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The fast and efficient exchange of information is undeniably a competitive advantage in business. And in a fluctuating economic environment, it’s paramount. Unfortunately, in the rush to adapt to market needs, many organizations sacrifice effective communication. As priorities shift, executives may impart only the immediate goals and fail to share the rationale and strategies behind them. They may be so focused on results, they neglect to praise and reward their staff. Most of all, managers may forget that communication is a two‐way exchange – one part dialogue, one part listening.

Employee turnover at Alberto‐Culver North America, which was running at more than twice the industry average, has been halved over the past seven years and the company’s ability to attract top people from other firms has increased. Lavin Bernick describes how the turnaround has been achieved by focussing on organizational culture at the $2.25 billion manufacturer and marketer of personal‐care, speciality grocery and household products, which counts VO5 shampoo and the Sally Beauty Company among its stable.

Nobody concerned with political economy can neglect the history of economic doctrines. Structural changes in the economy and society influence economic thinking and, conversely, innovative thought structures and attitudes have almost always forced economic institutions and modes of behaviour to adjust. We learn from the history of economic doctrines how a particular theory emerged and whether, and in which environment, it could take root. We can see how a school evolves out of a common methodological perception and similar techniques of analysis, and how it has to establish itself. The interaction between unresolved problems on the one hand, and the search for better solutions or explanations on the other, leads to a change in paradigma and to the formation of new lines of reasoning. As long as the real world is subject to progress and change scientific search for explanation must out of necessity continue.

Looks at the first 100 years of Italian cinema examining its role in Italy’s recent history. Provides a bibliography of major film directors, Italian cinema sources, reference works, histories, themes, theory and criticism and articles in journals.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.

On-ramp merging areas are typical bottlenecks in the freeway network since merging on-ramp vehicles may cause intensive disturbances on the mainline traffic flow and lead to various negative impacts on traffic efficiency and safety. The connected and autonomous vehicles (CAVs), with their capabilities of real-time communication and precise motion control, hold a great potential to facilitate ramp merging operation through enhanced coordination strategies. This paper aims to present a comprehensive review of the existing ramp merging strategies leveraging CAVs, focusing on the latest trends and developments in the research field.

The review comprehensively covers 44 papers recently published in leading transportation journals. Based on the application context, control strategies are categorized into three categories: merging into sing-lane freeways with total CAVs, merging into sing-lane freeways with mixed traffic flows and merging into multilane freeways.

Relevant literature is reviewed regarding the required technologies, control decision level, applied methods and impacts on traffic performance. More importantly, the authors identify the existing research gaps and provide insightful discussions on the potential and promising directions for future research based on the review, which facilitates further advancement in this research topic.

Many strategies based on the communication and automation capabilities of CAVs have been developed over the past decades, devoted to facilitating the merging/lane-changing maneuvers at freeway on-ramps. Despite the significant progress made, an up-to-date review covering these latest developments is missing to the authors’ best knowledge. This paper conducts a thorough review of the cooperation/coordination strategies that facilitate freeway on-ramp merging using CAVs, focusing on the latest developments in this field. Based on the review, the authors identify the existing research gaps in CAV ramp merging and discuss the potential and promising future research directions to address the gaps.

The purpose of this paper is to investigate the relationship between employee retention and job security and the impact of retention factors on the job security of nurses in public hospitals in South Africa. The retention of nurses is essential in public hospitals in South Africa. It is therefore critical that retention strategies are primed to ensure the job security of nurses.

The study is quantitative in nature, adopts a cross-sectional approach and is set within a positivist research paradigm. Pre-established questionnaires were used to collect data. Non-probability sampling was used to select a convenience sample. Questionnaires were distributed to three public hospitals in Johannesburg and 202 responses were received. The psychometric properties of the questionnaire were established through validity and reliability. Inferential and descriptive statistics were deployed to analyse data.

The study established that there is a relationship between retention factors and job security. The study further identified retention factors as predictors of job security and noted that training and development was the strongest predictor of job security amongst nurses. In addition, the study contributes towards research on retention practices of nurses from a South African perspective.

The study recommendations are diagrammatically represented. If implemented by human resource practitioners and nursing management, they are likely to enhance job security.

The study provides insights on the retention of nursing professionals in public hospitals in Gauteng and identifies retention factors which contribute most towards job security.

This paper aims to present a linear mathematical framework for modeling and optimizing road transport infrastructure. The framework assesses and optimizes performance of existing transport facility rather than relying on building new roads for the ever-increasing travel demand.

The mathematical framework is built upon a traffic model called Cell Transmission Model (CTM). CTM describes the relationship and evolution of traffic flow and concentration over space and time. The model is parsimonious and accurate in predicting traffic dynamics. More importantly, the traffic flow model is piecewise linear with which the corresponding transport facility optimization problem can be formulated as a Linear Programming (LP) problem and solved by established solution algorithm for global optimality.

We select a section on England Motorway M25 as a case study. With traffic data, we first calibrate the CTM, and we are able to produce traffic estimation with a reasonable error rate of 12 per cent. The corresponding LP then seeks an optimal ramp metering strategy that minimizes the delay on the motorway. It is shown that an optimal and practical strategy can be derived which reduces the motorway delay by 10 per cent without significantly hurting the surrounding connectors.

Instead of the tedious microscopic models used by many traditional tools, the underlying CTM is parsimonious and reliable. The tools developed herein are based upon plausible traffic theory and will be accessible for a wide range of users. The LP formulation can be easily implemented and solved for optimal and practical control strategies for real-world transport networks by using existing computer software (CPLEX) within reasonable computational time. The present work will certainly contribute to the sustainable development of transport facility.

Metropolitan areas suffer from frequent road traffic congestion not only during peak hours but also during off-peak periods. Different machine learning methods have been used in travel time prediction, however, such machine learning methods practically face the problem of overfitting. Tree-based ensembles have been applied in various prediction fields, and such approaches usually produce high prediction accuracy by aggregating and averaging individual decision trees. The inherent advantages of these approaches not only get better prediction results but also have a good bias-variance trade-off which can help to avoid overfitting. However, the reality is that the application of tree-based integration algorithms in traffic prediction is still limited. This study aims to improve the accuracy and interpretability of the models by using random forest (RF) to analyze and model the travel time on freeways.

As the traffic conditions often greatly change, the prediction results are often unsatisfactory. To improve the accuracy of short-term travel time prediction in the freeway network, a practically feasible and computationally efficient RF prediction method for real-world freeways by using probe traffic data was generated. In addition, the variables’ relative importance was ranked, which provides an investigation platform to gain a better understanding of how different contributing factors might affect travel time on freeways.

The parameters of the RF model were estimated by using the training sample set. After the parameter tuning process was completed, the proposed RF model was developed. The features’ relative importance showed that the variables (travel time 15 min before) and time of day (TOD) contribute the most to the predicted travel time result. The model performance was also evaluated and compared against the extreme gradient boosting method and the results indicated that the RF always produces more accurate travel time predictions.

This research developed an RF method to predict the freeway travel time by using the probe vehicle-based traffic data and weather data. Detailed information about the input variables and data pre-processing were presented. To measure the effectiveness of proposed travel time prediction algorithms, the mean absolute percentage errors were computed for different observation segments combined with different prediction horizons ranging from 15 to 60 min.

Resilience concepts in integrated urban transport refer to the performance of dealing with external shock and the ability to continue to provide transportation services of all modes. A robust transportation resilience is a goal in pursuing transportation sustainability. Under this specified context, while before the perturbations, robustness refers to the degree of the system’s capability of functioning according to its design specifications on integrated modes and routes, redundancy is the degree of duplication of traffic routes and alternative modes to maintain persistency of service in case of perturbations. While after the perturbations, resourcefulness refers to the capacity to identify operational problems in the system, prioritize interventions and mobilize necessary material/ human resources to recover all the routes and modes, rapidity is the speed of complete recovery of all modes and traffic routes in the urban area. These “4R” are the most critical components of urban integrated resilience.

The trends of transportation resilience's connotation, metrics and strategies are summarized from the literature. A framework is introduced on both qualitative characteristics and quantitative metrics of transportation resilience. Using both model-based and mode-free methodologies that measure resilience in attributes, topology and system performance provides a benchmark for evaluating the mechanism of resilience changes during the perturbation. Correspondingly, different pre-perturbation and post-perturbation strategies for enhancing resilience under multi-mode scenarios are reviewed and summarized.

Cyber-physic transportation system (CPS) is a more targeted solution to resilience issues in transportation. A well-designed CPS can be applied to improve transport resilience facing different perturbations. The CPS ensures the independence and integrity of every child element within each functional zone while reacting rapidly.

This paper provides a more comprehensive understanding of transportation resilience in terms of integrated urban transport. The fundamental characteristics and strategies for resilience are summarized and elaborated. As little research has shed light on the resilience concepts in integrated urban transport, the findings from this paper point out the development trend of a resilient transportation system for digital and data-driven management.