This part of the review aims to reduce the start-up burden of data collection and descriptive analytics for statistical modeling and route optimization of risk associated with motor vehicles. From a data-driven bibliometric analysis, we show that the literature is divided into two disparate research streams: (a) predictive or explanatory models that attempt to understand and quantify crash risk based on different driving conditions, and (b) optimization techniques that focus on minimizing crash risk through route/path-selection and rest-break scheduling. Translation of research outcomes between these two streams is limited. To overcome this issue, we present publicly available high-quality data sources (different study designs, outcome variables, and predictor variables) and descriptive analytic techniques (data summarization, visualization, and dimension reduction) that can be used to achieve safer-routing and provide code to facilitate data collection/exploration by practitioners/researchers. Then, we review the statistical and machine learning models used for crash risk modeling. We show that (near) real-time crash risk is rarely considered, which might explain why the optimization models (reviewed in Part 2) have not capitalized on the research outcomes from the first stream.

Job rotation is an organizational strategy that can be used, in part, to reduce occupational exposure to physical risk factors associated with work-related musculoskeletal disorders (MSDs). Recent studies, however, suggest that job rotation schedules may increase the overall risk of injury to workers included in the rotation scheme. We describe a novel optimization framework evaluating the effectiveness of a job rotation scheme using the fatigue failure model of MSD development and a case study with real injury data. Results suggest that the effect of job rotation is highly-dependent on the composition of the job pool, and inclusion of jobs with higher risk results in a drastic decrease in the effectiveness of rotation for reducing overall worker risk. The study highlights that in cases when high-risk jobs are present, job redesign of those high risk tasks should be the primary focus of intervention efforts rather than job rotation. Practitioner Summary: Job rotation is often used in industry as a method to “balance” physical demands experienced by workers to reduce musculoskeletal disorder (MSD) risk. This article examines the efficacy of reducing MSDs through job rotation using numerical simulation of job rotation strategies and utilizing the fatigue failure model of MSD development.

In the first part of the review, we observed that there exists a significant gap between the predictive and prescriptive models pertaining to crash risk prediction and minimization, respectively. In this part, we review and categorize the optimization/ prescriptive analytic models that focus on minimizing crash risk. Although the majority of works in this segment of the literature are related to the hazardous materials (hazmat) trucking problems, we show that (with some exceptions) many can also be utilized in non-hazmat scenarios. In an effort to highlight the effect of crash risk prediction model on the accumulated risk obtained from the prescriptive model, we present a simulated example where we utilize four risk indicators (obtained from logistic regression, Poisson regression, XGBoost, and neural network) in the k-shortest path algorithm. From our example, we demonstrate two major designed takeaways: (a) the shortest path may not always result in the lowest crash risk, and (b) a similarity in overall predictive performance may not always translate to similar outcomes from the prescriptive models. Based on the review and example, we highlight several avenues for future research.

Quality in the trucking industry involves several facets, including on-time performance and safety. In the largest naturalistic driving study to-date, with 496 drivers and 13 M miles driven, we address two safety questions: (a) does the occurrence of safety critical events increase during a driving shift? and (b) what is the effect of rest breaks on the incidence of those events? To address these two questions, we adopt point process models, commonly used to assess the reliability of repairable systems, to model the occurrence/likelihood of safety critical events. To account for driver differences, driver-level random effects were also assumed. Our results show that: (a) the intensity for hard brakes decreases throughout a shift, (b) rest breaks reduce the likelihood of activation of the automated collision mitigation system, and (c) there is a fair amount of variability among drivers. Given that a hard brake (a less severe safety critical event) is more common in the beginning of the shift, it can potentially be explained by an increased likelihood of being in a local/city road and/or increased likelihood of aggressive driving behavior early in a driver’s shift. Furthermore, we quantified the impact of rest breaks in reducing engagement of the more severe automated collision mitigation system, providing data-driven evidence on the importance of rest-break scheduling for trucking safety. Properties of the approach were also investigated through a simulation study, where we examined the consequences of an incorrect specification of the Bayesian priors.

With more and more data related to driving, traffic, and road conditions becoming available, there has been renewed interest in predictive modeling of traffic incident risk and corresponding risk factors. New machine learning approaches in particular have recently been proposed, with the goal of forecasting the occurrence of either actual incidents or their surrogates, or estimating driving risk over specific time intervals, road segments, or both. At the same time, as evidenced by our review, prescriptive modeling literature (e.g., routing or truck scheduling) has yet to capitalize on these advancements. Indeed, research into risk-aware modeling for driving is almost entirely focused on hazardous materials transportation (with a very distinct risk profile) and frequently assumes a fixed incident risk per mile driven. We propose a framework for developing data-driven prescriptive optimization models with risk criteria for traditional trucking applications. This approach is combined with a recently developed machine learning model to predict driving risk over a medium-term time horizon (the next 20 min to an hour of driving), resulting in a biobjective shortest path problem. We further propose a solution approach based on the k-shortest path algorithm and illustrate how this can be employed.

Patient satisfaction is critical to a health organization’s sustainability, making patient feedback an important source of insight for improving healthcare delivery. This study analyzes 31,054 patient responses across seven healthcare units to identify factors that influence patient satisfaction and to examine how satisfaction is reflected in qualitative comments across diverse care settings. Deep learning methods were employed to extract features related to sentiment and topics from patient comments. Linear regressions were used to evaluate the explanatory power of both quantitative ratings of healthcare service attributes and features extracted from qualitative comments, accounting for unit-specific healthcare settings. Our results reveal that trust and communication factors are the strongest predictors of patient satisfaction. However, the explanatory power of quantitatively rated survey items shows clear variations across units, suggesting that standardized survey questions inadequately capture patient experience in specific contexts. Text analysis uncovered important unit-specific priorities, such as prescription management in Medical Practice and result efficiency in OP Lab, that were absent from the rated survey items. Patients who mentioned topics not covered in the rated survey items tended to report lower satisfaction scores, indicating potential service gaps. The integration of quantitative ratings and qualitative comments in our analysis significantly improved explanatory power in six of the seven units. Based on the identified key determinants of patient satisfaction and emerging concerns from patient comments, we propose a tiered approach for healthcare practitioners to leverage both forms of feedback for more targeted, emotionally responsive, and context-aware improvements in patient care. As healthcare delivery continues to evolve, our findings also underscore the value of flexible, multi-modal feedback strategies—supported by an adaptive analytical framework—that can inform future research and system-level responses to the diverse and shifting expectations of patients.