Shoulder Width Research Articles

Crash severity prediction and interpretation for road determinants based on a hybrid method

In this study, 4 classification algorithms were employed to characterize the influence of road determinants on roadway crash severity with actual crash data. The crash data were obtained from crash records in Texas, USA, from January 2020 to April 2021. The prediction model of crash severity utilized 12 road-related features—including shoulder types, shoulder width, and curb types—as well as 10 other features—such as weather and illumination conditions—as input features. Three crash severity levels—“Minor Damage,” “Moderate Damage,” and “Severe Damage”—were used as output features. Decision tree, support vector machines, and multi-layer perceptron were employed to compare their prediction performance with the XGBoost model. The results show that the XGBoost model yields the best performance among the 4 algorithms. The overall accuracy, average precision, average recall, and average F1 score of the XGBoost model were 82.65%, 0.83, 0.82, and 0.82, respectively. Besides, SHapley Additive exPlanations (SHAP) and partial dependence plots were used to interpret the model results. Among the road-related features, the most influential one is the median width. Greater crash severity is related to paved right shoulder and curb. These findings are helpful for the design and planning of road safety.

ROAD TRAFFIC ACCIDENTS FACTOR ON RURAL ARTERIAL ROADS

In Indonesia, traffic accidents occur on 23.55% of national roads and 75.08% of rural roads. The rural roads were identified as accident-prone areas. As a result, unsafe roads hinder population mobility, disrupt daily life, and reduce access to education, employment, and essential services, thereby affecting the well-being and development of rural communities. To identify the dominant factors that influence the frequency of collisions, a more thorough analysis of the multifactorial causes of traffic accidents in accident-prone areas is necessary. This study focused on analyzing the factors that contribute to road traffic accidents. Cross-tabulation was done using chi-square analysis. The chi-square test does not assume a normal distribution of data, which is beneficial when dealing with real-world data that may not follow normal distribution patterns. This flexibility makes it a robust choice for analyzing traffic accident data, which can be highly variable and not normally distributed. The results of the chi-square analysis for significance values below 0.05 indicate that there is a correlation between accident-causing factors (the independent variable) and collision frequency (the dependent variable). The results indicate that human factors, such as carelessness and high speed, as well as road and environmental factors, such as horizontal alignment, road width, clear zone, road signs, road markings, and land use, influence traffic accidents. Infrastructure factors such as horizontal alignment, road width, clear zones, shoulders, signs, and markings influence traffic accidents because they directly impact road user safety. Non-standard road geometry (horizontal alignment, road width, shoulder width, and clear zone) combined with incomplete road safety facilities (signs and markings) have the potential to cause traffic accidents. Therefore, harmonizing road geometry and equipment is necessary to improve arterial road safety, especially in accident-prone areas. Furthermore, speed management across a variety of land uses is required to reduce traffic accidents.

Exploring interacting effects of risk factors on run-off-road crash severity: An interpretable machine learning model joint with latent class clustering

Run-Off-Road (ROR) crashes are frequent and pose a significant risk of injury and fatality. Given the complexity of their mechanisms and the interaction of multiple factors, this study aims to comprehensively investigate the factors influencing the severity of ROR crashes, which have been understudied. Furthermore, addressing the current methodological challenge in machine learning (ML) crash modeling, this study proposes an approach to tackle unobserved heterogeneity in ML. An interpretable ML joint with prior latent class clustering is implemented. The significant risk factors and interactions are interpreted using SHAP (SHapley Additive exPlanations) method across clusters. This study utilizes ROR crash records, traffic, and geometric data from main suburban freeways in Iran collected over a 5-year period. The key interacting factors associated with severe ROR during adverse weather (cluster 1) are: co-occurrence of low congestion and higher speed variation; low congestion, nighttime darkness and rollover; roadway departure by buses and mini-buses and rollover occurrence; vehicle departure and collision with fixed objects. Moreover, the critical interactions for nighttime condition (cluster 2) are: curve sections combined with longitudinal slope; inside shoulder width <1.5 m and a hit median concrete NewJersey barrier. The risky interactions for crashes occurred in curves (cluster 3) are: departing in two-lane sections in low congestion conditions; vehicles collisions with the median concrete NewJersey barriers. The findings of this study enhance comprehension of the significant effects of interactions under various conditions, offering valuable insights for policymakers. Additionally, recommendations are offered to mitigate the risk of severe ROR crashes.

Redesign of elderly beds as an effort to overcome degenerative diseases using a participatory approach

The utilization of beds among the elderly, especially those with degenerative diseases, presents health and comfort challenges. To address these issues, this research aims to redesign elderly beds using a participatory ergonomics method at Madania Potonoro Nursing Home. By involving the elderly and caregivers in the process, the research findings highlight the need for bed improvements. The proposed design includes additional features such as a folding table, bookshelf, cane holder, assistive standing devices, and a drink holder, based on identified needs. Anthropometric dimensions, including Shoulder Width (SW), Height (H), Popliteal Height (PH), Elbow Height in Sitting (EHS), Elbow Height in Standing (EHS), Seat Depth (SD), and Maximum Grip Diameter (MGD), serve as guidelines for bed design. Analyzing these dimensions at the 5th, 50th, and 95th percentiles, adjustments are made to accommodate additional attributes or features on the bed. The recommended design not only provides optimal comfort and safety for the elderly but also improves their overall quality of life. This research emphasizes the importance of involving users in the design process to ensure solutions align with their needs and preferences.

Estimation of the genetic parameters of sheep growth traits based on machine vision acquisition

In the realm of animal phenotyping, manual measurements are frequently utilised. While machine-generated data show potential for enhancing high-throughput breeding, additional research and validation are imperative before incorporating them into genetic evaluation processes. This research presents a method for managing meat sheep and collecting data, utilising the Sheep Data Recorder system for data input and the Sheep Body Size Collector system for image capture. The study aimed to investigate the genetic parameter changes of growth traits in Ujumqin sheep by comparing machine-generated measurements with manual measurements. The dataset consisted of 552 data points from the offspring of 75 breeding rams and 399 breeding ewes. Six distinct random regression models were assessed to pinpoint the most suitable model for estimating genetic parameters linked to growth traits. These models were distinguished based on the inclusion or exclusion of maternal genetic effects, maternal permanent environmental effects, and covariance between maternal and direct genetic effects. Fixed factors such as individual age, individual sex, and ewe age were taken into account in the analysis. The genetic parameters for the yearling growth traits of Ujumqin sheep were calculated using ASReml software. The Akaike information criterion, the Bayesian information criterion, and fivefold cross-validation were employed to identify the optimal model. Research findings indicate that the most accurate models for manually measured data revealed heritability estimates of 0.12 ± 0.15 for BW, 0.05 ± 0.07 for body slanting length, 0.03 ± 0.07 for withers height, 0.15 ± 0.12 for hip height, 0.11 ± 0.11 for chest depth, 0.13 ± 0.13 for shoulder width, and 0.53 ± 0.15 for chest circumference. The optimal models for machine-predicted data showed heritability estimates of 0.1 ± 0.09 for body slanting length, 0.14 ± 0.12 for withers height, 0.55 ± 0.15 for hip height, 0.34 ± 0.15 for chest depth, 0.26 ± 0.15 for shoulder width, and 0.47 ± 0.16 for chest circumference. In manually measured data, genetic correlations ranged from 0.35 to 0.99, while phenotypic correlations ranged from 0.07 to 0.90. In machine data, genetic correlations ranged from −0.05 to 0.99, while phenotypic correlations ranged from 0.03 to 0.84. The results suggest that machine-based estimations may lead to an overestimation of heritability, but this discrepancy does not impact the selection of breeding models.

Sensitivity analysis of the calibration factor resulting from the application of the HSM predictive method to Italian rural two-lane, two-way roads. The Emilia-Romagna case study

The present study investigates the adoption of predictive methods for road safety in the Italian context. The main goal of predictive methods is to describe safety performances of a road infrastructure according to the real number of occurred crashes and the actual infrastructure characteristics. Currently, the most reliable and widely used model is reported in the Highway Safety Manual (HSM), initially developed for the United States. In order to adapt the HSM existing model to the Italian context a calibration procedure is required. The main purpose of this study is to conduct the sensitivity analysis of the calibration factor, to evaluate the influence of various road characteristics (expressed in terms of Crash Modification Factors – CMFs) on the reliability of the model. Results show that the most influential factors are represented by planimetric curve radius, followed by shoulder type and width and Roadside Hazard Rating (RHR). In addition, the Italian roads taken into account for the present case study, which are single carriageway rural roads located in Emilia Romagna region, have generally exhibited more critical conditions compared to American roads. As a consequence, CMFs expressing the aforementioned infrastructure characteristics are typically greater than 1. Furthermore, road features and driving habits vary significantly with the orographic context (mountain, hill, or valley) and geographical area, factors that cannot be neglected when calibrating predictive models. As a result, a layered calibration would bring to the most reliable solution and, in specific situations, alternative predictive models to HSM could be developed.

Effects of nonsynonymous single nucleotide polymorphisms of the KIAA1217, SNTA1 and LTBP1 genes on the growth traits of Ujumqin sheep.

Sheep body size can directly reflect the growth rates and fattening rates of sheep and is also an important index for measuring the growth performance of meat sheep. In this study, high-resolution resequencing data from four sheep breeds (Dorper sheep, Suffolk sheep, Ouessant sheep, and Shetland sheep) were analyzed. The nonsynonymous single nucleotide polymorphisms of three candidate genes (KIAA1217, SNTA1, and LTBP1) were also genotyped in 642 healthy Ujumqin sheep using MALDI-TOFMS and the genotyping results were associated with growth traits. The results showed that different genotypes of the KIAA1217 g.24429511T>C locus had significant effects on the chest circumferences of Ujumqin sheep. The SNTA1 g.62222626C>A locus had different effects on the chest depths, shoulder widths and rump widths of Ujumqin sheep. This study showed that these two sites can be used for marker-assisted selection, which will be beneficial for future precision molecular breeding.

Нормированная система оценки предрасположенности к различным видам плавания

The article presents the results of a study conducted in the period from 2021 to 2023, involving 147 athletes - swimmers. The following morphometric body build indices were selected for normalization and identification of children's predisposition to swimming in different styles: body mass index (BMI), relative arm length index (RALI), ARE index (ARE), relative leg length index (RLI), Scelia index (SI), relative shoulder width index (RWI), Pinier index (PI), Erisman index (EI). As a result of the research the following results were obtained: for the butterfly style the most informative indices are ARE, SSPI, IE; for the backstroke method - IODR, IODN, IS; for the breaststroke method: IODN, IS, SSPI; for the freestyle: BMI, ARE, IP, IODR. Quantitative normalization of the obtained indices was carried out to identify high and low level of predisposition to the above mentioned styles of swimming-82B0-4

Presentation of artificial neural network models based on optimum theories for predicting accident severity on rural roads in Iran

This study focuses on predicting the number and severity of rural accidents using nine accident-related variables by the multi-layer perceptron (MLP) approach as a type of artificial neural network modeling method. The models were developed using the input parameters of shoulder width, roadway width, roadside hazard, access density, passing zone ratio, speed limit, pavement condition index, shoulder rumble strips, and centerline rumble strips. MLP models were created based on accident data that occurred from 2019 to 2020 on the Tehran–Qom and Tehran-Saveh rural roads in Iran. In order to achieve the highest accuracy, twenty MLP models have been built with various structures. The MLP model, consisting of a multilayer feedforward network with hidden sigmoid and softmax output, has been used in this study. In this regard, an attempt was made to present an optimum theory to select the best model. The most accurate model was chosen based on the R-value and root mean square error (RMSE), mean absolute error (MAE), f, SD, and R2. Results indicated that the R-value obtained from the optimum model was 0.912, representing the accurate performance of the selected model. In addition, access density, roadside hazard, and roadway width were identified as the most significant variables.

Accuracy and feasibility of 3D virtual dynamic fit technology

PurposeThe research evaluated the feasibility of 3D dynamic fit utilizing female compression tops by comparatively analyzing the virtual and actual dynamic fit.Design/methodology/approachSix female participants were 3D body-scanned and photographed in compression tops in four types of athletic movements (pull-up, kettlebell swing, circle-crunch and sit-up). Fit measurements, waist cross-sectional areas, waist width, waist depth, numerical simulation of clothing pressure (kPa) and objective pressure measurements (kPa) were collected from 3D virtual animation, 3D fit scan data and actual photos with the four types of athletic motions. The data were comparatively investigated between virtual and actual dynamic fit.FindingsThe 3D-animated body was not reflected with human body deformation because only bone structure was changed while maintaining the constant forms of muscle and body surface in athletic movements. Due to this consistency of virtual dynamic fit, there were significant differences with the actual dynamic fit at the top length, shoulder width and waist cross-sectional areas. Also, the virtual dynamic pressure indicated significantly higher levels than the objective dynamic pressure while presenting no significant correlations at the front neckline, breast, lateral waist, upper back, back armhole and back waist.Originality/valueThis study is the first to verify multiple aspects of virtual dynamic fit using 3D digital technology. This study provided useful information about which aspects of the current virtual animation need to be improved to apply in the dynamic fit evaluation.

Horizontal Curve Safety Performance Evaluation Based on Naturalistic Vehicle Lane Position Data

Performance-based design is a renewed approach for project decision-making to specifically address the purpose and needs of projects by providing design flexibility. Resources to evaluate roadway design based on safety performance are an important need for transportation practitioners. In this study, naturalistic driving study (NDS) data were used to estimate the safety effect of elements that influence driving behavior on rural undivided two-lane horizontal curves. Available data included 3,292 horizontal curves and 150,233 traversals, which required a significant data processing effort to conduct data cleaning and quality assessment. From the safety surrogates evaluated and methods implemented, lane position provided the most consistent and statistically significant results. Centerline and edge line encroachment events were modeled with the negative binomial using traffic volume and curve geometry as the predictor components. Encroachment estimates were associated with observed crashes from state data to convert encroachments to crashes. Predictor variables such as curve radius showed a decreasing trend in predicted crashes as curve radius increased. Similarly, as shoulder or lane width increased, predicted crashes decreased. Crash estimates derived from safety surrogates were used to develop an analytical tool aimed at practitioners for curve design considerations. Data input includes curve radius, shoulder width, lane width, curve length, traffic volume, expected service life, and construction cost. The economic assessment provides a quantitative measure for practitioners to evaluate alternative curve designs by assessing the tradeoffs between safety and costs of implementation.

Estimation of Weight and Body Measurement Model for Pigs Based on Back Point Cloud Data.

Pig farming is a crucial sector in global animal husbandry. The weight and body dimension data of pigs reflect their growth and development status, serving as vital metrics for assessing their progress. Presently, pig weight and body dimensions are predominantly measured manually, which poses challenges such as difficulties in herding, stress responses in pigs, and the control of zoonotic diseases. To address these issues, this study proposes a non-contact weight estimation and body measurement model based on point cloud data from pig backs. A depth camera was installed above a weighbridge to acquire 3D point cloud data from 258 Yorkshire-Landrace crossbred sows. We selected 200 Yorkshire-Landrace sows as the research subjects and applied point cloud filtering and denoising techniques to their three-dimensional point cloud data. Subsequently, a K-means clustering segmentation algorithm was employed to extract the point cloud corresponding to the pigs' backs. A convolutional neural network with a multi-head attention was established for pig weight prediction and added RGB information as an additional feature. During the data processing process, we also measured the back body size information of the pigs. During the model evaluation, 58 Yorkshire-Landrace sows were specifically selected for experimental assessment. Compared to manual measurements, the weight estimation exhibited an average absolute error of 11.552 kg, average relative error of 4.812%, and root mean square error of 11.181 kg. Specifically, for the MACNN, incorporating RGB information as an additional feature resulted in a decrease of 2.469 kg in the RMSE, a decrease of 0.8% in the MAPE, and a decrease of 1.032 kg in the MAE. Measurements of shoulder width, abdominal width, and hip width yielded corresponding average relative errors of 3.144%, 3.798%, and 3.820%. In conclusion, a convolutional neural network with a multi-head attention was established for pig weight prediction, and incorporating RGB information as an additional feature method demonstrated accuracy and reliability for weight estimation and body dimension measurement.

A nested grouped random parameter negative binomial model for modeling segment-level crash counts

In this study, a nested grouped random parameter negative binomial framework is proposed to model crash counts at the segment level, a three-level longitudinal framework. The proposed model accounts for correlations along county routes and over time and thus includes a time variable, the year index, to analyze crash counts. The model is applied to crashes on undivided two-lane arterial roads in Ohio from 2012 to 2017. The results present two variants of the model: one with varying intercepts and fixed slopes and the other with varying intercepts and slopes. Both variants have comparable interpretations concerning the fixed parameters, but the latter variant exhibits a significantly improved fit and provides additional information on the interpretations. The results show a significant quadratic relationship between the time variable and the crash count, indicating that, on average, the crash count of segments increases with a decreasing rate as time variable increases. Regarding random parameters, the findings show that 17% of segments within routes and 2% of routes exhibit crash counts that decrease at accelerating downward trend as time variable increases. The effect of the natural logarithm of the segment length varies significantly across different routes, with an increase in this value primarily leading to an increase in crashes. On the other hand, the effect of the total shoulder width also varies across routes, but unlike the former, an increase in this value generally results in a decrease in crashes. The proposed model shows high forecast accuracy for crash count prediction, making it a valuable tool for informed decision-making in safety improvement.

CORRELATION ROAD GEOMETRIC ON ROAD DAMAGE

The implementation of safe road infrastructure can be defined by the minimum number of traffic accidents that occur on these roads. Traffic accidents can caused by several factors, encompassing the geometric attributes of the road, characteristics of vehicles, human elements, and environmental conditions. Previous studies have identified specific geometric features, including superelevation, degree of curvature, road slope, and shoulder type, as influential factors in traffic accidents. Simultaneously, the state of the pavement, characterized by issues such as rutting, potholes, IRI value, and skid resistance, also plays a crucial role in traffic accident. The interplay between road geometric and pavement conditions underscores the importance of effective road management for ensuring safety. Existing research highlights a correlation between road geometric and road damage, with the Pavement Condition Index (PCI) directly correlating with road and shoulder width, and inversely correlating with transverse and longitudinal road slope. This phenomenon is, in part, attributed to heightened shear strain on narrow road shoulders and a reduction in the resilience modulus value of the subgrade on sloped roads.

Body surface scan anthropometrics are associated with grip strength in the general population

Background and aimBody shape and anthropometrics are well-known risk factors for cardiovascular diseases (CVD) and mortality. Hand-grip strength (HGS) is also a meaningful marker of health and a promising predictor of CVD and mortality. There is a lack of studies that have systematically investigated associations between body shape and anthropometrics with HGS. In a population-based study, we investigated if anthropometric markers derived from 3D body scanning are related to HGS. Methods and resultsWe used the data of 1,599 individuals aged 36 to 93 years, who participated in the Study of Health in Pomerania. A total of 87 anthropometric markers, determined by a 3D body scanner, were included in the analysis. Anthropometric measurements were standardized and used as exposure variables. HGS was measured with a hand dynamometer and used as outcome. Sex-stratified linear regression models adjusted for age and height were used to relate standardized anthropometrics and HGS. Anthropometric markers were ranked according to -log-p-values.In men, left and right forearm circumference, left arm length to neck (C7), left forearm length, and forearm-fingertip length were most strongly related to HGS. In women, right forearm circumference, forearm-fingertip length, shoulder breadth, left forearm circumference, and right wrist circumference showed the most significant associations with HGS. The final prediction models contained 13 anthropometric markers in males (R2=0.54) and eight anthropometric markers in females (R2=0.37). ConclusionsThe identified parameters may help estimate HGS in the clinical setting. However, studies in clinical settings are essential to validating our findings.

The effect of hip shoulder width ratio and vertebral column length on sensory level in term participants posted for lower segment caesarean section under spinal anaesthesia in Indian population: A prospective observational study

Hypotension is the most common complication of spinal anesthesia with high intrathecal spread of local anaesthetic drug especially during caesarean section. Hip Shoulder width Ratio (HSWR) is one of the factors which affects spread of spinal anaesthesia.85 ASA II participants with singleton term pregnancy undergoing elective caesarean section received 12mg hyperbaric bupivacaine intrathecally with 26G Quincke’s spinal needle in L3-4 intervertebral subarachnoid space via midline approach. Post spinal anaesthesia haemodynamic parameters were monitored every 5 minutes for the first 20 minutes and at the end of surgery. Sensory level was assessed by a pinprick test every 5 minutes till 20 minutes and post-surgery. Hypotension was defined as 20% fall from baseline systolic blood pressure at 15 minutes post spinal anaesthesia.We found significant positive correlation between high shoulder width ratio and highest sensory level achieved (p - 0.0005) using mutiple regression analysis and pearson's correlataion. With every unit increased in high shoulder width ratio spinal level significantly increased by 5 units. Hip shoulder width ration and age were significantly correlatwd with incidence of hypotension.Hip-shoulder Width Ratio has a positive effect on cephalad spread of spinal anaesthesia and thus incidence of hypotension. By knowing the Hip-shoulder Width Ratio, it can help anaesthesiologists to predict the spread of spinal anaesthesia and titrate the dose of 0.5% hyperbaric bupivacaine.

How Approaching Angle, Bottleneck Width and Walking Speed Affect the Use of a Bottleneck by Individuals.

Understanding pedestrian dynamics at bottlenecks and how pedestrians interact with their environment-particularly how they use and move in the space available to them-is of safety importance, since bottlenecks are a key point for pedestrian flow. We performed a series of experiments in which participants walked through a bottleneck individually for varying combinations of approaching angle, bottleneck width and walking speed, to investigate the dependence of the movement on safety-relevant influencing factors. Trajectories as well as 3D motion data were recorded for every participant. This paper shows that (1) the maximum amplitude of shoulder rotation is mainly determined by the ratio of the bottleneck width to the shoulder width of the participant, while the direction is determined by the starting angle and the foot position; (2) the 'critical point' is not invariant to the starting angle and walking speed; (3) differences between the maximum and minimum speed values arise mainly from the distribution of deceleration patterns; and (4) the position of crossing shifts by 1.75 cm/10 cm, increasing the bottleneck width in the direction of origin.

Redesign of Rice Planting Tools Using an Ergonomics Approach

Farmers' work in planting rice is always with a bent posture, causing complaints of musculoskeletal disorders, this is a problem in research. The aim of the research is to identify farmers' complaints of musculoskeletal disorders when planting rice and determine the size of ergonomic tools. The method used is standard Nordic questionnaires to determine the point of complaint among farmers, anthropometry and percentiles are used to design the tool. Statistical tests are used to see valid, uniform, and normal data. Results and discussion, in the standard Nordiq questionnaire, it was found that farmers experienced more than 50% complaints of severe pain in the waist, left calf, right calf, left ankle, right ankle, left foot, and right foot. The design uses eight anthropometric dimensions: standing elbow height, palm length, palm width, middle finger length, shoulder width, body thickness, upper arm length, and hand length. All anthropometric dimensions were declared uniform, in the data adequacy test they were declared valid, and in the normality test, all data were declared normally distributed. The 5th percentile is used for the dimensions of shoulder width, standing elbow height, and palm length, and the 95th percentile is used for the dimensions of palm width, middle finger length, body thickness, upper arm length, and hand length.In conclusion, complaints of musculoskeletal disorders were identified as very painful, located at the point of complaint of the waist. The rice planting tool has ergonomic dimensions, the height of the tool is 88.1 cm, the diameter of the tool handle is 8.13 cm, the length of the handle is 11.4 cm, and the distance between the handle and the brake is 9.32 cm. The dimensions of the rice seed bag are 33.70 cm wide, the distance between the front and back of the bag is 23.19 cm, the bag height is 35.64 cm, and the bag length is 20.5 cm.

Analisis Hubungan Geometrik Jalan dengan Potensi Kecelakaan di Jalan Imogiri - Dlingo, Bantul, Indonesia

Imogiri - Dlingo road section is a primary collector road located in the Mangunan tourist area, Bantul, Indonesia. This road is a provincial road with hilly terrain, lots of sharp turns, and long ascents and descents, which are potential for traffic accidents. Police data records that in February 2022 there was an accident on this road with 13 deaths and 33 injuries. Therefore, this study aims to evaluate road geometric, both horizontal and vertical alignments on Imogiri - Dlingo road based on Guidelines for Road Geometric Design number 13/P/BM /2021. To do so, road geometric analysis is conducted by employing road safety inspection data, which is then modeled using AutoCAD Civil 3D software. Based on the survey on road geometric, it is known that the number and width of lanes and shoulders are in accordance with Guidelines. Meanwhile, the analysis results using computer program show that this road has 24 horizontal and 6 vertical alignments. Nevertheless, 87% of bend radius, 67% of curve length, 58% of bend angle, 43% of slope, 57% of critical length, and 67% of control design (K) are not fulfilled the requirements. Therefore, Imogiri - Dlingo road is dangerous road and is potentially causing fatal accidents.

Extensive hypothesis testing for estimation of crash frequency models

Estimating crash data count models poses a significant challenge which requires extensive knowledge, experience, and meticulous hypothesis testing to capture underlying trends. Simultaneous consideration of multiple modelling aspects is required including, among others, functional forms, likely contributing factors, and unobserved heterogeneity. However, model development, frequently affected by time and knowledge, can easily overlook crucial modelling aspects such as identification of likely contributing factors, necessary transformations, and distributional assumptions. To facilitate model development and an estimation that can extract as many insights as possible, an optimization framework is proposed to generate and simultaneously test a diverse array of hypothesis. The framework comprises a mathematical programming formulation and three alternative solution algorithms. The objective function involves minimizing the Bayesian Information Criterion (BIC) to avoid overfitting. The solution algorithms include metaheuristics to deal with an NP-hard problem and search through a complex and nonconvex space. The metaheuristics also enable to handle unique datasets through varying search strategies. The effectiveness of the proposed framework was ascertained using three distinct datasets, and published models used as benchmarks. The results highlighted the ability of the proposed framework to estimate crash data count models, surpassing benchmark models in terms of insights and goodness-of-fit. The framework provides several advantages, such as robust hypothesis testing, uncovering unique specifications and vital insights in the data, and leveraging existing knowledge to enhance search efficiency. The framework also exposes the vulnerability of traditional analyst efforts to fall into local optima, bias, and limitations in creating more efficient models. In a compelling example using crash data from Washington, the proposed framework unveiled insights overlooked by a benchmark published model, identifying speed, interchanges, and grade breaks as likely crash contributors, and revealing the potential danger of excessively wide shoulders. Conversely, the benchmark model identified fewer contributing factors and missed a crucial non-linear relationship between crash safety and shoulder widths. While wider shoulders are typically associated with improved safety, the proposed models suggest a safety threshold beyond which further widening could decrease safety. The introduction of random parameters in the analysis revealed a more nuanced relationship with crash frequency, thereby underlining the limitations of models incapable of capturing heterogeneity.