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category: literaturenote citekey: jafariinvestigatinginfluencesocioeconomicfactors2025 title: Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach authors: "Jafari, Mahsa; Persaud, Bhagwant" year: 2025 date: 2025-00-00 2025 doi: 10.1016/j.aap.2025.108076 publication: "Accident Analysis & Prevention" url: "https://doi.org/10.1016/j.aap.2025.108076" zotero_key: GR6VQ68S zotero_storage: ASEB5UDE collections: transpordi_planeerimine folder: 001_artiklid/Road Safety Indicators firstAuthor: "Jafari, Mahsa"
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Accident Analysis and Prevention
journal homepage: www.elsevier.com/locate/aap
Investigating the influence of socioeconomic factors on the relationships between road characteristics and traffic crash frequency and severity-- A hybrid structural equation modelling − artificial neural networks approach☆
Mahsa Jafari * , Bhagwant Persaud
Department of Civil Engineering, Toronto Metropolitan University, 350 Victoria St, Toronto, ON M5B 2K3, Canada
ARTICLE INFO
Keywords: Crash severity Traffic safety Structural equation modeling Moderation Artificial neural networks
ABSTRACT
Traffic crashes result from complex interactions between driver, roadway, and environmental factors, which traditional methods often fail to capture. This paper investigates the influence of road, weather, and socioeconomic factors on traffic crashes, using a two-stage hybrid Structural Equation Modelling (SEM)-Artificial Neural Networks (ANN) approach to capture the complex relationships between these factors and crash intensity, a variable that jointly captures the frequency and severity of crashes. A database from Ohio collector road segments served as the case study in this novel hybrid approach, which utilized SEM to analyze the complex and moderating relationships between different factors and crash intensity. SEM revealed significant relationships between crash intensity and factors such as "Horizontal Curve," "Road" (AADT and surface width index), "Segment Length," "Speed Limit," "Vertical Curve," and "Vehicle Possession." Based on the SEM results, "Vehicle Possession" significantly moderated the relationship between "Horizontal Curve" and crash intensity. In the next step, ANN further identified key predictors, including "Segment Length," "Road," the interactions of "Vehicle Possession-Speed Limit," "Vehicle Possession-Vertical Curve," and "Age-Road." The findings highlight the advantage of the complementary application of linear and nonlinear methods in providing invaluable theoretical and methodological insights for crash data analysis.
1. Introduction
Globally, traffic fatalities are unacceptably high (Sheykhfard et al., 2023). According to statistics published by the World Health Organization (WHO), traffic crashes are projected to rank as the fifth leading cause of death in the world by 2030 (World Health Organization, 2018). The WHO revealed that despite various efforts to reduce road crashes, the reported results were not even close to the 50 % reduction target for 2020 (World Health Organization, 2018). In the United States, for example, 40,990 people were killed in highway crashes in 2023, with a fatality rate of 1.26 per 100 million vehicle miles (National Highway Traffic Safety Administration, 2024). These numbers suggest that investigating traffic crashes and evaluating their influencing factors are essential elements of road safety research. Such investigations typically involve analyzing multiple interrelated factors while considering their inherent complexity (Haghshenas et al., 2023). Key contributing factors to traffic crashes can be grouped into categories pertaining to driver traits, road conditions, vehicle characteristics, and crash attributes (Chang & Wang, 2006). Additionally, socioeconomic elements can also play a significant role in explaining traffic collision patterns and disparities across diverse populations (Liu et al., 2024). While previous literature has explored direct relationships between crashes and socioeconomic factors (Gehrke et al., 2023; Mahmoudi et al., 2023; Osama & Sayed, 2017; Saleem et al., 2020), limited attention has been given to potential mediator or moderator impacts that might be exerted on underlying relationships between crashes and contributing factors.
Various statistical approaches have been employed to study the influence of different factors on crash frequency and severity (See (Savolainen et al., 2011) and (Lord & Mannering, 2010) for detailed reviews.); these encompass regression models, Structural Equation
E-mail address: mahsa.jafari@torontomu.ca (M. Jafari).
☆ This article is part of a special issue entitled: 'RSS 2024' published in Accident Analysis and Prevention.
* Corresponding author.
Models (SEM), and machine learning techniques. It is important to note that some of these methodologies (e.g., regressions models) have limitations, including issues related to non-normal distributions of data, over- or under-dispersion, and endogeneity (Lord & Mannering, 2010; Savolainen et al., 2011), that may influence the outcome, potentially making them biased (Wang & Wang, 2012). SEM offers distinct benefits compared to conventional methods through enhanced capacity to model complex interactions among variables, evaluate latent constructs, concurrently test multiple dependent and independent variables, and accommodate both normal and non-normal datasets (Hassan, 2016; Kashani et al., 2021).
Generally, in some SEMs, relationships imply that independent constructs directly influence dependent constructs without any systematic influences from other variables. This assumption, however, is only sometimes valid. The understanding of the nature of direct relationships can change dramatically when a third variable is introduced into the analysis. What is referred to as "moderation" occurs when a third variable (i.e., the moderator) influences the strength or direction of a relationship between two constructs (Hair et al., 2022). The moderation analysis has been employed in a few studies on transportation (Guo et al., 2023; Haerani et al., 2019; Jing et al., 2023; Taiwo et al., 2024; Zhang et al., 2019). However, there has been little or no emphasis on exploring socioeconomic modifiers affecting both crash frequency and severity.
Despite the strengths of SEM, critics point to its incapacity to account for nonlinear variable impacts (Lee et al., 2020), thereby simplifying the complex decision-making process (Leong et al., 2013). To address this issue, artificial neural networks (ANNs), a form of artificial intelligence (AI), can be applied to model complex problems with linear and nonlinear relationships (Parhi et al., 2022). Thus, it seems of interest to complement linear SEM methods with nonlinear ANN methods (Li´ebana-Cabanillas et al., 2018) in that ANNs can be used to deal with these complex problems by modelling linear and nonlinear relationships. It is, however, inappropriate to use ANN methodology to test hypotheses since it is a black-box approach (Leong et al., 2013). Consequently, it is always recommended that a linear SEM model be complemented with a nonlinear ANN model (Li´ebana-Cabanillas et al., 2018).
Among the notable applications of this hybrid method beyond the traffic safety area are those in the fields of business (Arpaci & Bahari, 2023; Xia & Zhang, 2022), health (Sun et al., 2022), data analysis (Leong et al., 2020; Li´ebana-Cabanillas et al., 2018), hospitality and tourism (Xia & Zhang, 2022), structural engineering (Elseufy et al., 2022), and urban planning (Zhou et al., 2021). In one study in the latter category, the annoyance caused by traffic noise was assessed using the "two-stage structural equation modelling with artificial neural networks" approach. PLS-SEM was utilized to determine the weight and importance of each latent factor, test hypotheses and evaluate the reliability and validity of measurements. Then, ANN was used to uncover nonlinear relationships within the research model (Das et al., 2022).
To the best of the authors' knowledge, no study has thoroughly analyzed crashes in the traffic safety area using a hybrid method such as SEM-ANN to jointly consider both crash frequency and severity level while analyzing the moderation effect of socioeconomic variables. Typically, socioeconomic variables represent differences in travel behaviour, driving behaviour, and driving capabilities that are important for safety analyses but are not usually incorporated in traffic safety analysis. To help fill this void, the primary goal, and the central novelty, of this study is the investigation of crash frequency and severity using the hybrid SEM-ANN method to tease out influential road and environmental factors and the moderation effects of socioeconomic variables. The amalgamated measure of crash frequency and severity in the context of this investigation is referred to as crash intensity. Thus, the higher the crash frequency and the greater the severity of these crashes, the larger will be the crash intensity. A database of Ohio collector road segments was used as a case study for the investigation. In the process,
the research sought to address the following questions: 1) Which factors significantly affect crash intensity? 2) Do socioeconomic factors significantly moderate the relationships between road characteristics and crash intensity? 3) What are the most important variables in predicting crash intensity, considering nonlinear and more complex relationships? To address these questions, PLS-SEM was first employed to analyze and capture the moderating effect of certain variables. Then, ANN was applied to evaluate the potentially nonlinear relationships between latent variables and crash intensity.
2. Methodology
As noted above, a two-stage hybrid SEM-ANN approach was adopted. The first stage tested the hypotheses and identified factor scores for the ANN input to the second stage. The Smart-PLS 4.1 software (Ringle et al., 2024) was used to develop the PLS-SEM model. The PLS-SEM algorithm involves combining two models: the measurement model, which links latent variables to their observed variables, and the structural model, which maps how the latent variables relate to each other. The process begins by estimating initial scores for all available latent variables, usually with ordinary regression. It then refines these estimates by calculating how the observed data contribute to each latent variable and by analyzing the relationships between the latent variables. This iterative process ensures that the model maximizes the explained variance in the dependent constructs (Hair et al., 2022).
The next step after estimating the model parameters was to evaluate the PLS-SEM results. The first stage of evaluating the PLS-SEM model was to assess the measurement models. For this purpose, indicator reliability, internal consistency, convergent validity, and discriminant validity were evaluated for each factor following specifications recommended by Hair et al. (Hair et al., 2022). The indicator reliability criterion states that the factor loadings between the indicators and the construct should be at least 0.708. For internal consistency, Cronbach's alpha is a traditional measure, which should be larger than 0.7. However, Cronbach's alpha assumes that the reliability of all indicators is equal and is sensitive to the number of indicators within each construct. Therefore, the reliability coefficient (ρA) was used instead. For internal consistency, values below 0.6 indicate a lack of consistency. Convergent validity measures the share of the variance of the indicators explained by the construct, denoted by Average Variance Extracted (AVE). AVE should be 0.5 or higher, meaning that at least 50 % of the variance is explained by the latent variable. Discriminant validity measures how distinct the construct is from other constructs, which is assessed using the "Fornell-Larcker" criteria. According to the "Fornell-Larcker" criterion, the square root of the Average Variance Extracted (AVE) should be greater than the correlation coefficient with other constructs in the model.
For the second stage, to measure relationships between the predictors and the output, SPSS 29.0 software (IBM Corp, 2023) was used to develop the ANN model, employing a multi-layer perceptron with a Feedforward-Back Propagation algorithm. To specify the model, all independent and moderator variable scores derived from the preceding SEM analysis formed inputs within the initial layer. At the same time, crash intensity served as the ultimate output target. A 10-fold crossvalidation with a 90:10 training–testing data partition was applied to avoid overfitting (Parhi et al., 2022; Sim et al., 2014). The modelling process is shown in Fig. 1.
3. Data
The data set, which was assembled for a study conducted by Saleem et al. (Saleem et al., 2020), combines data from three sources: crash and roadway inventory data (FHWA, 2018), climate data (National Oceanic and Atmospheric Administration, 2018), and socioeconomic census data (U.S. Census Bureau, 2018). The data pertain to 6,971 traffic crashes on Ohio collector road segments during a 6-year period. In creating that
Fig. 1. Conceptual Framework of the study.
dataset, road segments were linked to climate and census data by representing each road segment as a single point at its midpoint; each midpoint was then assigned to the corresponding census-block group polygon that contained it, and a polygon file was used to map each census-block group as a unique geographic area. This process facilitated the spatial matching of road segments with relevant climate and census data (Saleem et al., 2020). Collector road segments were selected for use in this study because drivers from surrounding neighborhoods most likely use them; as a result, the socioeconomic characteristics of those neighborhoods logically become pertinent.
Table 1 summarizes the descriptive statistics for each variable in the dataset. Descriptive labels for the indicator variables used in the analysis, which are found in the last column, were sourced from the original dataset (Saleem et al., 2020). According to this Table, over half of the curves and grades fall into the "tangent" and "level" categories. Similarly, more than half of the population hold a diploma or higher degree and earn over \$50,000 annually. Table 1 also summarizes data on the indicators of crash intensity, for which frequency was modelled as the count of fatal plus severe lane departure crashes, while severity was considered as an ordinal variable, with 1 indicating property damage only (PDO) crashes, 2 indicating minor injury crashes, and 3 indicating fatal and severe injury crashes combined.
4. Analysis and results
As depicted in Fig. 2, the SEM model incorporates independent latent variable indicators comprising horizontal curvature, Ln(AADT), surface width, Ln(segment length), speed limits categorized as 40–50 mph and 50–60 mph, vertical grade, rainfall, and temperatures. Furthermore, socioeconomic variables considered as independent and moderator variables consist of age, the percentage of the population holding a higher degree of education, the percentage of household earnings surpassing \$50,000, and the percentage of the population owning more than one vehicle. Ultimately, crash intensity, the integrated measure of crash frequency and severity level, as defined earlier, was considered as the dependent variable.
In developing the SEM model, the construct reliability and validity were evaluated using criteria and threshold values presented earlier for indicator reliability, consistency reliability, convergent validity, and discriminant validity, which were gleaned from the book published by Hair et al. (Hair et al., 2022). Indicator reliability was assessed using the loadings of indicators on the corresponding latent variables. According to Table 2, construct validity is achieved in the model since the absolute loading values are higher than 0.7. In addition, consistency reliability and convergent validity criteria were also met, as the reliability coefficient (ρA) and Average Variance Extracted (AVE) exceeded 0.7 and 0.5, respectively (Hair et al., 2022).
The discriminant validity of the identified constructs was evaluated using the "Fornell-Larcker" criterion. According to Table 3, discriminant validity of latent variables was also achieved as the square root of AVE values (the diagonal values) are greater than the inter-construct correlation coefficients values (the non-diagonal values).
Lastly, the structural model was evaluated for its explanatory power
Table 1 Descriptive Statistics and Variable Names.
| Variable description |
mean | std | Min | max | Variable name/ category used in the analysis |
|---|---|---|---|---|---|
| Segment length (mi) |
0.58 | 0.94 | 0.001 | 8.03 | Ln(Segment Length) |
| Curve radius (ft) | 69.6 | 195.6 | 0 | 1311 | radiusft_moderate (500–1000 ft) radiusft_tangent (>1000 ft and no radius) |
| Grade percentage | 2.22 | 3.78 | 0 | 20 | grade steepness_level (0–3) grade steepness_steep (≥ 6) |
| Annual Average Daily Traffic |
2132 | 1648 | 92 | 12,360 | Ln(AADT) |
| Surface width (ft) Speed limit (mph) |
20.88 52.78 |
2.58 5.33 |
16.00 25 |
46.00 60 |
surf_wid speedlimit 40–50 mph speedlimit 50–60 mph |
| Total average rainfall and snowfall (in) |
69.7 | 21.73 | 48.2 | 158.64 | average precipitation |
| Average annual maximum temperature (F) |
61.09 | 2.54 | 57.00 | 69.00 | Tempmaxavg |
| Percentage of population ages 25 + with a diploma and university degree |
0.56 | 0.10 | 0.14 | 0.89 | diploma and universityplus |
| Percentage of households with income >\$50,000 |
0.51 | 0.14 | 0.03 | 0.96 | income more than 50 k |
| Percentage of households with at least one vehicle |
0.94 | 0.09 | 0.12 | 1.00 | vehicle 1 and more |
| Percentage of population ages 45+ |
0.46 | 0.1 | 0.04 | 0.83 | Age45plus |
| Fatal plus severe lane departure crashes |
0.59 | 0.88 | 0 | 10 | LNDP_KAB |
| Minor lane departure crashes |
0.16 | 0.42 | 0 | 5 | n/a |
| PDO lane departure crashes |
1.06 | 1.19 | 0 | 18 | n/a |
using the coefficient of determination (R2 ), the traditional measure of model fit. According to the output of PLS-SEM, the value of R2 was 0.085, indicating a weak explanatory power, possibly due to complex relationships between variables. This outcome suggested the need for further exploration using the hybrid SEM-ANN approach, which is described later.
It is noteworthy that SmartPLS, the software used for PLS-SEM,
Fig. 2. The Developed SEM Model.
Table 2 Loadings, Reliability coefficients and Average Variance Extracted values of constructs.
| Constructs | Indicators | Loading | Composite reliability (ρA) |
Average variance extracted (AVE) |
|---|---|---|---|---|
| Horizontal curve |
Radius ft_tangent Radiusft_moderate |
0.981 − 0.806 |
1.184 | 0.805 |
| Road | AADT Surface Width |
0.943 0.681 |
0.818 | 0.677 |
| Intensity | LNDP_KAB Severity |
0.952 0.884 |
0.932 | 0.844 |
| Speedlimit | speedlimit 40–50 speedlimit 50–60 |
− 0.938 0.977 |
1.003 | 0.917 |
| Vertical curve |
Grade steepness level Grade steepness steep |
0.977 − 0.957 |
0.971 | 0.935 |
| Weather | average precipitation Tempmaxavg |
0.935 − 0.836 |
0.838 | 0.786 |
provides various fit measures, including SRMR, Exact fit criteria d_ULS and d_G, NFI, and Chi2 . Nevertheless, Hair et al. emphasize that PLS-SEM focuses on prediction rather than explanatory modeling. Consequently, fit measures have limited utility in PLS-SEM models and can even be detrimental, as researchers may compromise predictive power in pursuit of a better fit (Hair et al., 2022).
After confirming the validity and reliability of the constructs, the path coefficients and p-values for all relationships were evaluated, as shown in Table 4, which reveals that six key factors − horizontal curve, road, segment length, speed limits, vertical grade, and vehicle possession − significantly influence crash intensity.
Considering road alignments, the SEM results revealed that decreasing radii are associated with more severe and frequent crashes, i. e., crash intensity (estimate = -0.233, p < 0.05); this is consistent with previous studies that indicated that sharper curves are associated with increased severity and frequency (Himes et al., 2019; Wu et al., 2017). In terms of vertical grade, the results show that level vertical alignments are associated with increasing crash intensity and that steep grades are related to lower intensity (estimate = -0.091, p = 0.001), which is consistent with the results of Saleem et al. (Saleem et al., 2020). These results can potentially be attributed to higher operating speeds on straight and level segments. For the road characteristics, the results showed that increasing AADT and surface width (which comprise the "road" variable) are associated with increasing crash intensity (estimate = 0.064, p < 0.05). The results also show that higher speed limits are associated with higher intensity (estimate = 0.102, p = 0.002), as indicated by Yu et al. (Yu & Xu, 2018).
Among the socioeconomic factors, vehicle possession positively impacts crash intensity, indicating that an increasing percentage of the population with more than one vehicle per household results in higher crash intensity (estimate = 0.066, p = 0.027), which is consistent with the results of Forrest et al. (Forrest et al., 2023). Regarding the interactions of socioeconomic variables on road and weather characteristics, the results show that vehicle possession moderates the impact of the horizontal curve on the intensity with a coefficient of − 0.072 (p = 0.04). The results further show that as the percentage of the higherincome population rises, the impact of horizontal grade on intensity
Table 3 Discriminant Validity using AVE Square Roots and Correlation Coefficients.
| Age | Intensity | education | horizontal curve | income | road | segment length | Spd limt |
vehicle possession | vertical curve | weather | |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Age | 1.00 | ||||||||||
| Intensity | 0.00 | 0.92 | |||||||||
| education | 0.30 | 0.00 | 1.00 | ||||||||
| horizontal curve | 0.02 | 0.05 | 0.01 | 0.90 | |||||||
| income | 0.06 | 0.05 | 0.29 | 0.09 | 1.00 | ||||||
| road | − 0.01 | 0.09 | 0.02 | 0.19 | 0.22 | 0.82 | |||||
| segment length | 0.00 | 0.27 | 0.05 | 0.39 | 0.15 | 0.16 | 1.00 | ||||
| spdlimt | − 0.01 | 0.07 | 0.07 | 0.02 | 0.03 | − 0.05 | 0.13 | 0.96 | |||
| vehicle possession | 0.36 | 0.03 | 0.50 | 0.02 | 0.21 | − 0.02 | 0.06 | 0.03 | 1.00 | ||
| vertical curve | − 0.01 | 0.09 | 0.05 | 0.11 | 0.06 | 0.08 | 0.44 | 0.04 | 0.05 | 0.97 | |
| weather | 0.03 | 0.06 | − 0.01 | 0.14 | 0.17 | 0.24 | 0.15 | 0.06 | − 0.10 | 0.04 | 0.89 |
Table 4 Path coefficients of SEM model and their significance.
| Relationships | Coefficient | P values |
|---|---|---|
| Age − > Intensity | − 0.013 | 0.691 |
| education − > Intensity | − 0.051 | 0.155 |
| horizontal curve − > Intensity | − 0.233 | <0.001 |
| income − > Intensity | − 0.016 | 0.597 |
| road − > Intensity | 0.064 | <0.001 |
| segment length − > Intensity | 0.3 | <0.001 |
| spdlimt − > Intensity | 0.102 | 0.002 |
| vehicle possession − > Intensity | 0.066 | 0.027 |
| vertical curve − > Intensity | − 0.091 | 0.001 |
| weather − > Intensity | 0.016 | 0.274 |
| Age − vertical curve − > Intensity | − 0.04 | 0.226 |
| vehicle possession − vertical curve − > Intensity | − 0.014 | 0.608 |
| education − road − > Intensity | − 0.009 | 0.57 |
| income − spdlimt − > Intensity | − 0.047 | 0.188 |
| income − horizontal curve − > Intensity | 0.022 | 0.532 |
| education − horizontal curve − > Intensity | 0.013 | 0.734 |
| income − vertical curve − > Intensity | 0.047 | 0.093 |
| Age − spdlimt − > Intensity | − 0.022 | 0.577 |
| education − spdlimt − > Intensity | − 0.001 | 0.99 |
| vehicle possession − spdlimt − > Intensity | 0.018 | 0.605 |
| vehicle possession − horizontal curve − > Intensity | − 0.072 | 0.04 |
| vehicle possession − road − > Intensity | 0.013 | 0.354 |
| income − road − > Intensity | 0.024 | 0.069 |
| education − vertical curve − > Intensity | 0.032 | 0.315 |
| Age − horizontal curve − > Intensity | 0.058 | 0.093 |
| Age − road − > Intensity | − 0.002 | 0.916 |
gets lower. Based on the SEM results, most moderation effects appear negligible and insignificant, likely due to unobserved nonlinearity among relationships. Applying Artificial Neural Networks (ANN) facilitated the exploration of such nonlinear relationships. The ANN investigation is described next.
For the ANN investigation, a 10-fold cross-validation with a 90:10 training–testing data partition was used to avoid overfitting. The 10-fold cross-validation assesses the performance of the ANN model by dividing data into ten subsets. This process helps to reduce the variability in the model performance and provides a more robust evaluation. As shown in Table 5, nine folds train the model in each iteration or network (the training sample), while the remaining fold validates it (the test sample). This process is repeated ten times. The performance metrics of the model are then averaged, providing a robust estimate of its expected performance on unseen data. Root Mean Square Error (RMSE), a measure that reflects the difference between predicted and actual observed values, was then used to evaluate the ANN models as a proxy for prediction accuracy (Li´ebana-Cabanillas et al., 2017). Ideally, minimal values should be achieved in the RMSE scores attained from train or test samples. Table 5 shows the sample sizes of training and test samples in each iteration and their corresponding RMSE. It can be seen that the
Table 5 ANN validation results (RMSE values).
| Network | Sample size (Training) |
Sample size (Test) |
RMSE (Training) |
RMSE (Test) |
RMSE (Training Test) |
|---|---|---|---|---|---|
| 1 | 6278 | 693 | 0.077 | 0.078 | 0.000 |
| 2 | 6225 | 746 | 0.081 | 0.075 | 0.005 |
| 3 | 6215 | 756 | 0.078 | 0.079 | 0.001 |
| 4 | 6291 | 680 | 0.077 | 0.077 | 0.000 |
| 5 | 6242 | 729 | 0.084 | 0.094 | 0.010 |
| 6 | 6274 | 697 | 0.077 | 0.077 | 0.000 |
| 7 | 6221 | 750 | 0.078 | 0.079 | 0.000 |
| 8 | 6253 | 718 | 0.079 | 0.085 | 0.006 |
| 9 | 6272 | 699 | 0.079 | 0.075 | 0.005 |
| 10 | 6249 | 722 | 0.079 | 0.077 | 0.002 |
| Mean | 0.079 | 0.080 | 0.003 | ||
| Standard Deviation (SD) |
0.002 | 0.006 | 0.003 |
RMSE values ranged from 0.07 to 0.09 for the outcome variable. In addition, there is a very slight average difference between training and testing RMSE values in the model (Mean = 0.003, SD = 0.003), further corroborating the substantial relative prediction accuracy and predictive power of the approach used in this study.
Following the optimal ANN architecture configuration, a sensitivity analysis ensued to discern the comparative significance of each variable. Sensitivity analysis in the ANN model shows the normalized importance scores as percentages that represent the variation in the dependent variable with changes in the independent variables associated with it. The sensitivity analysis results are shown in Table 6. Table 6 shows the ten most important variables obtained through sensitivity analysis. The normalized ranks from the sensitivity analysis indicate that the segment length is the most important predictor of intensity, followed by "Road" factor (an index of AADT and surface width), "Education-Road", "Vehicle Possession-Vertical Curve", and "Income-Road". These results seem to be intuitively logical and reasonable.
5. Summary and Conclusions
The paper aimed to apply a novel hybrid technique to predict the intensity of traffic crashes, which is an amalgamated measure of the frequency of crashes and their severity level. To accomplish this objective, a dataset containing crash and roadway inventory data, climate data and socioeconomic census data for Ohio collector road segments was used. A hybrid PLS-SEM-ANN technique was employed to analyze the factors affecting the intensity of traffic crashes. Using SEM, the influence of socioeconomic variables as moderators on the impact of road and weather characteristics on crash intensity was assessed, and, using ANN, the nonlinear complex relationships between independent, moderating and dependent variables were captured. SEM results showed that "Horizontal Curve", "Road", "Speed Limit", "Segment Length", "Vertical Curve", and "Vehicle Possession" have significant linear impacts on crash intensity. Still, most of the moderating effects were insignificant. However, ANN results revealed that the interactions are among the most important predictors of crash predictors, suggesting that using ANN to enrich SEM analyses offers unique insights into model relationships that would otherwise remain undetected.
Combining these complementary approaches can provide a more holistic understanding of contributing factors to traffic crashes, allowing for better managerial decisions. The in-depth analyses resulting from the hybrid approaches described can lead to designing appropriate countermeasures to mitigate crash intensity. Although SEM-ANN modelling may be considered complex, the approach can potentially help safety professionals identify key crash factors, prioritize them, and allocate resources effectively, thereby enabling a more strategic approach to improving traffic safety. In so doing, it offers several benefits, including enhanced accuracy, a deeper understanding of non-linear relationships, greater robustness to outliers, and improved model interpretability through prioritized factor analysis. Nevertheless, there are some limitations to this research. First, this research was only conducted for a database from Ohio; hence, the findings themselves cannot be generalized for application elsewhere. To further extend the scope of the current study, future studies may involve cross-national or cross-cultural perspectives. Second, although several variables are considered in the current study, more comprehensive information would be helpful, for example, more information about neighborhoods, such as religion and ethnicity, driver sociodemographic and behavioral characteristics, and characteristics of the vehicles involved in crashes. Third, a hybrid framework can be developed using more advanced machine learning algorithms to enhance the investigation of factors associated with crashes and their severity.
CRediT authorship contribution statement
Mahsa Jafari: Conceptualization, Data curation, Investigation,
Table 6 ANN Sensitivity Analysis Results.
| Network Variables |
Normalized Importance | ||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | Normalized rank | |
| segment length | 100 | 100 | 100 | 100 | 100 | 100 | 91.8 | 100 | 100 | 100 | 1 |
| road | 46.5 | 29.2 | 32.2 | 47.3 | 48.3 | 41.4 | 21.9 | 94.9 | 64.1 | 13.7 | 0.44 |
| vehicle possession − spdlimt | 18.9 | 72.2 | 25.8 | 13.5 | 14.8 | 31.8 | 100 | 68.8 | 24.2 | 46.5 | 0.42 |
| vehicle possession − vertical curve | 10.9 | 21.5 | 53.1 | 17.2 | 16.6 | 14.2 | 47.7 | 49.2 | 48.1 | 70.2 | 0.35 |
| Age − road | 13.5 | 36.4 | 47.1 | 15.4 | 13.1 | 15.7 | 10.8 | 62.9 | 50.2 | 83.3 | 0.35 |
| education − road | 21.4 | 58.6 | 12.0 | 14.7 | 12.4 | 26.8 | 9.30 | 62.2 | 47.9 | 63.9 | 0.33 |
| income − road | 11.9 | 68.0 | 24.3 | 18.4 | 17.9 | 17.4 | 41.0 | 43.3 | 24.9 | 37.6 | 0.31 |
| vehicle possession − road | 24.5 | 41.0 | 17.2 | 16.6 | 35.4 | 13.2 | 44.9 | 36.8 | 53.6 | 12.8 | 0.29 |
| income | 13.2 | 21.1 | 17.3 | 23.3 | 34.1 | 26.2 | 7.80 | 67.3 | 26.2 | 43.8 | 0.28 |
| education- horizontal curve | 16.9 | 53.4 | 17.4 | 9.30 | 43.0 | 17.4 | 18.3 | 22.4 | 11.8 | 46.9 | 0.25 |
Methodology, Software, Writing – original draft. Bhagwant Persaud: Conceptualization, Funding acquisition, Supervision, Writing – review & editing.
Declaration of Generative AI and AI-assisted technologies in the writing process
No generative AI or AI-assisted technologies are used in this manuscript.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgment
The financial support provided by a discovery grant (Appl. ID RGPIN-2023-03787) from the Natural Sciences and Engineering Research Council of Canada (NSERC) is gratefully acknowledged, as is the provision of the data set by the University of North Carolina Highway Safety Research Center.
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi. org/10.1016/j.aap.2025.108076.
Data availability
Data will be made available on request.
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