Corporate Social Responsibility (CSR) has been an articulated practice for over 7 decades. Still, most corporations lack an integrated framework to develop a strategic, balanced, and effective approach to achieving excellence in CSR. Considering the world’s critical situation during the COVID-19 pandemic, such a framework is even more crucial now. We suggest subsuming CRS categories under Sustainable Development Goals (SDGs) be used and that they subsume CSR categories since SDGs are a comprehensive agenda designed for the whole planet. This study presents a new CSR drivers model and a novel comprehensive CSR model. Then, it highlights the advantages of integrating CSR and SDGs in a new framework. The proposed framework benefits from both CSR and SDGs, addresses current and future needs, and offers a better roadmap with more measurable outcomes.

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.

Explaining complex algorithms and models has recently received growing attention in various domains to support informed decisions. Ranking functions are widely used for almost every form of human activity to enable effective decision-making processes. Hence, explaining ranking indicators and their importance are essential properties to enhance performance. Local explanation techniques have recently become a prominent way to interpret individual predictions of machine learning models. However, there has been limited investigation into explaining competitive rankings. This work proposes a hierarchical ranking explanation framework to capture local explanations for competitive rankings by defining a proper neighborhood construction approach. We explore various explanation techniques to identify the local contribution of ranking indicators based on the position of an instance in the ranking as well as the size of the neighborhood around the instance of interest. We evaluate the generated explanations for the Times Higher Education university ranking dataset as a benchmark of competitive ranking. The results reveal insights for a wide range of instances in the ranking list and indicate the importance of local explanations for competitive rankings.

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.

Traffic crashes and congestion generate high social and economic costs, yet traditional traffic monitoring methods, such as police patrols, fixed cameras, and helicopters, are costly, labor-intensive, and limited in spatial coverage. This paper presents a novel Drone Routing and Scheduling with Flexible Multiple Visits (DRSFMV) framework, an optimization model for planning drone-based highway monitoring under realistic operational constraints, including battery limits, variable monitoring durations, recharging at a depot, and target-specific inter-visit time limits. A mixed-integer nonlinear programming (MINLP) model and a linearized version (MILP) are presented to solve the problem. Due to the NP-hard nature of the underlying problem structure, a heuristic solver, Hexaly, is also used. A case study using real traffic census data from three Southern California counties tests the models across various network sizes and configurations. The MILP solves small and medium instances efficiently, and Hexaly produces high-quality solutions for large-scale networks. Results show clear trade-offs between drone availability and time-slot flexibility, and demonstrate that stricter revisit constraints raise operational cost.