Psychological well-being and multimodal predictors of operational safety in heavy-haul railway drivers

Abstract

BACKGROUND

Operational safety in heavy-haul railway systems is influenced not only by technical competence, but also by drivers’ psychological and physiological functioning. However, the relative contributions of psychological well-being, cognitive performance, and physiological indicators to real-world operational safety remain unclear.

AIM

To examine the associations of psychological well-being, cognitive task performance, and physiological indicators with operational safety among heavy-haul railway drivers.

METHODS

This observational study included 1117 operational records from 203 heavy-haul railway drivers. Psychological well-being was assessed using a multidimensional questionnaire covering mental fatigue, workload, self-efficacy, stress level, and emotional state. Cognitive performance was assessed using a rotating battery of computerized tasks, including Stroop, target tracking, ligature test, digit memory, BallSport, and Balloon tasks. A composite physiological indicator was derived from routine multimodal monitoring data. Operational safety was defined as full-score vs non-full-score performance. The overall psychological well-being score and other analytic predictors were entered into Poisson event-rate models with offset terms to estimate the associations of psychological well-being, cognitive task.

RESULTS

Higher overall psychological well-being was associated with a lower rate of non-full-score operational events at the driver level [incidence rate ratio (IRR) = 0.80, 95% confidence interval (CI): 0.65-0.99, P = 0.040]. In contrast, cognitive task indicators did not show stable independent associations with operational risk across task-specific models. The physiological indicator was not significantly associated with event rates overall (IRR = 0.90, 95%CI: 0.65-1.22, P = 0.510), but showed a significant protective association in the digit memory subsample (IRR = 0.26, 95%CI: 0.09-0.68, P = 0.012). Overall, operational safety appeared to be more consistently related to general psychological well-being than to isolated cognitive task performance, whereas the effect of physiological indicators may vary across cognitive load conditions.

CONCLUSION

Psychological well-being was a relatively stable protective correlate of operational safety among heavy-haul railway drivers, whereas individual cognitive task indicators showed limited independent explanatory value. Physiological indicators may have context-dependent relevance under specific cognitive load conditions. These findings support the value of multimodal safety assessment frameworks that prioritize psychological well-being while integrating cognitive and physiological information in a context-sensitive manner.

Key Words: Heavy-haul railway; Operational safety; Psychological health; Cognitive tasks; Multimodal assessment

Core Tip: This observational study examined whether psychological well-being, cognitive task performance, and physiological indicators were associated with operational safety in heavy-haul railway drivers. Using 1117 operational records from 203 drivers, we found that higher overall psychological well-being was associated with a lower rate of non-full-score operational events. Individual cognitive task indicators did not show stable independent associations, whereas physiological indicators showed a context-dependent protective association in the digit memory subsample. These findings support multimodal safety assessment frameworks that prioritize psychological well-being.

INTRODUCTION

Heavy-haul railway transportation is a cornerstone of large-scale energy and bulk commodity distribution in China, operating within a safety-critical system characterized by high workload, strong system coupling, and extremely low tolerance for error[1,2]. In this context, locomotive drivers occupy a central position in the human-machine-environment interaction chain and are required to sustain stable attention, judgment, and operational control over prolonged duty periods and under tightly constrained conditions[3,4]. Failures in this system carry substantial social and economic costs, extending beyond immediate operational disruption to broader supply-chain and public safety risks[1].

Contemporary railway human factors research increasingly emphasizes that major safety incidents rarely result from isolated technical failures or simple “driver error”. Instead, operational risk emerges from the interaction of individual states, organizational arrangements, and system design, ultimately manifesting at the level of human performance[5,6]. Accordingly, drivers’ psychological functioning, shaped by fatigue, stress, workload, and emotional regulation, has become a central concern in safety management. Empirical studies consistently show that fatigue and psychological workload are closely linked to impaired performance and elevated risk among train drivers, while acute stressors may influence behavior even when drivers are not fully aware of their effects[7].

Despite this growing recognition, important gaps remain in how driver well-being is conceptualized and measured in relation to operational safety. First, much of the existing literature relies on single-domain indicators, such as fatigue scales or isolated performance metrics, even though real-world safety is likely shaped by interacting psychological processes. From a well-being perspective, psychological health at work reflects a configuration of mental fatigue, perceived workload, self-efficacy, stress experience, and emotional state, rather than any single dimension in isolation[8,9]. Second, the predictive value of laboratory-based cognitive tasks for real-world safety outcomes remains contested. Although such tasks provide standardized measures of cognitive ability, their ecological validity in complex, dynamic operational environments is limited, and their effects may be overshadowed by more stable, context-general psychological states[10].

In response to these limitations, recent research has advocated multimodal approaches that integrate psychological, behavioral, and physiological indicators to improve risk detection and state assessment in safety-critical occupations[11]. Evidence from driving and fatigue monitoring suggests that combining subjective reports with behavioral and physiological signals can enhance sensitivity to risk states. However, how these different modalities jointly contribute to safety outcomes, and whether their effects are hierarchical or context-dependent, remains insufficiently understood, particularly in highly professionalized groups such as heavy-haul railway drivers.

Against this background, the present study examines operational safety among heavy-haul railway drivers using a multimodal framework that integrates psychological health, cognitive task performance, and physiological indicators. Operational performance is analyzed using an event-rate approach, treating non-perfect operations as risk events in line with safety management logic. The study addresses a focused theoretical question: In a population of well-trained drivers, are differences in safety outcomes better explained by task-specific cognitive abilities, or by more stable psychological health states together with context-dependent physiological conditions that generalize across operational contexts? By answering this question, the study aims to clarify the relative roles of psychological well-being, cognition, and physiology in safety prediction, and to inform the development of more effective, well-being-oriented risk monitoring systems in high-risk occupational settings.

MATERIALS AND METHODS

Participants

The study was conducted using data collected during routine operational monitoring, without any experimental manipulation or intervention. The research protocol was reviewed and approved by the Institutional Ethics Committee of City University of Macau, No. FHW-ER-2425-129, and all participants were informed that psychological, physiological, and operational data collected during routine work activities could be used for research purposes; written informed consent was obtained prior to their inclusion in the study. Participants were heavy-haul locomotive drivers employed by a large railway transportation company in China. The final analytical sample consisted of 203 drivers, who together contributed 1117 operational records during the study period, with each record corresponding to a completed duty cycle with valid operational performance data and matched psychological or behavioral assessments.

Because drivers were assessed repeatedly during routine operations, the data had a multilevel structure, with operational records nested within drivers. Of the 1117 operational records, 91.1% represented full-score performance, whereas 99 records (8.9%) were classified as non-full-score events, indicating the occurrence of at least one operational deviation during the evaluated duty.

Psychological well-being measures were available for all operational records. Cognitive functioning was assessed using a set of computerized tasks administered on a rotating basis during routine operations; consequently, task-specific sample sizes varied. Across tasks, the number of drivers providing valid data ranged from 81 to 142, with corresponding numbers of operational records ranging from 150 to 241. All participants were active-duty locomotive drivers at the time of data collection. Operational performance data were obtained from the company’s standardized assessment system. The study was based exclusively on data collected in routine work settings, without experimental manipulation.

Measures

Psychological well-being questionnaire: Psychological well-being was assessed using a brief pre-driving screening instrument covering five domains: Mental fatigue, workload, stress level, self-efficacy, and emotional state. Scores were derived according to the predefined operational scoring protocol used in the monitoring system. Because the item responses had different theoretical score ranges, the scoring pipeline applied min-max normalization before domain-level and overall score aggregation. The overall psychological well-being score was used as the primary psychological predictor in the main analyses. Additional information on domain composition and supplementary measurement details is provided in the Supplementary Table 1. Internal consistency estimates for the multi-item domains are presented in Supplementary Table 2.

Cognitive tasks: Cognitive functioning was assessed using a battery of computerized tasks administered on a rotating basis during routine operations. Each task targeted a distinct cognitive domain, and task-specific performance indicators were extracted for analysis. Because not all tasks were administered on every measurement occasion, sample sizes varied across tasks. The Stroop task assessed executive control and inhibitory processing. Two indicators were extracted: Reaction time (in milliseconds), reflecting processing speed, and accuracy rate, reflecting response correctness.

The target tracking task assessed sustained attention and visuospatial monitoring ability. Performance was indexed by accuracy rate, reflecting the proportion of correctly tracked targets. The Ligature test assessed visuomotor coordination and cognitive flexibility. Performance was indexed by total completion time (in seconds), with shorter times indicating better task performance.

The digit memory task assessed short-term working memory capacity. Performance was indexed by the number of digits correctly recalled (count), with higher scores indicating better working memory. The BallSport task assessed speed perception ability. Performance was indexed by reaction time (in seconds), with shorter times indicating better perceptual processing speed.

The Balloon task assessed risk-taking behavior and response persistence. Performance was indexed by the number of unexploded balloons (count), with higher values indicating more conservative and controlled task performance. The cognitive task performance was summarized using task-specific indicators reflecting accuracy, reaction time, or count-based outcomes, depending on the task. Descriptive statistics for each task are presented separately due to differences in measurement units and administration frequency.

Physiological indicator: The physiological indicator used in this study refers to a composite physiological-behavioral index developed within the railway industry to assess drivers’ physiological load and state stability during duty periods. Rather than representing a single biological mechanism, this indicator was designed to capture drivers’ overall physiological readiness and regulatory capacity in operational contexts.

Physiological data were collected through an integrated multimodal assessment system deployed in routine operational settings. The indicator integrates multiple signal sources, including cardiorespiratory activity, fatigue-related physiological features, patterns of alertness fluctuation, and operational behavioral metrics. Specifically, the physiological indicator incorporates: (1) Cardiorespiratory features, such as heart rate and heart rate variability, which serve as indirect indicators of autonomic nervous system activity and physiological arousal; (2) Fatigue-related physiological indicators, reflecting cumulative physiological strain and reduced recovery capacity; and (3) Behavioral and psychophysiological features, extracted from operational behavior patterns and affective computing-based analyses of facial expressions, speech prosody, and eye-movement dynamics, which reflect attentional engagement, emotional arousal, and fatigue-related behavioral signatures.

All physiological and behavioral features were temporally aligned and standardized prior to analysis. Feature integration was implemented using a multimodal fusion framework, in which the relative contribution of different signal sources was jointly modeled to generate an overall physiological state score. When multiple physiological records were available for the same driver within a given day, their mean value was used in subsequent analyses.

Higher values on this physiological indicator indicate better physiological regulation, lower fatigue levels, and greater readiness for safe operation, whereas lower values reflect increased physiological strain or potential fatigue-related risk. Similar composite physiological-behavioral indicators have been widely applied in safety-critical industries, such as aviation and railway operations, to support monitoring of alertness, fatigue, and operational risk[12-14]. Moreover, prior research on multimodal integration of physiological and behavioral data has demonstrated the feasibility of using composite indices to estimate individuals’ psychophysiological states based on multi-source signals[15]. Accordingly, the physiological indicator adopted in the present study is consistent with established industry practices and current advances in multimodal physiological monitoring research.

Operational performance: Operational performance was measured using standardized operational scores recorded by the railway company. For inferential analyses, performance was operationalized as a binary indicator (full score vs non-full score), reflecting whether any operational deviation occurred during the evaluated duty cycle. In heavy-haul railway operations, performance evaluation follows a zero-tolerance safety logic: Any deviation from standard operating procedures, regardless of its magnitude, is treated as an operational deviation. Given the extreme train mass, long braking distances, and high systemic coupling, even minor deviations may substantially increase safety risks. Accordingly, a non-full score does not merely indicate lower performance, but represents the presence of a safety-relevant operational deviation. Distinguishing between full-score and non-full-score performance, therefore, constitutes an industry-consistent and safety-relevant categorization, rather than a coarse simplification of performance variability.

Statistical analysis

Data were collected between April and November as part of routine operational monitoring procedures. Statistical analyses were conducted in three steps. First, the main variables, psychological well-being, the physiological indicator, and the six cognitive task measures, were summarized using descriptive statistics to characterize their distributions. Continuous variables were reported as mean ± SD, with medians (interquartile ranges) additionally provided when distributions showed noticeable skewness. Categorical variables were expressed as n (%). For the psychological well-being measure, scores were derived according to the predefined operational scoring protocol. Because the questionnaire items had different theoretical score ranges, min-max normalization was applied before domain-level and overall score aggregation. Operational safety outcomes were expressed as event counts (non-full-score operations), with the total number of operations included as an offset to account for differences in work exposure across drivers.

Next, to examine how psychological well-being relates to operational safety at the driver level, we fitted Poisson event-rate models. For each driver, the number of non-full-score events was treated as the outcome, and the log of the total number of operations was included as an offset. Within each task-specific subsample, when a driver contributed multiple records, continuous predictors were aggregated at the driver level using their mean values. This allowed us to estimate whether drivers with lower psychological well-being or poorer physiological status showed a higher rate of performance deviations after adjusting for differences in work exposure.

Finally, to test whether these associations held across different cognitive profiles, we conducted a set of joint Poisson models within each cognitive task subsample. Each model included the psychological well-being index, the corresponding cognitive task score, and the physiological indicator as predictors, again using the number of non-full-score events as the outcome and total operations as an offset. These models were used to evaluate the unique and combined contributions of psychological, cognitive, and physiological factors to operational performance. Because the cognitive tasks were administered in rotating subsamples of unequal size, these task-specific analyses were treated as exploratory. For the overall psychological well-being term, a light false-discovery-rate correction was additionally applied across the task-specific models. As a supplementary robustness check, we also fitted record-level generalized linear mixed models with driver-specific random intercepts. All statistical analyses were performed using R (Version 4.4.3; R Foundation for Statistical Computing, Vienna, Austria) within the RStudio integrated development environment (Posit Software, PBC, Boston, MA, United States). The threshold for statistical significance was set at P < 0.05.

RESULTS

Descriptive statistics and group comparisons

A total of 1117 operational records from 203 drivers were included in the analyses, of which 91.1% achieved full-score performance. Descriptive statistics for psychological well-being dimensions, the overall psychological well-being composite, and the physiological indicator are presented in Table 1. Among the psychological well-being dimensions, self-efficacy showed the highest mean value (7.18 ± 1.89), followed by mental fatigue (6.81 ± 2.14). In contrast, workload (3.09 ± 1.93) and stress level (3.92 ± 2.41) exhibited lower mean values. The score of overall psychological well-being, calculated as an equally weighted composite of the five dimensions was 5.38 ± 0.95. The composite physiological indicator demonstrated moderate variability across records (4.04 ± 2.20).

Table 1 Descriptive statistics of psychological well-being dimensions and physiological indicator.

Variable	mean ± SD1
Mental fatigue	6.81 ± 2.14
Workload	3.09 ± 1.93
Stress level	3.92 ± 2.41
Self-efficacy	7.18 ± 1.89
Emotional state	5.89 ± 1.90
Overall psychological well-being	5.38 ± 0.95
Physiological indicator	4.04 ± 2.20

¹The overall psychological well-being score was calculated as the equally weighted mean of the five psychological dimensions (each weighted at 0.20). Higher scores indicate better psychological functioning after score transformation.

Baseline performance across cognitive tasks showed a high degree of stability (Table 2). Group comparisons between full-score and non-full-score records were conducted using Welch’s t tests or Mann-Whitney U tests as appropriate, and indicated that nearly all cognitive task measures did not differ significantly between the full-score and non-full-score groups (all P > 0.05). Similarly, individual psychological dimensions showed no substantial group differences. However, the overall psychological well-being composite score was significantly lower in the non-full-score group (M = 5.16) compared with the full-score group (M = 5.40, P = 0.03).

Table 2 Descriptive statistics of cognitive task performance.

Task	Trials, n	mean ± SD1
Stroop-accuracy	241	0.801 ± 0.141
Stroop-time (milliseconds)	241	1182 ± 391
Target tracking	196	0.740 ± 0.216
Ligature test (seconds)	181	67.600 ± 20.800
Digit memory	176	6.670 ± 1.830
Ballsport (seconds)	173	1.520 ± 0.810
Balloon	150	10.200 ± 3.490

¹Reaction time for the Stroop task is reported in milliseconds, whereas reaction time for the BallSport task and completion time for the Ligature test are reported in seconds. Accuracy-based indicators are expressed as proportions, and count-based indicators represent raw counts.

Poisson event-rate models

At the driver level, 203 drivers contributed 1117 operational records, of which 99 were non-full-score events (event rate = 0.089 per operation). In a Poisson event-rate model with the logarithm of total operations as an offset, higher psychological well-being was significantly associated with a lower rate of non-full-score events [incidence rate ratio (IRR) = 0.80, 95% confidence interval (CI): 0.65-0.99, P = 0.040]. In contrast, the physiological indicator was not significantly related to event rates at the driver level (IRR = 0.90, 95%CI: 0.65-1.22, P = 0.510). No evidence of overdispersion was observed (dispersion ratio = 1.05, P = 0.295), supporting the adequacy of the Poisson event-rate specification.

Joint effects of psychological well-being, cognitive tasks, and physiological indicators

To examine whether psychological well-being remained associated with operational risk when cognitive performance and physiological status were considered jointly, a series of Poisson event-rate models was estimated. Each model included psychological well-being, one cognitive task, and a physiological indicator, and was fitted to the subsample of drivers who completed the corresponding cognitive task.

Across the exploratory task-specific models, the overall psychological well-being score showed a protective association with non-full-score events, with statistical significance retained only in the Balloon-task subsample after false-discovery-rate correction (Supplementary Table 3). As a supplementary robustness analysis, record-level generalized linear mixed models with driver-specific random intercepts showed a broadly similar pattern of results (Supplementary Table 4). No other task-specific associations remained significant after correction. Although these associations did not reach statistical significance in most task-specific models, the direction of the estimates was generally protective across subsamples (Table 3). Specifically, higher psychological well-being was associated with a lower rate of non-full-score operational events across diverse cognitive contexts.

Table 3 Joint effects of psychological well-being, individual cognitive tasks, and physiological indicators on the risk of non-full-score operational events (Poisson event-rate models).

Cognitive task1	Drivers (n)	Non-full events	Event rate	Psychological well-being IRR (95%CI)	P value	Cognitive task IRR (95%CI)	P value	physiological IRR (95%CI)	P value
Stroop-accuracy	142	23	0.095	0.88 (0.56-1.38)	0.575	1.06 (0.67-1.84)	0.807	1.19 (0.75-1.77)	0.422
Stroop-time	142	23	0.095	0.89 (0.57-1.40)	0.604	1.01 (0.63-1.60)	0.963	1.20 (0.75-1.80)	0.411
Target tracking	105	20	0.102	0.88 (0.54-1.45)	0.602	1.05 (0.68-1.68)	0.845	0.66 (0.38-1.13)	0.139
Ligature test	100	18	0.099	0.68 (0.40-1.15)	0.160	0.90 (0.56-1.55)	0.688	0.89 (0.47-1.47)	0.679
Digit memory	99	14	0.080	0.90 (0.53-1.48)	0.684	0.78 (0.44-1.40)	0.409	0.26 (0.09-0.68)	0.012
BallSport	94	15	0.087	0.73 (0.38-1.34)	0.324	1.11 (0.67-2.29)	0.731	1.07 (0.60-1.82)	0.797
Balloon	81	9	0.060	0.54 (0.27-1.09)	0.083	1.22 (0.59-3.38)	0.646	1.20 (0.60-2.26)	0.580

¹Each model includes psychological well-being, one cognitive task, and physiological indicator and is fitted on the subsample of drivers who completed the corresponding cognitive task. The outcome is the number of non-full-score events, with the logarithm of total operations used as an offset. Incidence rate ratio < 1 indicates a reduced rate of non-full-score events. All models were estimated using Poisson regression with an offset for the logarithm of total operations. For time-based cognitive measures (e.g., Stroop time, BallSport, Ligature test), scores were reverse-coded so that higher values indicate better performance.

IRR: Incidence rate ratio; CI: Confidence interval.

In contrast, individual cognitive task performance did not show a consistent independent association with event rates at the driver level. The IRRs for cognitive tasks varied in direction and were not statistically significant across models, suggesting that task-specific cognitive performance did not independently account for variations in operational risk when psychological well-being was included. The physiological indicator likewise did not show consistent independent effects across tasks. However, a significant protective association emerged in the digit memory task subsample, where higher physiological scores were associated with a substantially lower event rate. This finding suggests that physiological state may play a buffering role under specific cognitive load conditions, rather than serving as a general risk predictor.

DISCUSSION

Overview of key findings

The present study investigated whether operational safety among heavy-haul railway drivers is better explained by task-specific cognitive abilities or by more general psychological and physiological states. Three core findings emerged. First, overall psychological well-being showed a stable and protective association with operational safety, such that drivers with better psychological health exhibited lower rates of non-perfect operational events. Second, performance on individual cognitive tasks did not independently predict operational risk when psychological well-being was taken into account. Third, physiological indicators demonstrated context-dependent effects, becoming predictive only under specific cognitive load conditions, most notably in the digit memory task. Together, these findings suggest that operational safety in this highly professionalized context is shaped less by isolated cognitive skills and more by cross-context psychological regulation, with physiological state exerting conditional influence under elevated cognitive demands.

Psychological well-being as a cross-context predictor of operational safety

The most consistent result of the present study is the protective role of psychological well-being across operational contexts. Higher levels of overall psychological health were associated with lower rates of non-perfect operations, both at the driver level and across multiple task-specific subsamples. Psychological well-being in this study represents a composite of mental fatigue, perceived workload, stress, emotional state, and self-efficacy[16]. Rather than capturing momentary performance capacity, this construct reflects drivers’ sustained ability to regulate attention, emotion, and effort over prolonged duty periods[17]. In safety-critical environments such as heavy-haul railway operations, performance failures are rarely attributable to isolated cognitive lapses; instead, they emerge from cumulative strain, reduced self-regulatory capacity, and diminished resilience over time[13,18].

This interpretation is consistent with human factors theories emphasizing system-level and cross-temporal influences on safety performance. From a Swiss cheese perspective, psychological well-being functions as a higher-order defensive layer that modulates how effectively lower-level cognitive and behavioral processes are deployed[2,6]. Drivers with better psychological health are more likely to maintain stable vigilance, detect early signs of fatigue or overload, and adaptively regulate their behavior in response to operational demands[4,19].

The findings also align with the broaden-and-build theory of positive psychological functioning, which posits that positive emotional and psychological states broaden attentional scope and build enduring regulatory resources[20]. In the present data, psychological well-being did not enhance performance on any single task per se; rather, it appeared to reduce the likelihood of safety-relevant deviations across diverse contexts, supporting its role as a general regulatory resource rather than a task-bound enhancer[21].

Why do individual cognitive tasks not independently predict safety outcomes

Contrary to expectations derived from traditional cognitive performance models, none of the six cognitive tasks independently predicted operational risk when psychological well-being was included in the models. This pattern does not imply that cognitive abilities are irrelevant for safe railway operation, but rather that their predictive value is constrained in this context.

First, laboratory-based cognitive tasks assess isolated cognitive components under controlled and time-limited conditions, whereas real-world railway operation requires sustained integration of attention, perception, decision-making, and emotional regulation under fatigue and stress[4,18]. As such, the ecological validity of single-task cognitive measures for predicting long-term operational safety is inherently limited[10].

Second, heavy-haul railway drivers constitute a highly selected and extensively trained professional group. Cognitive abilities in this population likely operate as qualification thresholds rather than differentiating factors, resulting in restricted variance. Well-established principles from personnel psychology indicate that under such conditions, even valid cognitive measures will show reduced predictive power for performance outcomes[22].

More fundamentally, cognitive capacity appears to be a necessary but not sufficient condition for operational safety. The present results suggest that what matters most is not how well drivers perform on discrete cognitive tasks, but whether they can reliably mobilize their cognitive resources under conditions of fatigue, emotional fluctuation, and sustained workload[13,18]. Psychological well-being, rather than any isolated cognitive skill, may play a central role in shaping this mobilization process.

Context-dependent role of physiological indicators

An intriguing finding of the present study was that the physiological indicator did not show a stable overall association with operational safety, but emerged as a significant protective correlate in the digit memory subsample. This pattern suggests that the relevance of physiological functioning may be contingent on cognitive load conditions rather than uniform across all task contexts. Digit memory performance relies heavily on working memory maintenance, attentional control, and short-term information updating. Under such conditions, physiological states associated with fatigue regulation, arousal stability, or reduced internal strain may become more directly linked to behavioral consistency and error prevention. In contrast, under lower or different task demands, experienced drivers may compensate for transient physiological fluctuations through routine, skill, or strategic effort allocation, thereby attenuating the observable effect of physiological markers on operational outcomes.

This interpretation is broadly consistent with resource-based accounts of performance and fatigue, which emphasize that the behavioral consequences of fatigue and stress become more pronounced when task demands are high and regulatory resources are strained[13,19]. Given that digit memory performance depends heavily on working memory maintenance and attentional control[23], physiological vulnerability may be more likely to translate into observable performance instability under this type of cognitively demanding condition. It also aligns with prior fatigue and driving research showing that physiological indicators are more predictive of unsafe behavior during high-demand operational phases[24]. In this sense, physiological indicators may not function as universally stable predictors of safety performance, but rather as context-sensitive markers whose relevance increases when cognitive load is high. The present findings therefore support a conditional rather than global interpretation of physiological effects in multimodal operational safety assessment.

Toward a hierarchical multimodal framework of operational safety

Taken together, the present findings support a hierarchical multimodal framework for understanding operational safety in heavy-haul railway contexts. Psychological well-being emerged as the most stable, cross-context correlate of operational safety, shaping how drivers sustain performance over time. Cognitive abilities, while essential for baseline competence, showed limited discriminative power within a highly trained population, consistent with range restriction effects[22]. Physiological indicators contributed in a task-dependent manner, becoming salient under conditions of heightened cognitive load.

From an applied perspective, the present findings suggest that psychological well-being monitoring may serve as a foundational component of safety management in heavy-haul railway operations. Because overall psychological well-being showed a more stable association with operational safety than isolated cognitive task indicators, routine well-being assessment may be useful for early identification of drivers who could be at elevated operational risk over time. At the same time, the limited and context-dependent effects of cognitive and physiological indicators suggest that these measures may be better used as supplementary tools in targeted monitoring rather than as universal standalone screening criteria.

In practical terms, railway management may consider a tiered assessment framework. At the first level, regular psychological well-being monitoring could be incorporated into routine occupational health management. At the second level, cognitive tasks and physiological indicators could be deployed for more focused assessment in situations involving high workload, fatigue concern, or performance instability. In addition, the results support preventive interventions aimed at improving drivers’ psychological well-being, such as stress management, fatigue recovery support, mental health promotion, and organizational measures to reduce chronic workload burden. Such an approach may be more useful than relying solely on isolated performance tests when attempting to improve operational safety in safety-critical railway settings.

This hierarchy suggests that effective safety monitoring should move beyond single-domain indicators toward integrated systems that prioritize psychological well-being while incorporating cognitive and physiological information in relation to operational context[6,11]. Such an approach may improve risk detection and support more targeted, well-being-oriented safety interventions in safety-critical industries.

Limitations

Several limitations should be noted. First, the sample was drawn from a specific heavy-haul railway operational context, which may limit the generalizability of the findings to other railway systems, transportation settings, or safety-critical industries. Replication in other occupational and organizational contexts is needed before broader conclusions can be drawn. Second, psychological well-being was assessed using self-report measures and may therefore be subject to reporting bias, social desirability, or transient response tendencies. Future studies could strengthen the evidence base by combining self-report data with clinician-rated, behavioral, or longitudinal mental health indicators. Third, because the study was observational, the reported associations should not be interpreted as causal. Finally, the task-specific cognitive analyses were based on rotating subsamples of unequal size, and several subsamples included relatively few non-full-score events. These features may have reduced statistical precision and limited the stability of some task-specific estimates; accordingly, these findings, particularly those from the multimodal joint models, should be interpreted cautiously and regarded as exploratory.

CONCLUSION

Psychological well-being was the most stable factor associated with operational safety among heavy-haul railway drivers, whereas individual cognitive task indicators showed limited independent value in explaining non-full-score operational events. Physiological indicators appeared to have context-dependent relevance, becoming more salient under conditions of higher cognitive load. These findings support a hierarchical multimodal framework in which psychological well-being serves as a foundational component of safety monitoring, while cognitive and physiological indicators provide supplementary information in specific operational contexts. Such an approach may help improve risk detection and support more targeted, well-being-oriented safety management in safety-critical railway settings.

ACKNOWLEDGEMENTS

The authors thank the participating heavy-haul railway drivers and the supporting staff of the collaborating railway company for their cooperation and assistance in data collection and operational coordination.