Factors influencing the cruisers in recommending onshore excursions in Gran Canaria.

This paper investigates which elements are the ones that most influence the cruise passenger to recommend the onshore excursions, offering relevant information that could help all stakeholders (cruise firms, local operators, politicians, etc.) to better design/adapt shore excursions to the expectations of the cruise passengers, increasing the positive impact of this activity in the destination. The latter is essential for the existence of a good harmony between the cruise ships visiting a destination and the population living there. To this aim, an ordered logit model was estimated using a sample of 1,059 questionnaires obtained during the period 2018-2020. The results have shown that cruise passengers are more likely to recommend the onshore excursions if their expectations regarding the excursion have been covered, they have perceived a good quality/price ratio and have had a satisfactory tour guide service. Moreover, the fact that the cruisers aged between 45 and 55 are less likely to recommend excursions could suggest that the design of existing excursions could be improved by being tailored by demographic characteristics (i.e., aged-oriented) or even by offering different alternatives for each group during the shore excursion when possible. The results could be useful to all stakeholders when designing excursions to increase the positive impact of this activity on the destination.

Beyond the surface: Exploring the temporally stable factors influencing injury severities in large-truck crashes using mixed logit models.

An analysis of crash data spanning four years (January 1, 2015, to December 31, 2018) from the State of Washington is conducted to investigate factors influencing injury severity outcomes in large truck-involved crashes. The study utilizes a mixed logit model that accounts for unobserved heterogeneity to capture the variation influenced by other variables. Transferability and temporal stability across the years are assessed using the likelihood ratio test. A wide range of attributes, including driver characteristics, vehicle features, crash-related attributes, roadway conditions, environmental factors, and temporal elements, are considered. Despite a significant temporal instability warranted by the likelihood ratio test across the years, twenty-one parameters consistently exhibit stable effects on injury severity over the years of which thirteen are new. The identified stable parameters included over speeding, following too closely, falling asleep, missing/ faulty airbags, head-on collisions, crashes involving two or more than three vehicles, rear-end collisions, lane width, low-light conditions, sag curves, New Jersey barriers, snowy weather, and morning hours. The temporally stable factors affecting injury severities in large truck crashes are crucial in developing the needed to address these crashes. The findings of this study offer valuable insights for researchers, stakeholders in the trucking industry, and policymakers, empowering them to develop targeted policies that not only improve traffic safety but also alleviate associated economic losses.

Unpacking the hazards: An analytic study of injury patterns and risk factors in urban instant delivery.

The rapid growth of urban instant delivery, facilitated by digital platforms and characterized by on-demand, short-term, task-based labor, has raised concerns about safety, particularly with the increasing frequency of instant delivery crashes (IDCs). This study addresses knowledge gaps in understanding injury patterns and risk factors associated with IDCs. Utilizing data extracted from judicial verdicts on IDC disputes in China, encompassing demographic, contextual, crash, and injury information, the research employs ordered logit regression to identify significant factors affecting injury patterns, the number of injuries per person (IPP), and injury severity. Overall, traffic injuries related to instant delivery services have gradually improved since 2020, as evidenced by the severity of individual accidents, the number of injuries, and the economic losses. Analysis of 648 injuries among 448 non-fatal victims reveals a prevalence of lower extremity injuries, followed by external, upper extremity, and head injuries. While the majority of victims suffered a single injury, approximately 22 % experienced major injuries. Female delivery riders exhibited higher injury ratios across various body regions. Rider risk behavior, type of delivery vehicles, and the mode of transport of non-delivery travelers emerged as significant influencers of injury patterns. Notably, functional and physical intersection areas exhibited the highest injury ratios among facility types. Contrary to conventional wisdom, older riders and travelers aged above 50 were associated with higher injury severity, challenging the perception of young age as the primary risk factor. The prominence of lower extremity injuries underscores the necessity for heightened protective measures for delivery riders. Major injuries among victims emphasize potential long-term consequences and associated costs. The significance of gender, age, and risk behavior as determining factors highlights the need for targeted safety interventions. These findings offer crucial insights for stakeholders, guiding the formulation of precise safety measures and informed policy initiatives within the dynamic landscape of instant delivery safety.

Factors associated with financial risk due to health spending in Argentina.

This article aims to assess the association between household demographic and socioeconomic characteristics and catastrophic health expenditure (CHE) in Argentina during 2017-2018. CHE was estimated as the proportion of household consumption capacity (using both income and total consumption in separate estimations) allocated for Out-of-Pocket Health Expenditure (OOP). For assessing the determinants, we estimated a generalized ordered logit model using different intensities of CHE (10%, 15%, 20% and 25%) as the ordinal dependent variable, and socioeconomic, demographic, and geographical variables as explanatory factors. We found that having members older than 65 years and with long-term difficulties increased the likelihood of incurring CHE. Additionally, having an economically inactive household head was identified as a factor that increases this probability. However, the research did not yield consistent results regarding the relationship between public and private health insurance and consumption capacity. Our results, along with the robustness checks, suggest that the magnitude of the coefficients for the household head characteristics could be exaggerated in studies that overlook the attributes of other household members. In addition, these results emphasize the significance of accounting for long-term difficulties and indicate that omitting this factor could overestimate the impact of members aged over 65.

Association between high-risk fertility behaviors and neonatal mortality in Ethiopia: A multilevel mixed-effects logit models from 2019 Ethiopian mini demographic and health survey.

Objectives: This study aimed to explore the association between high-risk fertility behaviors and neonatal mortality in Ethiopia. Study design: A community-based cross-sectional study was conducted using data from the 2019 Ethiopian Mini-Demographic and Health Survey. Methods: Mixed-effects logit regression models were fitted to 5527 children nested within 305 clusters. The definition of high-risk fertility behavior was adopted from the 2019 EMDHS. The fixed effects (the association between the outcome variable and the explanatory variables) were expressed as adjusted odds ratios (ORs) with 95 % confidence intervals and measures of variation explained by intra-class correlation coefficients, median odds ratio, and proportional change invariance. Results: The presence of births with any multiple high-risk fertility behaviors was associated with a 70 % higher risk of neonatal mortality (AOR = 1.7, (95 % CI: 1.2, 2.3) than those with no high-risk fertility behavior. From the combined risks of high-risk fertility behaviors, the combination of preceding birth interval <24 months and birth order four or higher had an 80 % increased risk of neonatal mortality (AOR = 1.8, (95 % CI, 1.2, 2.7) as compared to those who did not have either of the two. The 3-way risks (combination of preceding birth interval <24 months, birth order 4+, and mother's age at birth 34+) were associated with approximately four times increased odds of neonatal mortality (AOR (95 % CI:3.9 (2.1, 7.4)]. Conclusions: High-risk fertility behavior is a critical predictor of neonatal mortality in Ethiopia, with three-way high-risk fertility behaviors increasing the risk of neonatal mortality fourfold. In addition, antenatal follow-up was the only non-high fertility behavioral factor significantly associated with the risk of neonatal mortality in Ethiopia.

Developing Problematic Performance Value Scores: Binding Routine Activity Performance, Environmental Barriers, and Health Conditions.

BACKGROUND: Community design features, such as sidewalks and street crossings, present significant challenges for individuals with disabilities, hindering their physical performance and social integration. However, limited research has been conducted on the application of Universal Design (UD) to address these challenges, particularly concerning specific demographic groups and population cohorts. Understanding the influence of environmental features on physical performance is crucial for developing inclusive solutions like UD, which can enhance usability and social integration across diverse populations. OBJECTIVE: This study aims to bridge this gap by investigating the complex relationships between environmental barriers, health conditions, and routine activity performance. An index was developed to evaluate users' UD performance based on functional capacity, providing scientifically rigorous and objectively measured evidence of UD effectiveness in creating inclusive built environments. METHOD: Using data from the Problematic Activities Survey (PAS) conducted in the U.S., Canada, and Australia and targeting individuals with and without functional limitations, multinomial logit models were employed to estimate the probabilities of encountering performance problems. This analysis led to the development of the Problematic Performance Value (PPV) score. RESULTS: The results demonstrated significant disparities in PPVs across various health conditions, particularly concerning curb ramps. Individuals facing mobility issues in their legs/feet, arms/hands, or back/neck encounter more pronounced challenges, especially when curb ramps lack proper design elements. Similarly, individuals with vision impairments face heightened difficulties with traffic signals, particularly due to issues with audible signal systems. These findings underscore the importance of addressing micro-level environmental challenges to accommodate individuals with varying functional capacities effectively. CONCLUSIONS: By providing insights into the most problematic daily activities encountered by diverse populations, the PPV score serves as a valuable indicator for guiding environmental design improvements and promoting equitable space usage. This can be used to guide improved UD solutions and decide areas of concentration by providing generalized information on specific environmental features that contribute to user performance.

A study on diversion behavior in weaving segments: Individualized traffic conflict prediction and causal mechanism analysis.

Lane change behavior disrupts traffic flow and increases the potential for traffic conflicts, especially on expressway weaving segments. Focusing on the diversion process, this study incorporating individual driving patterns into conflict prediction and causation analysis can help develop individualized intervention measures to avoid risky diversion behaviors. First, to minimize measurement errors, this study introduces a lane line reconstruction method. Second, several unsupervised clustering methods, including k-means, agglomerative clustering, gaussian mixture, and spectral clustering, are applied to explore diversion patterns. Moreover, machine learning methods, including Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Attention-based LSTM, eXtreme Gradient Boosting (XGB), Support Vector Machine (SVM), and Multilayer Perceptron (MLP), are employed for real-time traffic conflict prediction. Finally, mixed logit models are developed using pre-conflict condition data to investigate the causal mechanisms of traffic conflicts. The results indicate that the K-means algorithm with four clusters exhibits the highest Calinski-Harabasz and Silhouette scores and the lowest Davies-Bouldin scores. With superior classification accuracy and generalization ability, the LSTM is used to develop the personalized traffic conflict prediction model. Sensitivity analysis indicates that incorporating the diversion patterns into the LSTM model results in an improvement of 3.64% in Accuracy, 7.15% in Precision, and 1.34% in Recall. Results from the four mixed logit models indicate significant differences in factors contributing to traffic conflicts within each diversion pattern. For instance, increasing the speed difference between the target vehicle and the right preceding vehicle benefits traffic conflict during acceleration diversions but decreases the likelihood of traffic conflicts during deceleration diversions. These results can help traffic engineers propose individualized solutions to reduce unsafe diversion behavior.

Optimizing crash risk models for freeway segments: A focus on the heterogeneous effects of road geometric design features, traffic operation status, and crash units.

Traffic crash risk prediction models have been developed to investigate crash occurrence mechanisms and analyze the effects of various traffic operation factors, data on which are collected by densely deployed detectors, on crash risk. However, in China, freeway detectors are widely spaced (the spacing is usually more than 2 km) and the road geometries vary frequently, especially in mountainous areas. Moreover, many freeway sections are located in urban areas and serve commuting functions. Due to the different mechanisms of crash occurrence on road segments with different geometric design features and traffic operation status, it is necessary to consider these heterogeneities in crash risk prediction. In addition to considering observable heterogeneous effects, it is equally important to consider the existence of unobserved heterogeneities among crash units. This study focuses on the effects of different types of heterogeneities on crash risk for segments of the Yongtaiwen Freeway in Zhejiang Province, China, using crash, traffic flow, and road geometric design data. Latent class analysis (LCA), latent profile analysis (LPA), and a combination of both methods are respectively used to classify road segments into subgroups based on road geometric design features, the traffic operation status, and a combination of both. The results reveal that the binary logit model considering the heterogeneous effects of the combination of road geometric design features and the traffic operation status achieves the best performance. Furthermore, binary conditional logit models and grouped random parameter logit models are developed to analyze the unobserved heterogeneity among crash units, and the results show that the latter has a better goodness of fit. Finally, a paradigm of the crash risk prediction for freeway segments with widely-spaced traffic detectors and frequently-changing geometric features is provided for traffic safety management departments.

Analysis of pedestrian accident severity by considering temporal instability and heterogeneity.

The aim of this study was to investigate the effects of temporal instability and possible heterogeneity on pedestrian accident severity, 48786 accident data from 2018 to 2021 in the UK STATS database were used as the study object, and accident severity was used as the dependent variable, and 49 accident characteristics were selected as independent variables from 6 characteristics of accident pedestrian, driver, vehicle, road, environment and time to construct the pedestrian accident mean heterogeneity random-parameter logit model and examined its temporal stability. The results of model estimation and likelihood ratio tests indicate that the variables affecting pedestrian injury severity are highly variable and not stable over the years. And further demonstrates the potential of models that address unobserved heterogeneity for significant relationships in pedestrian accident severity analyses.

Analyzing the factors influencing speeding behavior based on quasi-induced exposure and random parameter logit model with heterogeneity in means.

INTRODUCTION: Speeding behavior is a major threat to road traffic safety, which can increase crash risks and result in severe injury outcomes. Although several studies have been conducted to analyze speeding crashes and relevant influential factors, the heterogeneity of variables has not been fully explored. Based on the traffic crash data extracted from the Crash Report Sampling System, the study aims to identify the factors that influence speeding driving with the consideration of variable heterogeneity. METHOD: Quasi-induced exposure technique is adopted to identify the disparities in the propensities of speeding for various driving cohorts. The random parameter logit model with heterogeneity in means is employed to examine the factors impacting speeding behavior. RESULTS: Results indicate that: (a) driving cohorts such as young drivers, male drivers, passenger cars, and pickups appear to have higher propensities of engaging in speeding driving; (b) the propensity of speeding is higher when the driver is drinking, distracted, changing lanes, negotiating a curve, driving in lighted condition, and on curved roads; and (c) the random parameter logit model with heterogeneity in means has better performance as opposed to that without heterogeneity in means. CONCLUSIONS: Speeding behavior can be influenced by various factors in terms of driver-vehicle characteristics, physical condition, driving actions, and environmental conditions. PRACTICAL APPLICATIONS: The findings could serve to develop effective countermeasures to reduce speeding behavior and improve traffic safety.

How can open public data promote efficient and equitable green production? Evidence from eco-efficiency in China.

Nowadays, concurrent attention to economic development and ecological issues is becoming an important trend. In this paper, we measure the eco-efficiency of 285 Chinese cities from 2003 to 2019 using a non-radial directional distance function and the data envelopment analysis method, based on which we analyze the club convergence of cities' eco-efficiency using the logt test; we estimate the impact of open public data platforms on eco-efficiency and its convergence using a multi-period difference in difference model and panel-ordered logit model, respectively. We find that, first, open public data platforms improve cities' eco-efficiency by about 6.5%, and the impact mechanisms include scale efficiency, technical efficiency, and total factor productivity, or, at the micro level, increasing the economic agglomeration degree, boosting the amount of foreign investment used, and increasing green innovation level. Second, there are three convergence clubs of eco-efficiency in China's cities, whose average eco-efficiency trends are above, close to, and below average, respectively. Third, public data platforms significantly increase the probability of cities belonging to the convergence clubs of high and medium eco-efficiency (Clubs 1 and 2) and decrease the probability of belonging to the low one (Club 3). However, the mechanisms only include technical efficiency and total factor productivity, or the amount of foreign investment used and the green innovation level at the micro level.

Safety-in-Numbers for route choice of bicycle trips: A choice experiment approach for commuters.

Safety-in-Numbers (SiN) implies that the risk of collision per road user is less when there are more road users. Although the available literature has confirmed the existence of SiN as an objective measure of safety, the effect on perceived safety, especially in the context of bicycle riders, has received much less attention. This study investigates the SiN effect on the perceived safety of bicycle riders that influences route choice behavior. A stated preference survey was performed in the South Delhi district of Delhi. The effect of attributes like posted speed limit, the volume of motorized traffic, bicycle infrastructure, and bicycle traffic/ crowding on route choice behavior was investigated. A binary logit model was developed to quantify the effect of these attributes on route choice. The results indicate that, in general, riders prefer routes with more bicycle traffic, hence validating SiN. But the effect does not always hold. For some riders, in the presence of dedicated bicycle infrastructure, when the perceived safety is higher, the presence of more bicycle traffic acts as crowding and demotivates riders to choose that route. The study also reveals that riders prefer routes with a low volume of motorized traffic and dedicated bicycle infrastructure. The outcomes suggest that a policy that encourages infrastructural development to provide lateral separation will encourage more people, hence increasing bicycle mode share as well as the perceived safety of riders.

Analysis of injury severity of work zone crashes on rural and urban work zones: Accounting for out-of-sample prediction and temporal instability.

This research utilizes data collected in Florida to examine the differentials in injury severities among single-vehicle drivers involved in work zone-related incidents, specifically focusing on the distinctions between rural and urban areas. The study encompasses a four-year period (2016-2019) of crash dataset. A likelihood ratio test was performed to examine model estimate's temporal consistency in datasets from rural and urban areas across several time periods throughout the year. Separate statistical models were estimated for both rural and urban datasets to understand different driver injury severity outcomes (no injury, minor injury, and severe injury) using a mixed logit approach with possible heterogeneity in mean and variance of random parameters. Out-of-sample simulations were conducted to see the effect of different parameter changes on injury severity probabilities in rural and urban work zone crashes. Over multiple years, various years in both rural and urban models have generated statistically significant random factors that effectively capture the presence of heterogeneity in means, accounting for unobservable variations within the data. Clear evidence of factors such as speed limits, work zone type, and traffic volume affecting the work zone injury severities were found to vary significantly between rural and urban work zone areas. However, despite this difference, rural and urban work zones share common safety problems and countermeasures such as driver education, improved signage, and appropriate traffic controls; combining ITS technologies and enhanced law enforcement can help mitigate crash severity in urban and rural work zone areas.

Analysis of medical impoverishment and its influencing factors among China's rural near-poor, 2016-2020.

Objective: This study investigates the determinants of medical impoverishment among China's rural near-poor, aiming to enhance public health services and establish preventative and monitoring systems. Methods: Using China Family Panel Studies and World Bank methods, we categorized rural populations and calculated their 2020 Poverty Incidence (PI) and Poverty Gap (PG), with impoverishing health expenditures (IHE) as the primary indicator. We analyzed the data from 2016 to 2020 using a conditional fixed-effects multinomial logit model and 2020 logistic regression to identify factors influencing medical impoverishment risk. Results: (1) In 2020, the near-poor in China faced a PI of 16.65% post-health expenditures, 8.63 times greater than the non-poor's PI of 1.93%. The near-poor's Average Poverty Gap (APG) was CNY 1,920.67, notably surpassing the non-poor's figure of CNY 485.58. Health expenses disproportionately affected low-income groups, with the near-poor more prone to medical impoverishment. (2) Disparities in medical impoverishment between different economic household statuses were significant (P < 0.001), with the near-poor being particularly vulnerable. (3) For rural near-poor households in China, those with over six members faced a lower risk of medical impoverishment compared to those with three or fewer. Unmarried individuals had a 7.1% reduced risk of medical impoverishment relative to married/cohabiting counterparts. Unemployment was associated with a 9% increased risk. A better self-rated health status was linked to a lower probability of IHE, with the "very healthy" reporting a 25.8% lower risk than those "unhealthy." Chronic disease sufferers in the near-poor and non-poor categories were at an increased risk of 12 and 1.4%, respectively. Other surveyed factors, including migrant status, age, insurance type, gender, educational level, and recent smoking or drinking, were not statistically significant (P > 0.05). Conclusion: Rural near-poor in China are much more susceptible to medical impoverishment, influenced by specific socio-economic factors. The findings advocate for policy enhancements and health system reforms to mitigate health poverty. Further research should extend to urban areas for comprehensive health poverty strategy development.

Flock level socio-economic and other associated risk factors for Peste des petits ruminants (PPR) exposure in sheep and goats in Madhya Pradesh state, India.

To effectively control and eradicate PPR, the comprehensive understanding of risk factors associated with PPR exposure is vital. Hence, this study investigated socioeconomic and other associated risk determinants for PPR exposure at flock level in sheep and goats in a non-vaccination programme implemented Madhya Pradesh state India. A total of 410 sheep and goat flocks, comprised mostly of goats but also some mixed flocks, were surveyed during 2016 using a multistage random sampling procedure. Further, 230 blood samples were also collected from the farmers-reported PPR affected flocks and sera were tested using c-ELISA to confirm PPR exposure. The primary data on socioeconomic factors, farm management factors, health status, vaccination details and other epidemiological risk factors were collected from flock owners and descriptive statistics, chi-square analysis and logistic regression models were fitted to identify the significant risk factors for PPR incidence. The farmer's education, flock size, rearing pattern, and awareness of PPR vaccination were found to be significant pre-disposing risk factors for PPR exposure in the flocks. Hence, the control and eradication strategy need to be designed comprehensively considering the key social factors like education and vaccination awareness along with other flock level risk factors to eradicate PPR by 2030 in consonance with the global plan.

Effect of large-scale land acquisition by domestic and international entities on farm investment in northern Ghana.

Rural households remained the access-marginalized demographic in Africa despite massive land rushes by both domestic and foreign enterprises. As a result, investment decisions made by agricultural households are affected. To resolve this issue, a study that examines how exposure to large-scale land acquisition (LSLA) impacts people's agricultural investment decisions is required. Using data from 664 households selected using a multistage sampling technique, we examined the nexus between LSLA under local and foreign organizations and short-term investment (STI) and long-term investments (LTI) in land-improving measures. The results revealed that although LSLA under domestic and foreign entities tends to have a negative and statistically significant impact on the probability of LTI, it has a positive and significant impact on the probability of doing so for STI. The results also showed a simultaneity between LSLA and farm investment. Thus, giving farmers legal ownership safeguards them as they undertake LTI. To encourage household investment and reduce further exposure to LSLA, improvements in legal land ownership can be made.

Avoidance of care: how health-care affordability influenced COVID-19 disease severity and outcomes.

In this study we examined the association between payor type, a proxy for health-care affordability, and presenting COVID-19 disease severity among 2108 polymerase chain reaction-positive nonelderly patients admitted to an acute-care hospital between March 1 and June 30, 2020. The adjacent-category logit model was used to fit pairwise odds of individuals' having (1) an asymptomatic-to-mild modified sequential organ failure assessment (mSOFA) score (0-3) versus a moderate-to-severe mSOFA score (4-7) and (2) a moderate-to-severe mSOFA score (4-7) versus a critical mSOFA score (>7). Despite representing the smallest population, Medicare recipients experienced the highest in-hospital death rate (19%), a rate twice that of the privately insured. The uninsured had the highest rate of critical mSOFA score on admission and had twice the odds of presenting with a critical illness when compared with the privately insured (odds ratio = 2.08, P =.03). Because payor type was statistically related to the most severe presentations of COVID-19, we question whether policy changes affecting health-care affordability might have prevented deaths and rationing of scarce resources, such as intensive care unit beds and ventilators.

Preferences in adolescents and young people's sexual and reproductive health services in Nigeria: a discrete choice experiment.

BACKGROUND: Barriers to utilization of sexual and reproductive health (SRH) services by adolescents and young people (AYP) have persisted despite evidence that youth-friendly services have a positive effect on contraceptive use, and patient knowledge and satisfaction. OBJECTIVE: The objective of this study was to elicit, and derive relative valuations for, attributes of SRH services that adolescents and young people value, and their willingness to pay for these services, in public health facilities. METHODS: A discrete-choice-experiment (DCE) that was developed using a mixed methods approach was administered to AYP from Ogun State, Southwest Nigeria. The DCE attributes were: the type of staff; physical environment; health worker attitude; cost; waiting time; contraceptive availability; and opening hours. The choice tasks had two unlabeled alternatives and an opt-out option. Panel mixed logit choice model was used to fit the choice data, along with estimation of willingness to pay (WTP). Also, a latent class logit model was used to detect underlying preference heterogeneity among the respondents. Finally, the uptake of the services in health facilities was investigated by estimating the probabilities for selecting hypothetical health facilities under different scenarios. RESULTS: A total of 859 AYP participated resulting in 6872 choice observations. The physical environment attribute had the highest utility rating relative to the other attributes, followed by preference for a doctor and for a service provider who was open and friendly. The cost and time coefficients were negative, revealing preference for lower cost and shorter waiting time. The latent class model had three classes that varied by their background characteristics. Probability of choosing any of the facility alternatives increased with introduction of more favorable facility characteristics. CONCLUSION: The pattern of preferences identified are potential targets for service design and delivery optimization that may result in improvements in service acceptability and utilization. These results strengthen the call for involving AYP in decision-making in health interventions for them and developing context-specific SRH programs for AYP in public health facilities.

Assessing the effects of geometry and non-geometry related factors in work-zone crashes.

OBJECTIVE: Work zones are unique in geometry and traffic management, utilizing special traffic signs, standard channelizing devices, appropriate barriers, and pavement markings. These configurations can introduce unexpected driving conditions, potentially posing risks to drivers. This analysis aims to explore potential differences in contributing factors between work-zone crashes where geometry was identified as a factor and those where it was non-geometry factor. To gain insights into driver injury severities in single-vehicle work-zone crashes, this study analyzed work zone crash data from Florida. METHOD: This study employed random parameters logit models, accommodating potential variations in parameter estimates' means and variances. The dataset encompassed a wide array of factors known to influence driver injury severity, encompassing crash characteristics, vehicle attributes, roadway features, prevailing traffic volume, driver profiles, and spatial and temporal considerations. RESULTS: This analysis yielded significantly distinct parameters for work-zone crashes, distinguishing between geometry-related and non-geometry-related factors (primarily the human factors). This distinction suggests a complex interplay between these factors. Notably, the marginal effects of individual parameter estimates exhibited marked differences between these two categories - geometry and non-geometry factors. CONCLUSION: These findings contribute to the growing body of research indicating that geometric restrictions within work zones introduce a distinct set of risk factors compared to non-geometry-related factors. Recognizing the significance of geometric restrictions, beyond typical driving conditions, holds the implications for enhancing safety within various work zone configurations and offers valuable insights for crash scene investigators to pinpoint contributing factors accurately.

Investigating the severity of single-vehicle truck crashes under different crash types using mixed logit models.

INTRODUCTION: Almost 90% of fatal road crashes occur in developing countries. Among these countries, Iran has a noticeable fatal crash rate of 21.47 deaths per 100,000 persons. Improving the safety of trucks is of particular importance in Iran where road freight is used to transport almost 90% of the commodities. Researchers have suggested dichotomizing crashes into single- and multi-vehicle categories and found that when this is performed vast differences can be identified between the mechanisms behind these categories of crashes, particularly when investigating truck crashes. METHOD: This study investigated single-vehicle truck crashes in Khorasan Razavi province in Iran from 2013 to 2021. Likelihood ratio tests were employed to show that separate models are statistically valid for different crash types. Subsequently, three mixed logit crash-type models were developed to investigate 5,703 single-vehicle truck crashes. RESULTS: Four significant variables were exclusive to collisions with an object (brake failure, ABS, primary roads, and rainy or snowy weather), five significant variables were associated with run-off-road crashes (driving a loaded truck, speed limit (>60 km/h), paved shoulders, driving uphill, and inability to control the truck), and three significant variables were associated with overturn crashes (overloaded truck, curved roads, and changing direction suddenly). In all crash types, both fastening the seatbelt and speeding were found to be significant factors. CONCLUSION: The research highlights the need to analyze single-vehicle truck crashes using distinct crash type models and highlights the unique contributing factors of three common single-vehicle crash types. PRACTICAL APPLICATIONS: The study presents recommendations for policy to address key crash risks for trucks in Iran, including education and training to improve driver experience, compliance with seat belt usage, enforcement of speeding, and vehicle technologies to monitor drivers.