Search results

Purpose – The purpose of this chapter is to review the methodological and empirical underpinnings of transport network screening, or management, as it relates to improving road safety. As jurisdictions around the world are charged with transport network management in order to reduce externalities associated with road crashes, identifying potential blackspots or hotspots is an important if not critical function and responsibility of transport agencies.

Methodology – Key references from within the literature are summarised and discussed, along with a discussion of the evolution of thinking around hotspot identification and management. The theoretical developments that correspond with the evolution in thinking are provided, sprinkled with examples along the way.

Findings – Hotspot identification methodologies have evolved considerably over the past 30 or so years, correcting for methodological deficiencies along the way. Despite vast and significant advancements, identifying hotspots remains a reactive approach to managing road safety – relying on crashes to accrue in order to mitigate their occurrence. The most fruitful directions for future research will be in the establishment of reliable relationships between surrogate measures of road safety – such as ‘near misses’ – and actual crashes – so that safety can be proactively managed without the need for crashes to accrue.

Research implications – Research in hotspot identification will continue; however, it is likely to shift over time to both closer to ‘real-time’ crash risk detection and considering safety improvements using surrogate measures of road safety – described in Chapter 17.

Practical implications – There are two types of errors made in hotspot detection – identifying a ‘risky’ site as ‘safe’ and identifying a ‘safe’ site as ‘risky’. In the former case no investments will be made to improve safety, while in the latter case ineffective or inefficient safety improvements could be made. To minimise these errors, transport network safety managers should be applying the current state of the practice methods for hotspot detection. Moreover, transport network safety managers should be eager to transition to proactive methods of network safety management to avoid the need for crashes to occur. While in its infancy, the use of surrogate measures of safety holds significant promise for the future.

Purpose – This chapter first provides the motivation for writing this book. It then describes the challenges involved with assessing societal safety through the analysis of transport system crashes. It concludes with a summary of the contents of the remainder of the book, identifying how various dimensions of the transport system challenges are addressed.

Methodology/Approach – This chapter discusses important real-world and methodological challenges that practitioners, academics and researchers face in making a more sustainable highway system through a reduction in the number and severity of transport network crashes resulting in fatalities, injuries and property damage.

Findings – The chapter first describes important challenges, such as complexity of the driving task, the challenges of engineering transport systems for humans, unanticipated effects that arise from differences between driver safety and security, the co-mingling of mobility modes of travel, and challenges in evaluating road safety. The chapters are separated into five general themes: driver behaviour, the transportation network, vulnerable road users, methods for understanding and predicting safety performance, and methods for evaluating safety impacts of countermeasures.

Practical Implications – Comprehending the challenges associated with road crashes is a first step in making the roadway system more sustainable. This book provides a broad and understandable description of these challenges and how they can be overcome by academics and practitioners working in transport network safety management.

Originality – This book presents a clear understanding and offers insights about the challenges and potential solutions that can be brought to bear to make a more sustainable and safe transport system, whether it is located in an urban or rural area, and for a wide variety of functional classifications and designs. The topics covered in this book are intended to be useful and applied to tackle transport system management anywhere in the world.

Much of the recent research on supply chain uncertainty has focused on relationships between manufacturers and suppliers and existing models have therefore been based on this dyadic structure. The aim is to establish a supply chain uncertainty model that explicitly incorporates transport operations and hence the logistics triad; supplier, customer and transport carrier.

This is a literature‐based activity that synthesises and extends existing models of supply chain uncertainty.

The paper develops a new model to reflect the nature of transport operations. Consequently, it identifies five main categories of uncertainty, namely from the points of view of the supplier, the customer and the carrier, respectively, the control systems used in the supply chain and external factors. The interfaces between the uncertainty categories involving all three parties of the logistics triad are identified, so as to develop a more holistic perspective on supply chain uncertainty and how it can be reduced.

This paper is conceptual in nature and empirical research into the area of transport uncertainty will be required to validate its findings. Following this, the model can be used to investigate and evaluate improvements in the economic and/or environmental performance of freight transport within supply chains.

The model is intended to provide a framework within which organisations, including logistics providers, can develop a supply chain strategy to mitigate the effects of uncertainty. By categorising uncertainty into the types described, organisations may determine where the greatest uncertainties lie and hence develop a prioritised plan for supply chain re‐engineering by initially targeting those uncertainties with the most significant implications for supply chain efficiency.

Little research has been undertaken on the impact of uncertainties on transport in the context of collaborative supply chain management. The model rationalises uncertainties into various types taking into account the nature of the logistics triad.

As the global freight transport network has experienced high vulnerability and threats from both natural and man-made disasters, as a result, a huge amount of data is generated in freight transport system in form of continuous streams; it is becoming increasingly important to develop sustainable and resilient transport system to recover from any unforeseen circumstances quickly and efficiently. The aim of this paper is to develop a stream processing data driven decision-making model for higher environmental performance and resilience in sustainable logistics infrastructure by using fifteen dimensions with three interrelated domains.

A causal and hierarchical stream processing data driven decision-making model to evaluate the impact of different attributes and their interrelationships and to measure the level of environmental performance and resilience capacity of sustainable logistics infrastructure are proposed. This work uses fuzzy cognitive maps (FCMs) and fuzzy analytic hierarchy process (FAHP) techniques. A real-life case under a disruptive event scenario is further conducted.

The result shows which attributes have a greater impact on the level of environmental performance and resilience capacity in sustainable logistics infrastructure.

In this paper, causal and hierarchical stream processing data decision and control system model was proposed by identified three domains and fifteen dimensions to assess the level of environmental performance and resilience in sustainable logistics infrastructure. The proposed model gives researchers and practitioners insights about sustainability trade-offs for a resilient and sustainable global transport supply chain system by enabling to model interdependencies among the decision attributes under a fuzzy environment and streaming data.

This work analyzes a pharmaceutical supply chain (PSC) in terms of supply chain visibility (SCV). The current good distribution practice (GDP) guideline demands increased visibility from firms. The purpose of this paper is to propose a solution for SCV enhancements based on automatic identification (Auto-ID) technologies.

The authors qualitatively analyze data from ten case studies of actors in a PSC. A review of Auto-ID technologies supports the derivation of solutions to enhance SCV.

This work shows that the functionalities of Auto-ID technologies offered by current practical monitoring solutions and challenges created by the GDP guideline necessitate further SCV enhancements. To enhance SCV, the authors propose three solutions: securPharm with passive radio frequency identification tags, transport containers with sensor nodes, and an SCV dashboard.

This study is limited to a PSC in Germany and is therefore not intended to be exhaustive. Thus, the results serve as a foundation for further analyses.

This study provides an overview of the functionality of Auto-ID technologies. In juxtaposition with the influence of the GDP guideline, the use of our Auto-ID-based solutions can help to enhance SCV.

This work analyzes a PSC in Germany, with consideration given to the influence of current legislation. Based on a multiple-case-study design, the authors derive three Auto-ID-based solutions for enhancing SCV.

Purpose – This chapter highlights main thrusts discussed in the book in its entirety.

Methodology – This chapter reviews the content of the book, drawing together the challenges safety analysts and other important road safety stakeholders confront while trying to understand and reduce transport system risks.

Findings – The chapter describes the challenges the profession is confronting. These challenges include the difficulty in analysing crashes and drawing reliable conclusions when crashes are rare, the complexities involved with piecing together and identifying actual causes of crashes, how driver behaviour is a dominant influence on crash risk, how a diverse mix of road users is challenging to manage, that estimating the safety benefits of treatments is fraught with complexity, how surrogate measures of safety – despite the analytical research needed on their linkages with crashes – can be used for proactively improving design and operational decisions, and how real-time data collected on monitored highways, which need the development of solid theoretical underpinnings moving forward – can be used reliably for estimating crash risks and manage safety. The chapter briefly summarises how these challenges may be addressed moving forward.

The chapter also identifies future research opportunities that can be pursued to further improve safe mobility. These opportunities include the use of alternate methodologies for ‘measuring safety’ such as advanced vision recognition techniques, the use of surrogate measures of safety such as time to collision, driverless and connected vehicles, naturalistic driving experiments and data, the reshaping of urban cities, and the rapid growth and motorisation of developing countries.

Practical implications – By fostering a better understanding of the challenges the profession is facing, and prompting a dialogue on how they can be overcome, will lead to a reduction in the number and severity of crashes of global transport networks, making them more sustainable. The chapter shows how some of these challenges can be tackled.

Originality/value of chapter – This chapter draws from all the chapters of this book that describes various challenges road safety professionals currently face, and directs readers to different parts of the book for more in-depth treatment on how these challenges can be met. The chapter also describes research opportunities where further safety gains can be obtained moving forward.

An optimal network design model is formulated providing a set of link investment pattern for the most reliable network with highest network performance under uncertain conditions. The connectivity probability of a link is assumed to be improved by the investment to the link. The object function is represented as the expected performance measure. The formulated model is categorized in a group of stochastic network design problem in which the existence of a link is probabilistic. The characteristics of the gradient vector of the objective function are analyzed. The derivatives of the objective function can be approximately evaluated without enumerating all possible network state vectors. Numerical examples are calculated for analyzing the sensitivity of optimal investment policies.

This paper considers correction aspects of computer communication networks modelling with emphasis on their performance evaluation. In general, the problem is to achieve the highest possible performance given constraints on the system.

The paper reviews the application of the analytical methods, based on the queueing theory, to the computer communication systems and makes an extension of theory to the improvement of the developed analytical models. In this sense the paper describes the derivation of a correction factor for analytical models to study more precise their basic parameters (end‐to‐end delay, performance, etc.).

The contribution is in incorporating the derived correction factor to account for the real non‐exponential nature of the input to the transmission channels of computer communication systems. The produced results by corrected analytical model are compared with results previously reported in the literature to estimate the magnitude of improvement.

The improved analytical models were tested under various ranges of parameters, which influence the architecture of the computer communication networks and which are important for practical use.

The rapid rate of growth of computer‐based communication systems (e.g. distributed computer networks, mobile data networks) has resulted in a renewed and intensive interest in this area. Efficient design of their service facilities leads to the sharing of resources among users. Such public shared networks are largely oversubscribed by independent users, which make random demands on the network resources. The optimal resource allocation to satisfy such demands and the proper settlement of contention when demands exceed the capacity of the resources, constitute the problem of being able to understand and to predict system behaviour.

To behaviour analysis we can use both analytical and simulation methods. Modelling and simulation are methods, which are commonly used by performance analysts to represent constraints and optimise performance. Principally the application of analytical queuing theory results belongs to the preferred method in comparison to the simulation method, because of their ability to analyse also very large networks.

The purpose of this paper is to understand the main market entry barriers confronted by the new operators in liberalized railway freight market (Poland and Sweden), as well as to analyze the inaugurating market of Finland.

Swedish and Polish markets were scrutinized utilizing qualitative case study, implemented through semi‐structured theme interviews. Among primary observations, numerous second‐hand sources were used to gain triangulation. Research was conducted during early 2009. The Finnish material was collected with Delphi technique‐based questionnaires in 2005.

The main findings support previous studies arguing that the main barriers to entry are rolling stock acquisition, needed investments and bureaucracy. In Sweden, companies were start‐ups established on the grounds of the incumbent. The Polish market obtained new operators via vertical integration with a significant competitive presence of a governmental operator. Inaugurating Finnish market is identified as a combination of these two. Therefore, it is easier to understand why new entrants are not operating in the Finnish market.

The research contributes novel, first‐hand data to the subject, which earlier have been studied mostly via second‐hand data and literature analyses.

Emerging technologies have the capacity to transform industries offering substantial benefits to users. Given the increasing demand for advanced logistics services, third-party logistic service providers (LSPs) face greater pressure to deploy and realise these technologies, especially given the demands and operational challenges created during the COVID-19 crisis. Drawing upon the diffusion of innovation (DOI) theory and technology–organisation–environment (TOE) framework, this paper goes beyond just identifying drivers and barriers to technology adoption to understanding how LSPs and industry experts perceive these drivers and barriers and simultaneously confront and undertake actions to implement them.

An exploratory study was conducted in three phases: (1) in-depth interviews with twelve stakeholders in the Australian logistics industry; (2) five in-depth interviews conducted with stakeholders during the COVID-19 crisis and (3) a focus group discussion session. All interviews were analysed using content analysis and revealed several drivers for the deployment of emerging technologies, including internal organisational factors that drive supply chain (SC) network optimisation.

The analysis of the three phases identified several drivers for the deployment of emerging technologies in logistics, including internal organisational factors that drive SC network optimisation. Also identified were external drivers including the impact of the COVID-19 crisis, along with barriers and specific actions that were considered and implemented by LSPs for sustainable operations, particularly in a post-COVID-19 environment.

This study explores organisational and industry drivers for the implementation of emerging technologies. Explicitly, it extends the extant research by highlighting organisational and industry drivers and enablers that influence adoption and deployment of emerging technologies. Second, it advances the existing perspectives on LSPs in the Australian context on the development and implementation of technology strategies. The paper offers insights around implementation of technologies, directly obtained from industrial application for managers and practitioners.