groceries to drivers and passengers. Studies have shown that hawkers get exposed to traffic injuries but the lack of quantitative analysis of their lane entry and exit behaviors in signalized intersections makes it difficult to improve traffic safety. This study analyzes the significant predictors of in-lane street hawkers' (1) lane entry within 30 s after the red signal illumination, (2) lane exit within 30 s after the green signal illumination, and (3) probability of getting injuries during the green signal time. Drone-based trajectory data were collected from a selected signalized intersection in Accra, Ghana. A Weibull accelerated failure time duration model incorporating Gamma frailty was used to evaluate hawkers' behaviors. Overall, the majority of hawkers exhibited red-light running behaviors exposing them to traffic injuries. An increase in traffic speed, especially beyond 20 km/h, exposed hawkers to injury risks significantly. Notably, hawkers' lane entry decreased significantly as the traffic speed increased. Their lane exit duration was significantly predicted by the queue lengths and traffic volumes. Accordingly, safety practitioners can enhance traffic regulation and control methods in addition to pro-poor social interventions to demotivate hawking at signalized intersections.

Traffic violation is one of the leading causes of traffic crashes. In the context of global aging, it is important to study traffic violations by elderly drivers for improving traffic safety in preparation for a worldwide aging population. In this study, a hybrid approach of Latent Class Analysis (LCA) and XGBoost based SHAP is proposed to identify hidden clusters and to understand the key contributing factors on the severity of traffic violations by elderly drivers, based on the police-reported traffic violation dataset of Beijing (China). First, LCA is applied to segment the dataset into several latent homogeneous clusters, then XGBoost based SHAP is established on each cluster to identify feature contributions and the interaction effects of the key contributing factors on the severity of traffic violations by elderly drivers. Two comparison groups were set up to analyze factors, which are responsible for the different severities of traffic violations. The results show that elderly drivers can be classified into four groups by age, urban or not, license, and season; factors such as less annual number of traffic violations, national & provincial highway, night and winter are key contributing factors for higher severity of traffic violations, which are consistent with common cognition; key contributing factors for all clusters are similar but not identical, for example, more annual number of traffic violations contribute to more severe violation for all clusters except for Cluster 2; some factors which are not key contributing factors may affect the severity of traffic violations when they are combined with other factors, for example, the combination of lower annual number of traffic violations and county & township highway contributes to more severe violation for Cluster 1. These findings can help government to formulate targeted countermeasures to decrease the severity of traffic violations by specific elderly groups and improve road service for the driving population.

Road traffic crashes (RTCs) are significantly high in Nigeria with serious social and health consequences. While existing studies on RTCs have mainly focused on the effect of socio-economic, environmental, human and mechanical factors to address the high rates, the relationship between road transport fares and RTCs has been glossed over in literature. Thus, this study examines the influence of road transport fares and other covariates on RTCs. Data on RTCs and the predictors between 2017 and 2022 were obtained from the records of the National Bureau of Statistics and the Federal Road Safety Corps. Spatial statistical techniques were used for the data analysis. RTCs vary across the country, and Northern Nigeria is the hot spot. Results from the spatial analysis show that road transport fares, population density, and illiteracy rate are significant predictors of RTCs. The study recommends striking a balance between fare affordability, the quality of service provided, and the implementation of effective transportation strategies.

Pivotal crash factors are investigated, and crash-severity model for the safety assessment at roundabouts and its vicinity in non-lane based heterogenous traffic is developed. An ordered-probit model was developed using crash-data collected between 2015-2019 for 20 roundabouts in India. The analysis revealed critical influencing parameters for determining the severity-level of crash outcomes at roundabouts, namely, inscribed-circle diameter, height of central island, number of circulatory lanes, presence of splitter island and median, posted-speed limit, type of collision, type of violation behaviour, collision partner, the pattern of collision, presence of road lane-marking, presence of street-light and age of victims. To precisely quantify the impact of each significant factor, marginal effects analysis was also carried out. The results show that the probability of fatal-injuries increased by 14.28% due to angle-collision, 15% for hit-pedestrians, 20.6% due to the pattern of collision and 15.60% due to the collision-partner, Whereas the probability of occurrence of grievous injury was the highest for rear-end with 17%, followed by sideswipe collision with 16% respectively. This study's findings can aid in developing effective remedies to reduce the crash severity for roundabouts road-users and updating the roundabout design standards, considering the safety perceptive.

This paper investigates the factors influencing the severity of driver injuries in single-vehicle speeding-related crashes, by comparing different driver age groups. This study employed a random threshold random parameter hierarchical ordered probit model and analysed crash data from Thailand between 2012 and 2017. The findings showed that young drivers face a heightened fatality risk when speeding in passenger cars or pickup trucks, hinting at the role of inexperience and risk-taking behaviours. Old drivers exhibit an increased fatality risk when speeding, especially in rainy conditions, on flush median roads, and during evening peak hours, attributed to reduced reaction times and vulnerability to adverse weather. Both young and elderly drivers face escalated fatality risks when speeding on road segments lacking guardrails during adverse weather, with older drivers being particularly vulnerable in rainy conditions. All age groups show an elevated fatality risk when speeding on barrier median roads, underscoring the significant role of speeding, which increases crash impact and limits margins of error and manoeuvrability, thereby highlighting the need for safety measures focusing on driver behaviour. These findings underscore the critical imperative for interventions addressing not only driver conduct but also road infrastructure, collectively striving to curtail the severity of speeding-related crashes.