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Reasonable assessments of the impact of information infrastructure construction on entrepreneurship will support the development of the digital economy in China. Using nationwide data from 2005 to 2017, this paper analyzes the causal effect of the "Broadband China" pilot policy on entrepreneurship based on a difference-in-differences approach. We find that information infrastructure construction significantly enhances entrepreneurship, which is robust under different settings. A mechanism exploration suggests that constructing information infrastructure benefits information acquisition, knowledge spillovers, and risk-taking behavior, which illustrate the internal mechanism of promoting entrepreneurship. Taken together, this paper supports the positive role of the digital economy in the promotion of entrepreneurship in addition to providing new insights into the construction of information infrastructure to support entrepreneurship. [ABSTRACT FROM AUTHOR]

Information technology infrastructure (ITI) has gone through an exponential change due to the rapid evolution of digital technologies during the past few years. It is therefore relevant to identify the factors that push organizations towards the adoption of new technologies. This paper considers a specific case study of the higher education institutions (HEI) to present (i) the motivational factors that push HEIs toward the adoption of new technologies, and (ii) the key challenges HEIs face in the adoption of these technologies. The data was gathered after conducting 17 open-ended interviews with practitioners working at HEIs in Finland and Pakistan. Data analysis identified 10 motivational factors and 41 challenges faced by HEI in the adoption of the latest technologies. Furthermore, the data was further classified to identify broad themes and categories representing the challenges and motivation factors. [ABSTRACT FROM AUTHOR]

• Robust infrastructure is essential to enhance urban productivity and support urban development. • Transportation infrastructure enhances urban productivity by promoting economic agglomeration. • Information infrastructure promotes urban productivity through labor mobility and agglomeration effects. • The promotion effect on urban productivity in eastern regions and megacities is more obvious. • Transportation and information infrastructure have positive spatial spillover effects on urban productivity. Improving total factor productivity is an important driving force for achieving high-quality economic development. Based on the panel data of 256 cities in China from 2009 to 2019, this paper investigates the effect of transportation and information infrastructure on urban productivity. The results show that transportation and information infrastructure promote urban productivity, and the effect in eastern regions and megacities is more obvious. The mechanism analysis shows that transportation infrastructure enhances urban productivity by promoting economic agglomeration, and information infrastructure enhances urban productivity by promoting labor mobility, economic agglomeration, financial agglomeration and industrial agglomeration. The spatial spillover effect shows positive spatial autocorrelation of urban productivity. Transportation and information infrastructure have significant positive spatial spillover effects on urban productivity. China should continue to promote the construction of transportation infrastructure, strengthen investment in information infrastructure, and promote the joint construction, sharing, coordination and integration of transportation and information infrastructure. [ABSTRACT FROM AUTHOR]

At present, traffic state prediction primarily relies on purely data-driven methods, ignoring the incorporation of physical constraints within the field of traffic flow. Taking this as a starting point, this paper endeavors to embed the physical mechanism of the traffic flow fundamental graph into the deep learning model, and proposes a physics-integrated spatiotemporal graph neural network with fundamental diagram learner (PI-STGnet) for highway traffic flow prediction. The PI-STGnet mainly comprises a semantic enhancement module (SE), a spatiotemporal correlation extraction module (ST-Block), and a fundamental diagram learner module (FD-learner). These modules are strategically designed to sequentially enhance the contextual semantic relationships of the traffic flow and speed inputs, extract intricate spatiotemporal correlations of traffic states, and adaptively learn the dynamic evolution of the physical relationships between traffic flow and speed. The real-world dataset employed to assess the performance of the PI-STGnet is sourced from the monitoring data of highway gantry sensors located in Ningde City, Fujian Province, China. The experimental results demonstrate that the proposed PI-STGnet has the advantages to extract spatiotemporal features and achieve prediction, surpassing the accuracy of cutting-edge baseline models. In summary, as an exploratory work, this paper provides a novel approach that driven by the fusion of deep learning and physical mechanisms for traffic flow prediction to a certain extent. • A physics-integrated graph neural network is proposed for traffic flow prediction. • Multi-source data is fused to achieve complete characteristics representation. • An improved ST-Block module is proposed. • A fundamental diagram learner module is designed and fused into GCN. • The experiment results prove PI-STGnet is superior to existing competitive models. [ABSTRACT FROM AUTHOR]

Various stages of highway project construction process involve text, image, audio, video and other related data sources involving many participants, forming a huge amount of data. Accurately tracing the source of responsibility, refining and applying the unbalanced data in the highway project archives is of great significance for realizing the intelligent transformation of highway construction project management. This paper firstly sorts out the construction process of highway projects and the main data sources, constructs a data association network between construction entities and construction process, as well as a knowledge map of highway construction data. Then, according to the highway construction stage, an index system based on 12 key data is constructed by using the entropy weight cloud model method, and the importance of the data is evaluated. Thirdly, based on the unbalanced characteristics of highway project data, a method of mining big data in highway project archives using classification evaluation indexes is proposed, and the accuracy of this method is verified by case calculation. Finally, taking the Shizong Qiubei Expressway in China as an example, the intelligent management and control suggestions for key data of transportation projects are proposed. It is found that the key data with special importance rate in highway construction include construction data, supervision data and completion data. Boosting algorithm is more accurate than the traditional SMOTE algorithm for unbalanced data mining, which helps to save the project construction cost and improve the quality of data extraction in the project archives. This study provides a theoretical reference for key data traceability of highway project intelligent management and control platform and the improvement of intelligent management efficiency. [ABSTRACT FROM AUTHOR]

Open building information modeling for infrastructure and cloud services featuring the InfraBIM have already been identified as a feasible solution to manage worksite information, from surveys and design to the construction phase. This paper describes the use of InfraBIM on remote and automatically controlled excavators as part of the digitalization of the construction process. The concept of the InfraBIM process is briefly discussed. The systems of guided and remote control are explained, and the Open InfraBIM-based trajectory generation concept for automatic excavation is introduced. To assess the functionality of these systems, a small-scale excavation task was planned and executed using three different modes, with each outcome evaluated and discussed. The quality of trenches dug using the excavator was verified with scanned data, and excavation efficiency was evaluated by measuring the time to complete the task. For remote excavation, the InfraBIM-based guidance was observed to support remote operation better than a video feed. The paper proposes sharing more detailed situational as-built data in the InfraBIM concept to enable adaptive autonomous excavation. [Display omitted] • The InfraBIM Machine Control Models are suitable for trajectory generation. • Vehicle-to-vehicle and vehicle-to-cloud communication model is discussed for synchronous swarm collaboration. • The InfraBIM concept lacks sufficient initial and situational data for adaptive autonomy. • Remote operation challenges are emphasized in more demanding 3D shaping tasks. • Near future vision is to free operators from basic controls to enable swarm operation. [ABSTRACT FROM AUTHOR]

The development of artificial intelligence (AI) technology for radiology has accelerated in the past decade, but its deployment in radiology practices has been slow. We take a sociotechnical approach and suggest that the limited use of AI in radiology practices can be attributed to a recurring tension between planned and emergent change. The paper contributes with a conceptualization and understanding of the tension during the procurement of AI for radiology. To balance this tension, we suggest that health organizations need to redefine the concept and scope of traditional procurement projects, with well-defined goals and project time. Instead, we propose that health organizations need to conceptualize their procurement and implementation projects of AI technology as evolving change processes. The study is based on an interpretive research approach and informed by the Information Infrastructure framework. Empirically, we study the procurement of AI solutions for radiology at a large health trust in Norway. [ABSTRACT FROM AUTHOR]

Constructing geometric digital twins of highways at present still demands substantial human effort. Unlike most previous work that uses deep learning models to segment point clouds of highways into class level or object instance level, this paper further segments pavements into a more detailed level (lanes, hard shoulders, central reserves). The central curves of each lane marking are fitted in a two-step method, approximated by a polynomial and then converted into the Frenet coordinated system. The fitted curves with smoothly changing curvature are used to separate points of road surfaces into lanes, hard shoulders, and central reserves, resulting in a mean Intersection over Union (mIoU) at around 90%. This automatic approach extracts geometric and object category information from point clouds and stores the information in a graph, showing the hierarchical relationships among various components and offering the potential for expansion into more comprehensive digital twins encompassing the entire highway network. • Segment point clouds of pavements into a more detailed level. • Fit curves to lane markings in point clouds and use them to segment pavement points. • Generate graph representation of highway geometric digital twins from point clouds. [ABSTRACT FROM AUTHOR]

Development and implementation of Current Research Information Systems (CRIS) is one of the most transparent and practical approaches to curate research information on a national level. The process of building and implementing such systems is a complex and time consuming where successful results heavily depend on the established research information infrastructure of a country, the interoperability of the systems and the quality of the information which reside in them. The purpose of this paper is to analyse the existing Ukrainian Research Information Infrastructure and identify which databases could be reused and integrated with a national Ukrainian Current Research Information System (URIS). The analysis showed that there are functional databases and registries that collect data on research activities and could be used as a data sources for the URIS. In particular, the Unified State Electronic Database on Education is a potential data source on higher educational institutions, the National Repository of Academic Texts — on metadata on research output, internal database of the National Research Foundation of Ukraine and database on research projects maintained by Ukrainian Institute of Scientific Technical and Economic Information — on projects. Secondly, it was identified that Ukrainian research infrastructure lacks complete, up-to-date registry on researchers. Finally, we discussed the challenges and solutions for further steps in building national CRIS. [ABSTRACT FROM AUTHOR]

Information technology (IT) service management is an essential part for development of a company's IT. This case study discusses how to convalesce IT services using the information technology infrastructure library (ITIL) framework and measure service level management (SLM) using Fuzzy ITIL (FITIL) approach. This paper aims to obtain an appropriate model for the measurement of IT service management by using fuzzy approach. Besides that, this paper aims to be able to provide an improving recommendations and IT governance based on current value (as is) and expected value (to be). The research method functioned is by measuring maturity level using best practice of ITIL v3 to condition before and after of improving process based on a questionnaire that has been performed. After obtaining the value of the maturity level for each cycle within ITIL, then the value will be created as an input for FITIL. The manufacture of FITIL is done in 4 stages, namely fuzzification, knowledge base, inference, and defuzzification. The results of the conditions before and after of the improving process have been successful in increasing the level of maturity in each ITIL cycle. The case study indicates an improvement in the increased level of maturity in SLM with FITIL approach. [ABSTRACT FROM AUTHOR]

Vehicles play a pivotal role in the current era of Industry 4.0 by providing passengers with excellent mobility, comfort, and safety while strengthening national and international economies. Unanticipated vehicular engine issues can hinder performance and lead to costly maintenance. As analytics processes become faster, more accurate, and more reliable, intelligent fault prediction and diagnosis for vehicles, particularly engines, is becoming increasingly popular. To date, hybrid deep learning approaches to vehicle engine diagnostics have been limited, and none have used engine health monitoring and categorisation based on vulnerability assessment and vehicle structural information. This paper introduces a hybrid deep learning-based vehicular engine health monitoring system (VEHMS) decision model using Deep CNN (convolutional neural network)-BiGRU (bi-directional gated recurrent unit). This model monitors a vehicle's engine health in real-time and classifies its status as good, critical, moderate, or minor condition. Several advanced and hybrid deep learning algorithms were applied to monitor engine health and categorise its status by integrating sensor data with evaluated vulnerability information from an infrastructure vulnerability assessment model. The Deep CNN-BiGRU-based VEHMS decision model outperformed other techniques with an accuracy of 0.8897, ensuring minimal decision losses while classifying engine conditions. This study aims to contribute to developing comprehensive vehicle health monitoring systems and advance the automotive industry by incorporating more intelligent features. The proposed approach can enhance vehicle performance, reliability, and efficiency in the transportation sector by improving engine health monitoring. [ABSTRACT FROM AUTHOR]

Highlights • This paper uses the arrival of broadband, to study the causal effects of heterogeneous types of ICT on firm performance. • Instruments are constructed on the infrastructure underlying broadband, which determines its availability and speed. • The instruments strongly predict the upgrading of various hardware ICTs, but not software technologies. • We find that these ICTs impact the scale of firms, through employment and sales, but not their productivity. • Access to broadband is linked to complementary investment in various ICTs which are consistent with the scale effect found. Abstract The recent economics literature has begun to recognise that ICT is a heterogeneous technology altering information storage, processing and communication in distinct ways. In this paper we use the arrival of a new communication technology, ADSL broadband, to study the effects of heterogeneous types of ICT on firm performance. To do so free from endogeneity bias, we construct instruments using postcode-level geographic variation in the infrastructure underlying broadband internet – the pre-existing telephone network. We show that after placing various restrictions on the sample, instruments based on the timing of ADSL broadband enablement and the cable distance to the local telephone exchange satisfy the conditions for instrument relevancy and validity. We find in turn, that ICT causally affects firm size (captured by either sales or employment) but not productivity. [ABSTRACT FROM AUTHOR]

Abstract In this paper, continuing our research on designing a Network Security Intelligence Center (NSIC) as a combination of a Security Intelligence Center and a Network Operations Center, we applied the PDCA approach to its development in accordance with the requirements, which we formulated for its operations and self-protection. Following the Plan stage of this approach, we proposed a five-layered NSIC's infrastructure (including platform, software, service delivery, access and management layers) and its high-level zone security infrastructure with four zones: demilitarized, trusted, restricted and management zones. All of them are briefly described in the paper. The main area of short-term further work concludes the paper. [ABSTRACT FROM AUTHOR]

Future 5G networks aim at providing new high-quality wireless services to meet stringent and case-specific needs of various vertical sectors beyond traditional mobile broadband offerings. 5G is expected to disrupt the mobile communication business ecosystem and open the market to drastically new sharing based network operational models. 5G technical features of network slicing and small cell deployments in higher carrier frequencies will lower the investment barrier for new entrants to deploy local radio access networks and offer vertical specific services in specific areas and allow them lease the remaining required infrastructure on demand from mobile network operators (MNO) or infrastructure vendors. To realize the full vision of 5G to benefit the society and promote competition, innovation and emergence of new services when the 5G end-to-end network spans across different stakeholders administrative domains, the existing regulations governing the mobile communication business ecosystem are being refined. This paper provides a tutorial overview on how 5G innovations impact mobile communications and reviews the regulatory elements relevant to 5G development for locally deployed networks. This paper expands the recent micro licensing model for local spectrum authorization in future 5G systems and provides guidelines for the development of the key micro licensing elements. This local micro licensing model can open the mobile market by allowing different stakeholders to deploy local small cell networks with locally issued spectrum licenses ensuring pre-defined quality guarantees for the vertical sectors’ case specific needs. [ABSTRACT FROM AUTHOR]

Local energy sharing (LES) is a concept that enables sharing between distribution system participants such as consumers, producers, and prosumers at the local level in a transparent and cooperative manner. It can improve local supply-demand balancing, reduce voltage deviations, and improve social welfare. However, the feasibility of such an approach is highly dependent on the regulatory framework and implementation requires investment in an adequate information and communication infrastructure. This paper examines components for the implementation of LES within energy communities in the EU, with a focus on price-forming methods that can be integrated into a net-billing system and adopted for different regulatory set-ups. Further, a method for the assessment of impacts on market participants is provided. The approach is applied for the assessment of the opportunities for LES in the city of Križevci, considering local generation, flexibility options, and real-life regulatory requirements. It is shown that LES under appropriate regulatory provisions can be an effective market-based mechanism for stimulating local generation and flexibility activation, and that way support decarbonization and local self-sufficiency. All members can benefit from participating in the energy community, but the distribution of the benefits notably depends on the applied LES price-forming method. On the other hand, subject to regulatory setup, the trade-offs are reflected on reduced revenues for market participants that generate income based on transmission fees, taxes, and/or surcharges. • Components for local energy sharing (LES) implementation within energy communities. • Framework for feasibility evaluation of LES under different regulatory set-ups. • MILP LES market-clearing model with energy sharing compensation price method. • LES price-forming methods integrated into a net-billing system. • Simulation of LES as a market with a local energy sharing coordinator. [ABSTRACT FROM AUTHOR]

To investigate whether information infrastructure can improve air quality, this paper presents an empirical study based on urban data for 2015 to 2018. To analyse regional differences in the impact of information infrastructure on air pollution, we used full sample and subsample data to make estimates respectively. The results show that information infrastructure can significantly reduce air pollution. However, there are regional differences in this effect, which is only significant in the eastern part of China. Information infrastructure can reduce air pollution in two ways: improving the quality of traditional infrastructure and improving energy efficiency In addition, the robustness test results are consistent with the above results. Finally, we put forward some suggestions for the future construction and application of information infrastructure. • Measuring information infrastructure with the technological index. • Information infrastructure can significantly reduce urban air pollution. • Traditional infrastructure and energy efficiency can act as influence mechanisms. • Information infrastructure has different effects on air quality in different regions. [ABSTRACT FROM AUTHOR]

Maintenance planning on bridges commonly faces multiple challenges, mainly related to complexity and scale. Those challenges stem from the large number of structural elements in each bridge in addition to the uncertainties surrounding their health condition, which is monitored using visual inspections at the element-level. Recent developments have relied on deep reinforcement learning (RL) for solving maintenance planning problems, with the aim to minimize the long-term costs. Nonetheless, existing RL based solutions have adopted approaches that often lacked the capacity to scale due to the inherently large state and action spaces. The aim of this paper is to introduce a hierarchical RL formulation for maintenance planning, which naturally adapts to the hierarchy of information and decisions in infrastructure. The hierarchical formulation enables decomposing large state and action spaces into smaller ones, by relying on state and temporal abstraction. An additional contribution from this paper is the development of an open-source RL environment that uses state-space models (SSM) to describe the propagation of the deterioration condition and speed over time. The functionality of this new environment is demonstrated by solving maintenance planning problems at the element-level, and the bridge-level. • Hierarchical reinforcement learning (RL) formulation for bridge maintenance planning. • Decision-making based on the deterioration condition and deterioration speed. • Maintenance planning at element and bridge-level. • Benchmark RL environment for transportation infrastructure maintenance policies. [ABSTRACT FROM AUTHOR]

The smart city emerges as an innovation-driven strategy to achieve green development. While a large body of existing literature has assessed the environmental performance of the smart city, rarely does literature investigate the effect of smart city on green technological innovation, an essential part of and path to green development. This paper analyzes the impact of the smart city on green technological innovation through the Multi-period DID approach, using a dataset of Chinese prefecture-level cities from 2003 to 2016. The baseline results show that smart city can promote green technological innovation. The heterogeneous tests show that the smart city's green technological innovation effects are heterogeneous by geographic location, urban population size, economic development level, and information facilities infrastructure. Moreover, the paper explores the role of green technological innovation as an essential mechanism of the other aspects of green development. The mediating effect analyses show that smart city can promote urban industry structure upgrading and green efficiency through green technological innovation. • Smart city can stimulate green technological innovation as a part of green development performance. • Smart city's green technological innovation effects are heterogeneous by cities' characteristics. • Smart city can achieve green efficiency improvement and industry structure upgrading via green technological innovation. [ABSTRACT FROM AUTHOR]

• Providing a more effective Weighed DTW for traffic flow data analysis. • Systematically analysing and labelling a large real traffic dataset from the City of Melbourne. • GWDTW outperforms Euclidean and DTW using k-medoid for clustering traffic flow data. This paper presents a novel similarity measure to identify interesting traffic patterns on a large traffic flow time series data for the central suburbs of Melbourne city in Australia. This new measure is a weighted Dynamic Time Warping (DTW) method based on Gaussian probability function, named GWDTW, that reflects the relative importance of peak hours. We have shown its superior performance over two benchmark similarity measures, the Euclidean distance and conventional DTW measure, on the intersection clustering task using k -medoids clustering algorithm, with respect to both internal and external evaluation measures. With intensive evaluation, the results show that GWDTW is a very effective similarity measure for modelling traffic behaviours, which can provide policy makers with more valuable information for infrastructure design, and smart city development. [ABSTRACT FROM AUTHOR]

Supported by digital systems, closed loop medication management (CLMM) is considered an essential method to improve the quality of medication management in hospitals. This includes electronic ordering, verifying, preparing and administering medication. However, healthcare has so far seen very few fully working solutions of CLMM. We therefore pose the research question: What is the nature, challenges and consequences of CLMM in hospitals? We find that CLMM is highly resource demanding and implies a lot of engagement and workaround by health personnel. While some workaround are expected, we find that others may jeopardize patient safety. The paper contributes with an understanding and conceptualization of CLMM and proposes socio-technical strategies for using digital tools to increase the quality of the medication management process. Theoretically, we draw on the concept of information infrastructures, work practice perspectives and work system theory. Empirically, we present a study of the first hospital in Norway to have implemented CLMM. [ABSTRACT FROM AUTHOR]

While the 21st century is crowded with disruptive technologies and ground-breaking innovations, this study will take a step back and explore basic digital competence, such as navigating a spreadsheet or using word processing tools and e-mail to conduct tasks at work. Based on a case study approach where data is derived from iterative testing of 213 employees in the banking sector and 10 semi-structured interviews, we contribute to the literature of information infrastructure and knowledge management by addressing the basic digital level of competence amongst employees. We found that the level of basic digital competence was surprisingly low, especially regarding spreadsheets; however, our analysis revealed that the skills can be quickly improved. The challenge is rather to make employees aware of the possibilities and benefits of improvement. This paper presents insights that should be useful for employees and managers in banks and similar industries. It also reminds researchers within information systems to pay attention to basic digital competence. [ABSTRACT FROM AUTHOR]

Productivity measures are based on either physical or revenue-based measures of output. Revenue based measures require the use of a price index. In this paper we propose an approach to generate a quality-adjusted producer price index for the production of roadways. We use price data for highway projects across the contiguous United States from 2005 through 2017. The data set includes approximately 5000 unique items for each project. These items are distilled to form 60 "item baskets". We redefine our output from being lane-miles to 'lane-miles of service'. The indicator of quality is the deterioration rate of a roadway, which is measured using data on pavement roughness that links to deterioration and the time between required servicing; an improvement in quality reduces deterioration and increases the time span between maintenance and rebuilding. We use a chained-Fisher price index and find that our proposed, quality-adjusted producer price index exhibits 1.7 percent lower annual growth than the unadjusted price index. Given price inflation has been overestimated in the past by failing to account for quality changes, our finding suggests the lack of productivity growth in producing roads, bridges and infrastructure, may have been significantly underestimated. • Create price index to capture quality improvements. • Price deflators disaggregated across technologies and activities. • Measure productivity with quality adjusted price index. • Show productivity is underestimated. [ABSTRACT FROM AUTHOR]

Traffic congestion is, nowadays, one of the most important highway problems. Highway tolls with booth operators are one of the causes of traffic congestion on highways, especially in rush hour periods, or during seasonal holiday travels. The value of driver waiting time (needed to stop and pay the toll) and the cost of the toll booth operators can reach up to about one-third of the revenue. In this paper we propose a novel methodology for continuous-time optimal control of highway tolls by predicting the optimal number of active modules (booths) in toll stations. The proposed methodology is based on a combination of recurrent neural networks, queuing theory, and metaheuristics. We utilized several recurrent neural network architectures for predicting the average intensity of vehicle arrivals. Moreover, the prediction error of the first recurrent neural network was modelled by another one in order to provide confidence estimates, additional regularization, and robustness. The predicted intensity of vehicle arrival rates was used as an input of the queuing model, whereas differential evolution was applied to minimize the total cost (waiting and service costs) by determining the optimal number of active modules on a highway toll in continuous time. The developed methodology was experimentally tested on real data from highway E70 in the Republic of Serbia. The obtained results showed significantly better performance compared to the currently used toll station opening pattern. The solutions obtained by solving a system of differential equations of the queuing model were also validated by a simulation procedure. • In this paper we proposed the methodology for unsolved problem of predicting the optimal number of active modules on toll stations. • Flexible modelling of the intensity function without using complicated simulation procedures. • The proposed methodology was applied on real-world data and demonstrate its effectiveness in practice. [ABSTRACT FROM AUTHOR]

The coupling of physical systems with information technologies infrastructure in a networked control system (NCS) has a number of advantages such as remote control, reduced system complexity, low maintenance cost and improved system efficiency. But these benefits come at the cost of making the NCS vulnerable to cyberphysical attacks on the communication layer. One such class of attacks is the zero-dynamics attack (ZDA) which targets the internal dynamics of an NCS with the potential to cause serious damage to the physical system while remaining stealthy. A better understanding of how ZDAs are constructed will enable more effective countermeasures. In this paper, we discuss two approaches for constructing a ZDA and demonstrate using three industrial processes studied in this context perhaps for the first time. • Networked control systems are vulnerable to attacks on the communication layer. • Zero-dynamics attacks remain stealthy, targeting the internal dynamics of a system. • Geometric control and fault detection based methods generate similar attack vectors. • Zero-dynamics attacks rapidly destabilize industrial plants causing physical damage. [ABSTRACT FROM AUTHOR]

• This paper investigates the underlying factors that affect injury severity in the rollover crashes in North Carolina. • A random parameter logit approach with heterogeneity in means and variances is employed. • Instabilities and stabilities of factor effects are considered in different vehicle types and time periods. • Recommendations are provided to mitigate the injury severity based on research findings. Problem: The rollover crash is a serious crash type that often causes higher injury severities. Moreover, factors that contribute to the injury severities of rollover crashes may show instabilities in different vehicle types and time periods, which requires further investigations. This study utilizes the rollover crash data in North Carolina from Highway Safety Information System (HSIS) to study the effect instabilities of factors in vehicle type and time periods in rollover crashes. Methods: The injury severities of drivers are estimated using the random parameters logit (RPL) model with heterogeneity in means and variances. Available factors in HSIS have been categorized into three groups, which are drivers, road, and environment, respectively. This study also justifies the segmentations through transferability tests. The effects of identified significant factors are evaluated using marginal effects. Results: Factors such as FWP (farm, wood, and pasture areas), unhealthy physical condition, impaired physical condition, road adverse, and so forth have shown instabilities in marginal effects among vehicle types and time periods. Practical Applications: The finding of this research could provide important references for policy makers and automobile manufactures to help mitigate the injury severity of rollover crashes. [ABSTRACT FROM AUTHOR]

In an increasingly digitalized world, cybersecurity has emerged as a critical component of safeguarding sensitive information and infrastructure from malicious threats. The threat actors are often in line or even one step ahead of the defense, causing the increasing reliance of security teams on artificial intelligence while trying to detect zero-day attacks. However, most of the cybersecurity solutions based on artificial intelligence that can be found in the literature are trained and tested on reference datasets that are at least five or more years old, which gives a vague insight into their security performances. Moreover, they often tend to be designed as isolated, self-focused components. The aim of this paper is to design and implement a modular network intrusion detection architecture capable of simulating cyberattacks based on real-world scenarios while evaluating its defense capabilities. The architecture is designed as a full pipeline from real-time network data collection and transformation to threat-information presentation and visualization, with a pre-trained artificial intelligence module at its core. Well-known components like CICFlowMeter, Prometheus, and Grafana are used and modified to fit our data preparation and core modules to form the proposed architecture for real-world network traffic security monitoring. For the sake of cyberattack simulation, the proposed architecture is situated within a virtual environment, surrounded by the Kali Linux-based penetration simulation agent on one side and a vulnerable agent on the other. The intrusion detection artificial intelligence module is trained on the CICIDS-2017 dataset, and it is demonstrated using the proposed architecture that, despite being trained on an outdated dataset, the trained module is still effective in detecting sophisticated modern attacks. Two case studies are given to illustrate how modular architectures and virtual environments can be valuable tools to assess the security properties of artificial intelligence-based solutions through simulation in real-world scenarios. [ABSTRACT FROM AUTHOR]

Prior solutions on session-based recommendation (SBR) are mainly limited by two major issues: (1) the sequence and transition relationships of items need further integration; (2) the context clues from the neighboring sessions remain largely under-explored. To overcome these issues, this paper proposes a novel Context-aware Graph Embedding Network (CGENet) with gate and attention mechanisms, that not only can effectively exploit the collaborative relationship between the sequence and transition patterns in each session, but also benefit greatly from topological context patterns among different sessions. Specifically, the proposed CGENet consists of three different parts, i.e., Transition Pattern Learning (TPL) module, Sequential Pattern Learning (SPL) module, and Context Pattern Learning (CPL) module. The TPL module is built on Graph Isomorphic Network (GIN) with multiple information highways to capture the transition relationships between items. To maximize the value of sequence-position information, a Gated Multilayer Perceptron (gMLP) is introduced into the SPL module to model the long-term dependencies between sequence tokens. Under the standardized guidance of the graph attention layer, the CPL module can further explore the topological contexts from neighboring sessions, thereby enhancing its ability to predict user preferences more effectively. Extensive experiments on three benchmark datasets demonstrate the superiority of the proposed CGENet compared to the state-of-the-art baselines. [Display omitted] [ABSTRACT FROM AUTHOR]

Information integration and analysis is the field of IT science established since the first data storages were created, and it keeps being a question of current interest for many researchers and developers all over the world in different business areas. This paper discusses approaches to the question, being developed and used for the management of mega-science experiment metadata, characterized by wide variety, vast volumes and continuously evolving structure. [ABSTRACT FROM AUTHOR]

The perimeter control concept based on macroscopic fundamental diagram (MFD) modeling is now well-established in various literature. Recent research efforts are devoted to develop perimeter control schemes, which can be eventually applied to urban traffic control systems. Modern urban traffic control systems increasingly rely on information technology infrastructure, combining various network and physical facilities and use of communication technologies, which increases the vulnerability for cyberattacks. As witnessed in recent real-life events, modern urban traffic control systems are vulnerable and not protected from internal and external, malicious and accidental threats. Hence, applicable perimeter control algorithms should be robust not only against dynamic uncertainties, but in addition, it should be resilient against cyberattack issues for real future implementations. In this paper, a resilient perimeter control scheme is developed for a modified multi-region MFD system model. The developed perimeter control is a fully decentralized scheme, which operates based on online local regional information only. It achieves an a priori desired tracking performance with simultaneous compensation of (i) cyberattacks in the channels of control signals transmission, (ii) model parameter uncertainties, (iii) unknown but bounded disturbances, and (iv) control input constraints. A framework of adaptive control techniques is utilized to design the perimeter control. The proposed fault-tolerant perimeter control does not require on-line fault detection, as the fault effects caused by cyberattacks are continuously and adaptively compensated by the developed algorithm. Stability proof of the closed-loop system in terms of the tracking and parameter errors are introduced, and simulation results for three aggregate urban traffic regions are presented. [ABSTRACT FROM AUTHOR]

Governments' net zero emission target aims at increasing the share of renewable energy sources as well as influencing the behaviours of consumers to support the cost-effective balancing of energy supply and demand. These will be achieved by the advanced information and control infrastructures of smart grids which allow the interoperability among various stakeholders. Under this circumstance, increasing number of consumers produce, store, and consume energy, giving them a new role of prosumers. The integration of prosumers and accommodation of incurred bidirectional flows of energy and information rely on two key factors: flexible structures of energy markets and intelligent operations of power systems. The blockchain and artificial intelligence (AI) are innovative technologies to fulfil these two factors, by which the blockchain provides decentralised trading platforms for energy markets and the AI supports the optimal operational control of power systems. This paper attempts to address how to incorporate the blockchain and AI in the smart grids for facilitating prosumers to participate in energy markets. To achieve this objective, first, this paper reviews how policy designs price carbon emissions caused by the fossil-fuel based generation so as to facilitate the integration of prosumers with renewable energy sources. Second, the potential structures of energy markets with the support of the blockchain technologies are discussed. Last, how to apply the AI for enhancing the state monitoring and decision making during the operations of power systems is introduced. • The use of blockchain and artificial intelligence for facilitating prosumers to be integrated into smart grids and decarbonising the power systems. • Review how policy designs price of carbon emissions to facilitate the integration of prosumers with renewable energy sources. • Future energy markets with blockchain technologies. • How to apply artificial intelligence technologies for enhancing the control and decision-making in smart grids. [ABSTRACT FROM AUTHOR]

Abstract: To meet the safety requirement for the increasing traffic densities nowadays, there exists a growing demand for advanced systems that can provide drivers essential traffic and travel information to improve road safety and traffic efficiency. In this paper, we combine the video analysis and multimedia networking technologies to present a highly integrated intelligent system that can achieve the above goals. For traffic information, the system presented in this paper collects traffic parameters and detects relevant events by analyzing traffic surveillance videos. Through robust tracking algorithms and reasoning logics, important traffic parameters and events are extracted from the surveillance videos accurately. Afterwards, summarized real-time traffic conditions and important events along with corresponding live traffic videos are formed into layers and multicasted through an integration of WiMAX infrastructure and vehicular ad hoc networks (VANET). By the support of adaptive modulation and coding in WiMAX, the radio resources can be optimally allocated when performing multicast so as to dynamically adjust the number of data layers received by users. In addition to multicast supported by WiMAX, we also design a knowledge propagation and information relay scheme by VANET. Through this relaying technology, about 80% of the mobile stations that were unable to subscribe additional layers of data due to insufficient downlink bandwidth from WiMAX could regain more than 90% of the data in the additional layers within tolerable buffering time. [Copyright &y& Elsevier]

Abstract: A grid is an infrastructure for resource sharing. At present, many scientific applications require high computing power in processing, which can only be achieved by using the computational grid. For the selection and allocation of grid resources to current and future applications, grid job scheduling is playing a very vital role for computational grids. They constitute the building blocks for making grids available to the society. The efficient and effective scheduling policies, when assigning different jobs to specific resources, are very important for a grid to process high computing intensive applications. This paper presents an agent based job scheduling algorithm for efficient and effective execution of user jobs. This paper also includes the comparative performance analysis of our proposed job scheduling algorithm along with other well known job scheduling algorithms considering the quality of service parameters like waiting time, turnaround time, response time, total completion time, bounded slowdown time and stretch time. We also conducted the QoS based evaluation of the scheduling algorithms on an experimental computational grid using real workload traces. Experimental evaluation confirmed that the proposed grid scheduling algorithms posses a high degree of optimality in performance, efficiency and scalability. This paper also includes a statistical analysis of real workload traces to present the nature and behavior of jobs. [Copyright &y& Elsevier]

Abstract: The computing world is now populated with smartphones which combine the features of a phone with a general purpose computer and come loaded with sensors including digital cameras, global positioning system (GPS) receivers, accelerometers and many more. In this paper we argue that these devices are an ideal platform for collecting data for use in urgent computing simulations. We describe how these devices will have far reaching impacts on how people connect with and organize their communities and discuss how this coincides with the rise of community driven response to disasters and the need for decentralized command and control that is discussed in disaster management literature. We also that smartphones, by providing technology which can network people without the use of centralized infrastructure, and which are carried, used, and maintained as part of daily life, are a promising platform for building distributed disaster management applications, which could be part of the inputs provided to an urgent computing simulation. In this paper we describe not only the potential of the platform, but also analyze the challenges it faces in order to realize that potential, and discuss how our middleware is designed to meet these challenges and bring about the future of disaster management applications for smartphones using urgent computing. [Copyright &y& Elsevier]

• A web-based software for crash data collection, processing and analysis is presented. • The software is designed for mobile and desktop electronic devices. • Crash data before and after the use of the software in the city of Vico Equense, Italy were compared. • The time saving was more than one hour per crash, i.e., a 36% reduction. • The most valuable benefit was the reduction of the police officers mistakes. First step of the development of an effective safety management system is to create reliable crash databases since the quality of decision making in road safety depends on the quality of the data on which decisions are based. Improving crash data is a worldwide priority, as highlighted in the Global Plan for the Decade of Action for Road Safety adopted by the United Nations, which recognizes that the overall goal of the plan will be attained improving the quality of data collection at the national, regional and global levels. Crash databases provide the basic information for effective highway safety efforts at any level of government, but lack of uniformity among countries and among the different jurisdictions in the same country is observed. Several existing databases show significant drawbacks which hinder their effective use for safety analysis and improvement. Furthermore, modern technologies offer great potential for significant improvements of existing methods and procedures for crash data collection, processing and analysis. To address these issues, in this paper we present the development and evaluation of a web-based platform-independent software for crash data collection, processing and analysis. The software is designed for mobile and desktop electronic devices and enables a guided and automated drafting of the crash report, assisting police officers both on-site and in the office. The software development was based both on the detailed critical review of existing Australasian, EU, and U.S. crash databases and software as well as on the continuous consultation with the stakeholders. The evaluation was carried out comparing the completeness, timeliness, and accuracy of crash data before and after the use of the software in the city of Vico Equense, in south of Italy showing significant advantages. The amount of collected information increased from 82 variables to 268 variables, i.e., a 227% increase. The time saving was more than one hour per crash, i.e., a 36% reduction. The on-site data collection did not produce time saving, however this is a temporary weakness that will be annihilated very soon in the future after officers are more acquainted with the software. The phase of evaluation, processing and analysis carried out in the office was dramatically shortened, i.e., a 69% reduction. Another benefit was the standardization which allowed fast and consistent data analysis and evaluation. Even if all these benefits are remarkable, the most valuable benefit of the new procedure was the reduction of the police officers mistakes during the manual operations of survey and data evaluation. Because of these benefits, the satisfaction questionnaires administrated to the police officers after the testing phase showed very good acceptance of the procedure. [ABSTRACT FROM AUTHOR]

• This study explores the applicability of univariate and two-stage approaches for estimating crash frequency by severity. • The prediction accuracies of univariate and two-stage severity distribution function (SDF) methods were found to be similar. • The results highlight the potential of the two-stage SDF approach at least for curved two-lane road sections. • The two-stage SDF approach can be especially a useful method for the all too frequent cases where samples are too small. The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models. [ABSTRACT FROM AUTHOR]

Abstract The evolution of smart city services and applications requires a more efficient wireless infrastructure to provide the needed data rate to users in a high-density environment with high mobility, satisfying at the same time the request for high-connectivity and low-energy consumption. To address the challenges in this new network scenario, we propose to opportunistically rely on the increasing number of connected vehicles in densely populated urban areas. The idea is to support the macro base station (BS) with a secondary communication tier composed of a set of smart and connected vehicles that are in movement in the urban area. As a first step towards a comprehensive cost-benefit analysis of this architecture, this paper considers the case where these vehicles are equipped with femto-mobile access points (fmAPs) and constitute a mobile out-of-band relay infrastructure. We first study this network system with a continuous time model, in which three techniques to select an fmAP (if more than one is available) are proposed and the maximal feasible gain in the data rate is characterized as a function of the vehicle density, average vehicle speeds, handoff overhead cost, as well as physical layer parameters. We then introduce a time slotted model, in which we consider a more realistic communication channel, with an exponential path loss model, and we investigate the tradeoff between energy consumption and expected data rate, as a function of the system parameters. The analytical and simulation results, with both the continuous and time slotted models, provide a first benchmark characterizing this architecture and the definition of guidelines for its future realistic study and implementation. [ABSTRACT FROM AUTHOR]

Abstract: Several global developments (e.g. diminishing production resources, growing demand for bio-energy) and numerous sector-wide crises (e.g. BSE, swine fever, dioxin) have led to a changing attitude of society towards the consequences of the food systems activities for social, economic and environmental issues, captured in the term of sustainability. Consumers in their role as final customers, and as a consequence also enterprises within agri-food supply chains, show increasing interest in the characteristics of food, and in turn, on the availability of related information and guarantees. Enterprises in agri-food supply chains are facing new expectations and are seeking to communicate social, economic and environmental performance of their business to customers within their supply chain and to consumers. New solutions for determination and communication of sustainability are needed for the agri-food sector, covering single aspects of sustainability as well as sustainability in a broader sense, including social, economic and environmental issues. Integrated, computer-based information services, since they are mainly building on existing systems, provide flexible solutions for enterprises to measure and evaluate sustainability of products throughout agri-food supply chains. Gained information on product characteristics might be used for decision support within enterprises as well as for communication of sustainable practices to customers and consumers, resulting in increased competitiveness of enterprises, supply chains and the sector by satisfying customers’ and consumers’ demands for information on the sustainability of a product. The present paper introduces a framework for developing information services for European pork chains using the three selected information domains food safety (representing social sustainability), quality (representing economic sustainability) and global warming potential (representing environmental sustainability). Resulting information models give an aggregated overview on information availability and exchange in European pork chains, additional information demands of possible service users and deficiencies in the existing information infrastructures. The paper supports different stakeholders involved in agri-food production, such as service developers, enterprise decision makers and management consultants, in developing enterprise- and chain-specific solutions which meet customers’ and consumers’ demands by providing appropriate sustainability information and guarantees. [Copyright &y& Elsevier]

Abstract: In many organizations, information system and IT infrastructure have been considered as the most strategic capital and valid information is an important factor leads to business success. In this paper we investigate IT effects on project risk management which is vital in any industrial and business activities as a continuous and forecasting process. Risk management involves identifying the potential risks, measuring, monitoring and controlling them in an organization to meet its strategies and objectives and causes to decrease the undesired effects in project life cycles. It also has a main rule in strategic management of the organization by controlling the threads. In this paper we have estimated the effects of IT usage on 50 Iranian oil companies by analyzing the data which have been provided by questionnaires. Three different indices-cost, time, and performance-have been utilized to evaluate information technology impact on risk management. Our results illustrate the positive impact of information technology on risk management especially in optimizing time of process rather than cost and performance. [Copyright &y& Elsevier]

Abstract: The significant impact of e-commerce (EC) on the livelihood or rural populations in developing countries like Iran has made this topic of popular interest to many researchers in the past decade. To take advantage of e-commerce, employing suitable models which are adaptive to the circumstances of villages in rural areas is indispensable. Iran is on track for achieving this goal, development of EC in Rural areas. Considering the importance of sharing rural ICT experiences, the trend and experiences of the Rural EC infrastructure in Iran are demonstrated in this paper. According to our research, Iran’s rural ICT network development started in 2000 with the far northern village of Shahkooh which is known as the first multi-media center of Iran. In 2004, Iran national strategic plan of Rural ICT built two well-equipped telecentres near the villages of East Livan and Gharnabad. Taking availability of data and the duration of operation al time to account, these two telecenters were selected for this paper. In 2005, UNESCO Tehran Cluster Office was empowered to carry out a study on the economic and social effect of rural ICTs to share with others, acting in this field at regional and social levels. In fact, in order to find applicable and durable solutions for economic, social and environmental problems, these projects were carried out in the rural areas of Iran. In this paper, a practical model of e-commerce for rural areas of Iran is proposed. Our research is based on quantitative and qualitative methodologies. The qualitative methods comprised of open-ended interviews with officials and telecentre operators. The proposed model is related to the national project known as “10000 Rural ICT Center” which was started in the year 2004. In this project, Rural EC services are part of the IT application services at the Rural ICT Centers which supply four services; Communication Services, IT services, Postal Services and E-Banking services. [Copyright &y& Elsevier]

Abstract: In a MANET, mobile nodes must cooperate to dynamically establish routes using multi-hop wireless links. Some of the main challenges in this area related to routing protocols. These networks do not require any existing infrastructure or central administration. To make MANETs adaptive to different mobility and traffic patterns, this paper proposes a novel routing scheme which is utilized mobile agents and attempts to develop DSR protocol in MANETs with simple node-level management behavior resulting in overall system optimization. We develop a probabilistic multi-path routing algorithm and incorporates factors like signal strength into the route metrics so as to predict link breaks before they actually occur. in addition to signal strength and shortest-path metrics, our algorithm updates the goodness of choosing a particular path based on congestion measurement and energy level in each node. By All of the above improvements in DSR, the simulation results testify that our new Robust algorithm can be suitable in ad-hoc situations. We refer to the protocol as the Multi Agent based Adaptive DSR (MA-DSR). Finally, this paper proposes a new cross layer approach and some modifications in the MAC layer for RERR Analysis.. By this way, we can detect reason of RERR reception and apply proper mechanisms for performance improvement. We introduce yellow, red and black lists for decrease escaped, low energy, congested and insecure nodes chances in routing strategy. We expect that new condition aware MA-DSR, shows better performance. [Copyright &y& Elsevier]

Abstract Cloud robotics (CR) is a red-hot branch of the burgeoning field of service robots that is centered on the benefits of integrating infrastructure and shared services via a cloud computing environment. Although it extends the computation power and information sharing capabilities of the network robots, the development and operations (DevOps) of the CR system are currently limited for enterprise-scale projects due to the heavy framework. In fact, current developed CR systems are typical distributed monomer architectures followed by a "top-down" design. As the scale of the applications gets larger, the operation and maintenance of CR systems will become a very difficult task. In this paper, a new architecture for a microservice-based cloud robotics system in intelligent space is proposed to solve the present dilemma. To enable this, we design a service management architecture based on a microservice to provide a highly efficient and flexible development/deployment mechanism. The container technology based on the docker engine is then used to functionally decompose the application into a set of collaborating services to ensure the software design methods, based on microservice, easy for implementation. Finally, a real experiment on SLAM (Simulation localization and mapping) in an intelligent space is implemented to verify the proposed architecture. Compared with traditional monomer architectures, the results show that the proposed framework is more productive, flexible and cost effective. [ABSTRACT FROM AUTHOR]

“Progress in information technology and tourism management: 20 years on and 10 years after the Internet—The state of eTourism research” is reviewed in terms of its significance to academic literature linking Information and Communication Technologies (ICTs) and tourism. Ten years after its publication in 2008, we revisit this paper with a view to observing the main changes in eTourism over these years, analysing the strategic lines that are driving its evolution, and verifying the fulfilment of the tendencies anticipated by Buhalis and Law. Their diagnosis has been very accurate and, given the rapidity of the technological changes, it is appropriate to highlight the changes that this sector has experienced since then. [ABSTRACT FROM AUTHOR]

Analyzes the White Paper on the Intellectual Property Rights in the National Information Infrastructure and its impact on libraries and educational institutions. Concepts on library services, educational institutions and the public use of libraries; Emphasis on the rights of copyright owners.

Abstract: The JET Intershot Analysis (Chain1) generates processed data following a pulse. Maintaining the pulse repetition rate is one of JET''s key success factors, so performance of Chain1 is crucial. This paper will describe JET''s experience of managing Chain1, including a description of the control system used to ensure the analysis chain runs as quickly as possible, and a discussion of JET''s experience of integrating externally developed codes into a standard analysis framework. The current Chain1 infrastructure was developed in 1999 and although reliable and efficient is starting to prove costly in terms of flexibility and extensibility to meet JET''s current and future needs. For this reason JET is planning to re-implement the Chain1 system. The paper will outline the work done towards this aim, and present a model of the proposed new system. Finally, possible future steps towards an integrated data production chain for JET will be discussed, and the potential applicability to next generation fusion devices will be outlined. [Copyright &y& Elsevier]

• GTWOLR model is employed to explore the temporal and spatial variation of the factors affecting crash severity. • Bi-square kernel function and fixed bandwidth in the model can best adapt to crash data in the paper. • The effects of eight factors on the crash severity vary across space and over time. • Targeted and local traffic safety strategies for rural highways are proposed. Traffic crashes are the result of the interaction between human activities and different socio-economic, geographical, and environmental factors, showing a temporal and spatial relationship. The temporal and spatial correlations must be characterized in crash severity studies, for which the geographically and temporally weighted ordered logistic regression (GTWOLR) model is an effective approach. However, existing studies using the GTWOLR model only subjectively selected a type of kernel function and kernel bandwidth, which cannot determine the best expression of the spatiotemporal relationship between crashes. This paper explores the optimal kernel function and kernel bandwidth considering the aforementioned problem to obtain the best GTWOLR model to analyze the crash data based on the crash data of rural highways in Anhui Province, China, from 2014 to 2017. First, the GTWOLR models with Gaussian or Bi-square kernel function and fixed (the spatiotemporal distance remains constant of local sample) or adaptive (the quantity of the local sample is constant) bandwidth are compared. Second, the log-likelihood and Akaike information criterion are used to compare the GTWOLR model with the ordered logistic regression (OLR) model. Finally, the spatial and temporal characteristics of the contributing factors in the best GTWOLR model are analyzed, and corresponding countermeasures for improving traffic safety on rural highways are proposed. Model comparison results reveal that although the difference was insignificant, the Bi-square kernel function with fixed bandwidth (BF)- GTWOLR model has a better goodness of fit than the GTWOLR models with other types of kernel function and bandwidth and the OLR model. The BF-GTWOLR model estimation results showed that eight factors, including pedestrian-vehicle crash, middle-aged driver, hit-and-run, truck, motorcycle, curve, slope and mountainous, passed the non-stationary test, indicating their varying effects on the crash severity across space and over time. As a crash severity modeling approach that effectively quantifies the spatiotemporal relationships in crashes, the BF-GTWOLR model, which adapts to crash data, may have implications for future research. In addition, the findings of this paper can help traffic management departments to propose progressive and targeted policies or countermeasures, so as to reduce the severity of rural highway crashes. [ABSTRACT FROM AUTHOR]

The striving of nations to build a broadband-based information society is paralleled by individuals striving to improve their personal lot and by organisations seeking a competitive advantage--all supposedly to be achieved via broadband. However, the social and economic impact of broadband may be surrounded in controversies. This paper argues that these controversies need to be identified and addressed if public policy decisions about broadband are to be made with confidence. While literature on broadband abounds, it is rarely directed at the impact of broadband on social and personal issues. Rather, that literature typically focuses on broadband's economic aspects, but with poor benefit identification and measure difficulties, the findings tend to be steeped in rhetoric. Largely, what we are left with is literature on the impact of narrowband Internet. While research on the Internet abounds, its findings are typically indeterminate and often disputed. The paper works with literature on broadband and extrapolates from the literature on narrowband with its disputed impacts to identify possible impacts of broadband. In so doing it identifies a core set of controversies regarding broadband at the national, individual and organisational levels. It then calls for diversity of analysis of possible broadband outcomes. Only with such analysis will it be possible to place the likely broadband future in the context of broader national, individual and organisational aspirations. [ABSTRACT FROM AUTHOR]

Telecommunications access in the rural and remote areas of Australia has come under considerable scrutiny in recent years. In the past decade, various Federal Government reports and inquiries have citied inadequate infrastructure, lack of service provision, the high cost of access and "thin" markets as key impediments. Whilst these difficulties are not restricted to rural and remote Australia, they tend to impact disproportionately on regional communities, notably in education and health service provision. Particularly at risk are the indigenous communities in rural and remote regions of Australia. In this paper telecommunications infrastructure is perceived as the lynch-pin for achieving sustainable economic and social development. It is suggested that without appropriate development of information infrastructure, the disparities already experienced by rural and remote communities will be further exacerbated as the reliance of goods and services over computer-mediated networks increases. The paper offers a new paradigm that will assist in maintaining a competitive telecommunications environment and facilitate the sustainable social and economic development of rural and remote communities. [ABSTRACT FROM AUTHOR]

Co-modality transportation aims to integrate passenger and freight flows into a single system by sharing public transport between passengers and freight. This paper proposes a crowdsourced co-modality transportation system (CCTS) for enhancing the practical implementation of co-modality transportation in urban areas. Firstly, empirical investigations are conducted, including semi-structured interviews with industry practitioners and online surveys with passengers, to collect system design considerations. Secondly, an overview of the crowdsourced co-modality management platform (CCMP) is presented by highlighting three key components: co-modality services, information infrastructure, and decision support system. Thirdly, we formulate the parcel assignment problem in the CCTS and examine the factors that influence the performance of the CCTS. The empirical investigations suggest that crowdsourced passengers care about the potential risks and the degree of operation difficulty in crowdsourced activities but do not care about privacy issues. In addition, the experimental studies show that crowdsourced co-modality is more feasible and promising if more passengers are willing to provide crowdsourcing services. [ABSTRACT FROM AUTHOR]

Accurately predicting the highway traffic speed can reduce traffic accidents and transit time, which is of great significance to highway management. Three essential elements should be considered in the long-term highway traffic speed prediction: (1) adaptability to speed fluctuation, (2) exploring the spatio-temporal correlation effectively, and (3) prediction of non-error propagation. This paper proposes a novel spatio-temporal generative inference network (STGIN) driven by data and long-term prediction. STGIN consists of three parts; semantic enhancement, spatio-temporal correlation extraction block (ST-Block), and generative inference. Semantic enhancement is first used to model the contextual semantics of traffic speed, improving the adaptability to speed fluctuations. The ST-Block is then used to extract the spatio-temporal correlations of the highway network. Finally, generative inference is used to pay attention to the correlation between historical- and target-sequences to generate the target hidden outputs rather than a dynamic step-by-step decoding way; it avoids long-term prediction error propagation in the spatial and temporal dimensions. The evaluation experiments use the monitoring data of highway in Yinchuan City, Ningxia Province, China. For long-term highway speed prediction, the experimental results demonstrate that the performance of the proposed method is better than that of the baseline methods. • The review highlights model prediction performance improvement for long-term highway traffic speed prediction. • STGIN considers the traffic speed fluctuation on the highway network. • STGIN effectively solves dynamic spatio-temporal correlations extraction. • STGIN conquers the prediction error propagation in spatial and temporal dimensions. [ABSTRACT FROM AUTHOR]