Search results

The purpose of this study is to investigate the influence of institutional dynamics on road accidents and whether this relationship is moderated by information and communication technology (ICT).

The study adopted a quantitative approach with 133 respondents. Research hypotheses were tested in AMOS version 21. In addition, moderated regression analysis was used to test the moderating role of ICT on the relationship between institutional dynamics and road accidents.

The results show that vehicle maintenance, policy enforcement, safety culture, driver training and driver management positively influence road accidents. Moreover, the study established that ICT moderates the relationship between institutional dynamics and road accidents.

The results of this study serve as a practical guideline for policymakers in the road haulage sector. Managers may gain insights on how to design effective interventions to reduce road accidents.

This research contributes to the existing body of knowledge by exploring previously unexplored moderating paths in the relationship between institutional dynamics and road accidents. By highlighting the moderating role of ICT, the study sheds new light on the institutional dynamics that influence road accidents in the context of road haulage companies.

The purpose of this study is to extensively explore the vehicular network paradigm, challenges faced by them and provide a reasonable solution for securing these vulnerable networks. Vehicle-to-everything (V2X) communication has brought the long-anticipated goal of safe, convenient and sustainable transportation closer to reality. The connected vehicle (CV) paradigm is critical to the intelligent transportation systems vision. It imagines a society free of a troublesome transportation system burdened by gridlock, fatal accidents and a polluted environment. The authors cannot overstate the importance of CVs in solving long-standing mobility issues and making travel safer and more convenient. It is high time to explore vehicular networks in detail to suggest solutions to the challenges encountered by these highly dynamic networks.

This paper compiles research on various V2X topics, from a comprehensive overview of V2X networks to their unique characteristics and challenges. In doing so, the authors identify multiple issues encountered by V2X communication networks due to their open communication nature and high mobility, especially from a security perspective. Thus, this paper proposes a trust-based model to secure vehicular networks. The proposed approach uses the communicating nodes’ behavior to establish trustworthy relationships. The proposed model only allows trusted nodes to communicate among themselves while isolating malicious nodes to achieve secure communication.

Despite the benefits offered by V2X networks, they have associated challenges. As the number of CVs on the roads increase, so does the attack surface. Connected cars provide numerous safety-critical applications that, if compromised, can result in fatal consequences. While cryptographic mechanisms effectively prevent external attacks, various studies propose trust-based models to complement cryptographic solutions for dealing with internal attacks. While numerous trust-based models have been proposed, there is room for improvement in malicious node detection and complexity. Optimizing the number of nodes considered in trust calculation can reduce the complexity of state-of-the-art solutions. The theoretical analysis of the proposed model exhibits an improvement in trust calculation, better malicious node detection and fewer computations.

The proposed model is the first to add another dimension to trust calculation by incorporating opinions about recommender nodes. The added dimension improves the trust calculation resulting in better performance in thwarting attacks and enhancing security while also reducing the trust calculation complexity.

A considerable amount of police evaluation research focuses on innovative approaches to reduce crime at places. This is hardly coincidental; policing and place-based scholars have found crime is highly concentrated, and when police focus on these places, they can prevent and reduce crime. The regularity of such findings led Weisburd (2015) to assert the existence of a “law of crime concentration.” Given that bold assertion, the authors test whether the law of crime concentration is generalizable to one of the most common public safety concerns that police handle—traffic crashes.

To determine whether the law of crime concentration applies to traffic crashes, the authors examined crash locations and times in all counties in Utah across four years. Following and expanding on Weisburd's methods, the authors calculate the bandwidth of concentration for these crashes and analyze various types by severity and possible explanations for variations in crash concentrations across the state.

A small proportion of street segments and intersections experience a disproportionately high number of crashes, and the degree of concentration of crashes may be even higher than that of crime. Further, there are variations in the levels of crash concentration across counties and in the severity of injuries resulting from the crashes.

Place-based criminologists and policing scholars have not often explored traffic crashes in their analyses. Yet, traffic problems take up a significant amount of law enforcement time and resources and are often priorities for most law enforcement agencies. Given what the authors know from traffic, policing and crime and place research, targeted approaches at micro traffic crash hot spots can be beneficial for public safety prevention.

This study is the first to explore the application of Weisburd's Law of Crime Concentration to traffic crashes. Given that police spend a significant amount of time and resources on traffic-related problems in their jurisdiction, finding more effective, evidence-based approaches to address this public safety concern should be a high priority for police and researchers alike.

The purpose of this study was to investigate the likely causes of failure of some sections of road pavements in Ajaokuta, Northcentral Nigeria. This was achieved through a geotechnical assessment of subgrade soils in affected areas.

The methods entailed field and laboratory methods and statistical analysis. Subgrade soil samples were retrieved from a depth of 1,000 mm beneath the failed portions using a hang auger. The soils were analyzed for natural moisture content (NMC), Atterberg limit (liquid limit, plastic limit and linear shrinkage), grain size distribution, compaction and California bearing ratio (CBR), respectively.

The results of the geotechnical tests ranged from NMC (12.5%–19.4%), sand (84%–98%), fines (2%–16%), LL (16.0%–32.2%), PL (17%–27.5%), LS (2.7%–6.4%), PI (2.5%–18.4%), maximum dry density (1756 kg/m2–1961 kg/m2), optimum moisture content (13.2%–20.2%), unsoaked CBR (15.5%–30.5%) and soaked CBR (8%–22%), respectively. Pearson’s correlation coefficient performed on the variables showed that some parameters exhibited a strong positive correlation with r2 > 0.5.

Funding was the main limitation.

Comparing the results with Nigerian standards for road construction, and the AASHTO classification scheme, the subgrade soils are competent and possess excellent to good properties. The soils also exhibited very low plasticity, a high percentage of sand, high CBR and low NMC, which implies that it has the strength required for road pavement subgrades. The likely causes of the failures are, therefore, due to the use of poor construction materials, technical incompetence and poor compaction of sub-base materials, respectively.

This chapter provides an overview of the potential use of Intelligent Transport Systems (ITS) and associated artificial intelligence (AI) techniques in the land transport sector in an attempt to achieve related United Nations Sustainable Development Goals (SDGs) targets. ITS applications that have now been extensively tested worldwide and have become part of the everyday transport toolkit available to practitioners have been discussed. AI techniques applied successfully in specific ITS applications such as automatic traffic control systems, real-time image processing, automatic incident detection, safety management, road condition assessment, asset management and traffic enforcement systems have been identified. These methods have helped to provide traffic engineers and transport planners with novel ways to improve safety, mobility, accessibility and efficiency in the sector and thus move closer to achieving the various SDG targets pertaining to transportation.

This chapter discusses the proposed integrated sustainable road infrastructure project implementation (ISRIPI) model and its constructs and sub-constructs, as well as public participation (PP), climate change response (CCR), and stakeholder management (SM). CEEQUAL was the most comprehensive rating system, with 11 criteria. Lim (2009) and Ugwu and Haupt (2007) were the most thorough studies examining sustainability models for infrastructure projects, focussing on social and cultural sustainability (SCS), economic sustainability (ES), environmental sustainability (EnS), institutional sustainability (IS), health and safety (HS), project management (PM), resource utilization and management (RUM), and engineering performance (EP). The Brundtland Report and Rio Summit defined social sustainability as the right to a decent standard of living, social justice between generations, within generations, and around the world. Thin (2002) showed social justice, unity, investment, and safety as aspects of society. Rosenström et al. (2006) defined social qualities as objects that make people happy. Cultural sustainability is access to cultural assets for current and future generations, and cultural legacy is the collection of physical signals passed on from the past to each civilization and, by extension, to all humans. EnS seeks to reduce the environmental impact of road infrastructure projects. Sustainable road infrastructure project implementation (SRIPI) must consider PM issues such as delivery system, risks, duration, performance assessment, sustainability clauses, and contract type. Quality control systems must be set up to ensure the optimum utilization and management of resources in SRIPI. EP criteria measure sustainable road infrastructure projects’ (SRIP) durability, quality, resilience, adaptability, functionality, carrying capacity, and robustness. This research developed an SRIPI model based on the models of CEEQUAL, Lim (2009), and Ugwu and Haupt (2007). The hypothesized framework consists of 37 SRIPI factors, including SCS, ES, IS, HS, PM, RUM, EP, CCR, PP, and SM. It projected the analytical authority of these constructs in the success of SRIPs to test whether the realization of SRIPI outcomes depends on the supposed indicators of the variables. SRIPI is a multidimensional structure composed of 11 latent variables, derived from literature review and Delphi study findings.

In successfully meeting city and metropolitan growth, sustainable development is compulsory. Sustainability is a must-focus for any project, particularly for large and mega rail infrastructure. This paper aims to investigate to what degree social, environmental and economic factors influence the government when planning sustainable rail infrastructure projects. To respond to such a matter, this paper focuses on two Australian mega-rail projects: the South West Rail Link (SWRL) and the Mernda Rail Extension (MRE).

As the basis of an experimental evaluation framework strengths, weaknesses, opportunities and threats (SWOT) and factor analysis were used. These two methods were specifically selected as comparative tools for SWRL and SWRL projects, to measure their overall sustainability effect.

Using factor analysis, in the MRE, the factors of network capacity, accessibility, employment and urban planning were seen frequently throughout the case study. However, politics and economic growth had lower frequencies throughout this case study. This difference between the high-weighted factors is likely a key element that determined the SWRL to be more sustainable than the MRE. The SWOT analysis showed the strengths the MRE had over the SWRL such as resource use and waste management, and natural habitat preservation. These two analyses have shown that overall, calculating the sustainability levels of a project can be subjective, based on the conditions surrounding various analysis techniques.

This paper first introduces SWRL and MRE projects followed by a discussion about their overall sustainable development. Both projects go beyond the traditional megaprojects' goal of improving economic growth by developing and enhancing infrastructure. Globally, for such projects, sustainability measures are now considered alongside the goal of economic growth. Second, SWOT and factor analysis are undertaken to further evaluate the complexity of such projects. This includes their overall sustainable development vision alignment with environmental, economic and social factors.

Pre-positioning and distributing relief items are important parts of disaster management as it simultaneously considers activities from both pre- and post-disaster stages. This study aims to address this problem with a novel mathematical model.

In this research, a bi-objective mixed-integer linear programming model is developed to tackle pre-positioning and distributing relief items, and it is formulated as an integrated location-allocation-routing problem with uncertain parameters. The humanitarian supply chain consists of relief facilities (RFs) and demand points (DPs). Perishable and imperishable relief commodities (RCs), different types of vehicles, different transportation modes, a time window for delivering perishable commodities and the occurrence of unmet demand are considered. A scenario-based game theory is applied for purchasing RCs from different suppliers and an integrated best-worst method-technique for order of preference by similarity to ideal solution technique is implemented to determine the importance of DPs. The proposed model is used to solve several random test problems for verification, and to validate the model, Iran’s flood in 2019 is investigated as a case study for which useful managerial insights are provided.

Managers can effectively adjust their preferences towards response time and total cost of the network and use sensitivity analysis results in their decisions.

The model locates RFs, allocates DPs to RFs in the pre-disaster stage, and determines the routing of RCs from RFs to DPs in the post-disaster stage with respect to minimizing total costs and response time of the humanitarian logistics network.