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Tourism consumption as a typical representative of service consumption, has strong comprehensiveness and driving force, continuously expanding new consumption upgrade space. In the context of the digital economy, the development of the mobile internet has opened up new opportunities for the tourism industry acting as an important factor influencing the consumer behaviour of the residents. Based on 9007 resident survey data from the China General Social Survey (CGSS) in 2017, this paper explores the relationship between Internet use, access to information and residents' travel consumption behavior by means of probit model and mediating effect model. First, according to the findings, Internet use has a positive impact on residents' travel consumption behavior, increasing residents' tourism expenditure. Second, the mechanism suggests that access to information plays a partially mediating role between Internet use and travel consumption behavior. Third, further analysis revealed that, for the retired population, the more frequently they use the Internet, the more likely they are to spend on travel. Compared to those with lower incomes, those with higher incomes will spend less on travel after retirement. With this in mind, in order to promote residents' tourism consumption, speed and fee reduction should be continuously promoted, urban and rural information infrastructure should be strengthened, and accessibility of tourism information for residents should be improved. At the same time, there is a need to innovate the way tourism products are promoted, improve the types of tourism products and launch diversified tourism products. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper will investigate the emergence of a digitalising state in the global south through a focus on new techniques of governance initiated by the information age. It will discuss the mechanisms of the digitalising state across two realms – the governance of information infrastructures evident in the transition from paper to digital data, and the governance of informational peripheries emerging across digital and territorial exclusions. The paper argues that through these dynamics, the digitalising state is engaged in a politics of digitalisation-as-urbanisation where digitalisation becomes both a product and producer of regional urbanisation. [ABSTRACT FROM AUTHOR]

Currently, China has entered a stage of high-quality development due to innovations and improving capabilities of the cities. Being the most valuable urban brand, civilized cities are a symbol of the city's comprehensive ability. Their construction blueprint coincides with the realization of high-quality development. Therefore, exploring the relationship between civilized cities' construction and urban innovative development is a matter of great significance for improving national high-quality development policies and strengthening civilized cities' construction. This paper examined the data from 242 cities from 2003 to 2016, applying the multi-period difference-in-difference model to test the established hypothesis. The results indicate that the pilot policy for civilized cities has effectively improved the level of urban innovation. The results also validate that the title of the civilized city effectively promotes the urban innovation level by promoting the agglomeration of human capital, improving the level of informatization, and increasing investment in scientific as well as technological innovation. At the same time, there are significant differences in policy effectiveness for cities with different urban levels and geographical locations. The construction of future civilized cities should focus on attracting and cultivating high-quality talents, strengthening investment and support in information infrastructure construction, and scientific and technological innovation research and development to adapt to local conditions and resultantly achieve the improvement in urban innovation level. [ABSTRACT FROM AUTHOR]

Environmental protection supervision is an important manifestation of ecological accountability system reform. Taking the implementation of China's central environmental protection inspection (CEPI) policy as a quasi-natural experiment, this paper applies the time-varying difference-in-difference (DID) model to identify the impact of the CEPI policy on the likelihood of enterprises' environmental violations. The main conclusions include: (1) the implementation of CEPI policy is conducive to inhibiting environmental violations. After a series of robustness tests, including the parallel trend test, the placebo test, the exclusion of policy interference, and the replacement of explained variables, the result remains robust. In terms of dynamic perspective, there is a lag in the impact of CEPI policy on enterprises' environmental violations. (2) The effect of CEPI policy is more prominent on samples of large enterprises, enterprises with highly concentrated equity, enterprises that polluted heavily, enterprises owned by executives with a political background, and enterprises that are situated in regions with higher level of marketization and more developed information infrastructure. (3) The CEPI policy can restrain the environmental violations of enterprises by a dual mechanism of releasing punishment and deterrence signals from the external governance, and forcing enterprises to enhance environmental management system from the internal governance. [ABSTRACT FROM AUTHOR]

This paper discusses three case studies of early science museum-related websites in the 1990s and early 2000s, when web technology was still relatively new and evolving. The Virtual Museum of Computing (VMoC) was a completely virtual museum, originally produced in 1995 as part of the Virtual Library museums pages (VLmp), an international online museum directory within the WWW Virtual Library, adopted by the International Council of Museums (ICOM). The Science Museum in London was one of the first museums in the United Kingdom to have its own web server. The museum hosted an early meeting on web service provision by and for museums, concurrently with an exhibition on the Information Superhighway at the museum in 1995. Exhiblets were launched online in 1998. Ingenious was a multi-site digital collections transformation project, launched as a website in 2003. Virtual Leonardo and Leonardo's Ideal City were two experiments conducted by the digital team of the Science and Technology Museum of Milan, between 1999 and 2001. The experiment consisted of the creation of a shared online 3D world, namely a reconstruction of the real museum in the first case and a completely imaginary world in the second case. This paper describes the above three case studies from the early World Wide Web and then draws some conclusions, from first-hand experience of developments at the time. We cover both the advantages and the challenges encountered by the various projects and illustrate why they did not necessarily become established, despite promising early results. [ABSTRACT FROM AUTHOR]

Background: This paper explores the feasibility of establishing a large-scale population-based cohort and biobank in Switzerland by assessing potential participants' needs, expectations, and concerns about such an infrastructure providing information on health, lifestyle, and exposure trajectories, the development of disease, and risk factors over time. Methods: We utilized a scenario-based questionnaire in the Swiss Health Study pilot phase (2020–2021), involving 1349 adults aged 20–69 from the cantons Vaud and Bern. We conducted descriptive statistics supported by R and qualitative content analysis of n = 374 open responses related to attitudes towards research. Results: We highlight the benefits and challenges of the scenario-based approach, discuss the sample represented in the pilot phase, and present implications for building a full cohort. We also report on participants' attitudes towards and previous experience with health research. We analyze references regarding informed consent and feedback, attitudes towards the Swiss Health Study, and recommendations on improving its scope, design, and instruments. Results indicate a high interest (90%) in participating in a national health study, with 85% of a random population sample willing to join a long-term cohort. Only 43% were familiar with biobanks, and 44% preferred general consent. Trust was high for Swiss-based public research but lower for researchers from other countries or private sector. Over 95% expressed willingness to complete online questionnaires, undergo physical examination, and donate biosamples. Almost all participants wanted to know the outcomes of the medical tests (99.5%) and the exposure to environmental stressors (95%) from their study center visit. Preferred tools for monitoring sleep, physical activity, and diet were known smartphone apps with automatic data management. Conclusion: Overall, the study reveals a positive attitude towards personalized health research, with a strong willingness to share data and samples. Key insights focus the meaning of informed consent for participation, the relevance of sampling and representativeness, as well as the significance and challenges of personalized feedback, especially regarding environmental health concerns. Findings emphasize participants' supportive yet reflexive stances, underscoring the importance of aligning research values with individual values in personalized health research. These insights contribute valuable considerations for refining the scope, design, and instruments of future cohort studies. [ABSTRACT FROM AUTHOR]

This study specifically set to identify the status and extent of utilization of Blockchain technology in the delivery of construction projects in Imo State, Nigeria. The study was guided by the technology acceptance model (TAM) theory. The study used a descriptive research design method of investigation, and a simple random sampling technique was used to select a sample size of 123 from a population of 194 practitioners using the Krejcie and Morgan's method of sample size determination. The data collection and survey instrument included a well-structured questionnaire, discussions, personal observations and visits to elicit information from respondents/construction project locations. The collected data was presented in the form of frequency distribution, figures, and charts using descriptive statistical tools using IBM SPSS Statistics version 25.0. The study's findings indicate a general low level of utilization/application of Blockchain technology in the delivery of construction projects in Imo state. This is due to the fact that none (0.00%) of the respondents have heard of Blockchain technology as applicable to construction projects-related activities, 9 (10.47%) have used Blockchain technology sparingly, 35 (40.70%) have never heard of Blockchain technology and 42 (48.84%) have heard of Blockchain technology, but never applied it on any construction project-related activity out of the eighty-six (86) responses used for the study. This study recommends that, a united front is needed to make Blockchain technology awareness-raising and encourage practitioners to embrace it. Blockchain can provide a dependable infrastructure for information management throughout all stages of the construction project life cycle as well as secure storage of sensitive sensor data. [ABSTRACT FROM AUTHOR]

Purpose: Information infrastructures can enable or constrain how companies pursue their visions of sustainability reporting and help address the urgent need to understand how corporate activity affects sustainability outcomes and how socio-ecological challenges affect corporate activity. The paper examines the relationship between sustainability reporting information infrastructures and sustainability reporting practice. Design/methodology/approach: The paper mobilises a socio-technical perspective and the conception of infrastructure, the socio-technical arrangement of technical artifacts and social routines, to engage with a qualitative dataset comprised of interview and documentary evidence on the development and construction of sustainability reporting information. Findings: The results detail how sustainability reporting information infrastructures are used by companies and depict the difficulties faced in generating reliable sustainability data. The findings illustrate the challenges and measures undertaken by entities to embed automation and integration, and to enhance sustainability data quality. The findings provide insight into how infrastructures constrain and support sustainability reporting practices. Originality/value: The paper explains how infrastructures shape sustainability reporting practices, and how infrastructures are shaped by regulatory demands and costs. Companies have developed "uneven" infrastructures supporting legislative requirements, whilst infrastructures supporting non-legislative sustainability reporting remain underdeveloped. Consequently, infrastructures supporting specific legislation have developed along unitary pathways and are often poorly integrated with infrastructures supporting other sustainability reporting areas. Infrastructures developed around legislative requirements are not necessarily constrained by financial reporting norms and do not preclude specific sustainability reporting visions. On the contrary, due to regulation, infrastructure supporting disclosures that offer an "inside out" perspective on sustainability reporting is often comparatively well developed. [ABSTRACT FROM AUTHOR]

This paper explores the dynamics of peer support and companionship among older adults on a social networking site during the COVID-19 lockdown. Drawing from the authors' five-month experience as volunteer facilitators and a qualitative study involving users, social workers, and managers, the paper examines two modes of online peer support and companionship: one based on voice messages, the other on visual messages. Guided by critical media and data studies, and incorporating concepts from cultural studies of mobile media and information infrastructure studies, the analysis highlights the interplay of relational/emotional and infrastructural work and uncovers intricate gendered and age-related configurations. Our conclusion emphasises, first, the need to comprehend how socio-technical systems shape emotional, relational, and infrastructural work during emergency digital interventions, and second, the importance of examining how specific notions of support and older people's agency and response-ability are embedded in the socio-technical organisation of these digital interventions. [ABSTRACT FROM AUTHOR]

Identifying roads' hazardous locations and solving their problems are key measures in traffic safety management. However, since the traditional hotspot identification (HSID) rests on the yearly-aggregated crashes, two problems appear: locations that become unsafe at specific short periods may remain unidentified as they may not show noticeable crash counts, and results of the problem diagnosis analysis on hotspots' crashes potentially contain a great amount of uncertainty. Even though researchers have recently added the dimension of time and analyzed accidents spatio-temporally to obtain more insights, the mentioned problems have not been addressed fully. Hence, this paper first suggests a new linear DBSCAN-based HSID method and demonstrates its acceptable performance by comparison with KDE+, the well-known clustering technique; Second, employing the proposed technique, the paper presents an algorithm for the spatial analysis of accidents through diverse time dimensions, which categorizes the risky locations based on their periodic reappearance. The tempo-categorization purpose is to enhance diagnosing causative risks by understanding their arising periods. The algorithm is tested using Allegheny highways crash data from 2014 to 2019. Results illustrate the contribution of the suggested method to problem diagnosis and detecting hidden unsafe points. [ABSTRACT FROM AUTHOR]

The analysis of influencing factors serves as the cornerstone for the research on smart community construction. Drawing upon both field research and extensive literature study, this paper explores the influencing factors of China's smart community construction and its effective paths by taking 52 national pilot zones for community governance and service innovation in China as examples. In the constructed analytical framework of influencing factors, elements such as economic development, capital investment, information infrastructure, community governance, public support system, and smart platform are included. By the use of the qualitative comparative analysis (QCA) method, the results of the study show that community governance, public support system, and smart platform are necessary conditions for smart community construction, while economic development, capital investment and information infrastructure play a leading role in the four combined paths. Finally, this study provides a new perspective for theoretical research, a reference forgovernmental departments to make decisions, and experience for the construction of smart communities in other developing countries. [ABSTRACT FROM AUTHOR]

The theoretical impact of the electric vehicle (EV) market share growth has been widely discussed with regards to technical and socioeconomic aspects in recent years. However, the prospection of EV scenarios is a challenge, and the difficulty increases with the granularity of the study and the set of variables affected by user behavior and regional aspects. Moreover, the lack of a robust database to estimate fast-charging stations' load curves, for example, affects the quality of planning, allocation, or grid impact studies. When this problem is evaluated on highways, the challenge increases due to the reduced number of trips related to the reduced number of charger units installed and the limited EVs range, which influence user anxiety. This paper presents a methodology to estimate the highway fast-charging station operation condition, considering regional and EV user aspects. The process is based in a block of traffic simulation, considering the traffic information and highway patterns composing the matrix solution model. Also, the output block estimates charging stations' operational conditions, considering infrastructure scenarios and simulated traffic. A Monte Carlo simulation is presented to model entrance rates and charging times, considering the PDF of stochastic inputs. The results are shown for the aspects of load curve and queue length for one case study, and a sensibility study was conducted to evaluate the impact of model inputs. [ABSTRACT FROM AUTHOR]

Background: The global concern for the environment and sustainable development goals has led to a growing focus on the increasing activities of city logistics. This research study aims to evaluate the current state of city logistics within the framework of sustainable development and analyze the key factors that impact the efficient management of city logistics in Jordan. Methods: To achieve these research objectives, a quantitative approach utilizing a survey questionnaire was employed. The findings emphasize a significant disparity between current practices in city logistics and desired outcomes, indicating an urgent need for improvement in cost reduction, enhancement of quality of life, and promotion of sustainability. Results: The results reveal that the performance of urban authorities in managing city logistics is influenced by various factors, including regulatory inefficiencies, suboptimal human resource performance, deficiencies in information systems, and challenges related to coordination amongst stakeholders. Conclusions: Consequently, this paper puts forth actionable recommendations for improving city logistics management, which involve amending existing regulations, implementing comprehensive training programs for employees, strengthening infrastructure for information systems, and fostering robust communication channels between urban authorities and stakeholders. [ABSTRACT FROM AUTHOR]

The road agencies collect and submit weigh-in-motion (WIM) data to the Federal Highway Administration as part of their traffic monitoring program. Therefore, the WIM data should be precise and accurate. One way to evaluate WIM measurement errors is by using the test truck data collected immediately before and after equipment calibration. The limitation of this approach is that the data represent a snapshot in time and may not represent a long-term WIM site performance. This paper presents an approach for estimating WIM system accuracy based on axle load spectra attributes (normalized axle load spectra (NALS) shape factors). This alternative approach allows for characterizing temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to Class 9 NALS shape factors in the proposed methodology. This paper aims to determine WIM system errors based on axle loading without physically performing WIM equipment performance validation using test trucks. The presented methodology can be used to estimate systematic errors (drift) in the WIM system at any point in time after the equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the single axle (SA) and tandem axle (TA) can be estimated with SA and TA NALS shape factors with an acceptable degree of error for bending plate to quartz piezo sensors. Examples are included to demonstrate the application and significance of the developed models. [ABSTRACT FROM AUTHOR]

Green technology innovation is an effective means to achieve high-quality economic development. The impact and mechanism of digital financial inclusion on regional green technology innovation are tested using a threshold regression model and the panel fixed effect model, based on China's provincial Panel data (provincial Panel data are regional annual report data) from 2011 to 2020. According to the study, there is a direct link between local green technology innovation and digital financial inclusion. This paper highlights the differences in their influence by location and usage depth and underscores the necessity of government engagement to improve these characteristics. Information infrastructure needs to be strengthened, especially in areas with gaps. Greater investment in research and development (R&D) indirectly supports regional green technology innovation since it is impacted by digital financial inclusion. Interestingly, a threshold effect becomes most noticeable when digital financial inclusion rises above a particular threshold. Promoting utilizing digital financial inclusion to lessen regional differences in green technology innovation is important. [ABSTRACT FROM AUTHOR]

Based on the data from two field surveys in 2015 and 2022, this paper calculates the weight of values using the entropy weight method and the variation coefficient method, and evaluates risk using the information quantity method. The information quantities of four levels of criteria (hazards, exposure, vulnerability, emergency responses, and capability of recovery) were extracted and inputted into a random forest model. After optimizing the hyperparameters of the random forest using GridSearchCV, the risk assessment was performed again. Finally, the accuracy of the two evaluation results was verified using an ROC curve, and the model with the higher AUC value was selected to create a risk map. Compared with previous studies, this paper considers the factors of emergency responses and recovery capability, which makes the risk assessment more comprehensive. Our findings show that the evaluation results based on the coupling model are more accurate than the evaluation results of the information method, as the coupling model had an AUC value of 0.9329. After considering the indices of emergency responses and capability of recovery, the risk level of the highest-risk area in the study area decreased. [ABSTRACT FROM AUTHOR]

Revenue risk is the most critical factor that hampers the long-term viability of highway infrastructure service projects. This paper develops an integrated multi-method decision framework to evaluate and compare multiple revenue risk mitigation strategies for build-operate-transfer (BOT) highway infrastructure concessions. Using the project data of a real highway BOT case project in India, this paper models and evaluates various risk mitigation strategies on different performance metrics using Monte Carlo simulation analysis. The technical efficiencies of the risk mitigation strategies are estimated using data envelopment analysis (DEA) and efficient strategies are identified. The findings suggest that strategies comprising appropriate combinations of minimum revenue guarantee, excess revenue share, and revenue shortfall loan mitigate the revenue risk most efficiently. The decision framework and the findings facilitate efficient public infrastructure contract design by policymakers and set multiple research directions. [ABSTRACT FROM AUTHOR]

The problem of detecting communities in real-world networks has been extensively studied in the past, but most of the existing approaches work on single-domain networks , i.e. they consider only one type of relationship between nodes. Single-domain networks may contain noisy edges and they may lack some important information. Thus, some authors have proposed to consider the multiple relationships that connect the nodes of a network, thus obtaining multi-domain networks. However, most community detection approaches are limited to multi-layer networks , i.e. networks generated from the superposition of several single-domain networks (called layers) that are regarded as independent of each other. In addition to being computationally expensive, multi-layer approaches might yield inaccurate results because they ignore potential dependencies between layers. This paper proposes a multi-domain discrete-time quantum walks (MDQW) model for multi-domain networks. First, the walking space of network nodes in multi-domain network is constructed. Second, the quantum permutation circuit of the coin state is designed based on the coded particle state. Then, using different coin states, the shift operator performs several quantum walks on the particles. Finally, the corresponding update rule is selected to move the node according to the measurement result of the quantum state. With continuous update iteration, the shift operator automatically optimizes the discovered community structure. We experimentally compared our MDQW method with four state-of-the-art competitors on five real datasets. We used the normalized mutual information (NMI) to compare clustering quality, and we report an increase in NMI of up to 3.51 of our MDQW method in comparison with the second-best performing competitor. The MDQW method is much faster than its competitors, allowing us to conclude that MDQW is a useful tool in the analysis of large real-life multi-domain networks. Finally, we illustrate the usefulness of our approach on two real-world case studies. [ABSTRACT FROM AUTHOR]

In the field of multimodal sentiment analysis, it is an important research task to fully extract modal features and perform efficient fusion. In response to the problems of insufficient semantic information and poor cross-modal fusion effect of traditional sentiment classification models, this paper proposes a composite hierarchical feature fusion method combined with prior knowledge. Firstly, the ALBERT (A Lite BERT) model and the improved ResNet model are constructed for feature extraction of text and image, respectively, and high-dimensional feature vectors are obtained. Secondly, to solve the problem of insufficient semantic information expression in cross-scene, a prior knowledge enhancement model is proposed to enrich the data characteristics of each modality. Finally, to solve the problem of poor cross-modal fusion effect, a composite hierarchical fusion model is proposed, which combines the temporal convolutional network and the attention mechanism to fuse the sequence features of each modality information and realizes the information interaction between different modalities. Experiments on MVSA-Single and MVSA-Multi datasets show that the proposed model is superior to a series of comparison models and has good adaptability in new scenarios. [ABSTRACT FROM AUTHOR]

The increasing access to cyberspace and its resources is having a significant impact on our societies and our daily lives, profoundly transforming the way we live, work and interact. The importance of cyber security converts into the need to keep information, data and devices private and secure. Cyber-security is an essential element for the flat functioning of all critical information infrastructures. In this context, the treatment of cyber-security in this paper is concrete, taking into account the cyberattack that our country went through, which was aimed at destroying critical structures and testing the capacities of Albania. It is worth noting that a secure cyber environment cannot function without giving it the opportunity to have the relevant mechanisms. Specifically, increasing cyber resilience in critical information infrastructures are flexible cyber security measures, which focus on the flat functioning of online public services keeping in focus the real threats and the risk management plan. Furthermore, within the framework of commitments as a candidate country of the European Union, Albania is meticulously implementing all European acts and international standards for the drafting of a consolidated and stable legal framework. In the present paper, through a legal assessment, special attention has been paid to the National Strategy for the period 2021-2023 and legal rules on cyber-security in the Albanian legislation, aiming to analyze the legal provisions, which regulate it in this regard. Also, an important objective of this paper is also the fact that it may serve as an important basis for further studies in this field. The analysis of the cyber-security legal regulation in Albania is based on the qualitative method, which contains also the research, analytical, descriptive, interpretive methods. The results of this paper, which treats an innovative topic, will stimulates debate in the academic level and contribute to the legal doctrine in Albania that lacks such. [ABSTRACT FROM AUTHOR]

Copyright of Bibliotheksdienst is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The period from the mid-1990s to the mid-2000s saw the transformation of information and communication infrastructure. In the same period, TPRC evolved from a narrower focus on conventional telecommunications and information policy to "The Research Conference on Communications, Information, and Internet Policy." Through the lens of my own interdisciplinary work on Internet policy and intersecting TPRC activity, this retrospective describes an arc of change that began at the 1994 TPRC and continued for about a decade. It combines description, commentary, and reflections on what this history might bode for TPRC as metaverses and Web3 progress from today's hype to tomorrow's Internet. [ABSTRACT FROM AUTHOR]

In this paper, the object of the research is 110 kV power grids of three regions of the Republic of Kazakhstan: Astana city, Turkestan region and Shymkent city, as well as West Kazakhstan region. Operators of the studied power grids have no idea about the real levels of voltage and current sinusoidality distortion coefficients, as well as about their relationship with other regime and system parameters of power grids. Similar problems may be faced by other grid companies that do not have the appropriate information and measurement infrastructure to monitor the modes of power grids in terms of voltage and current sinusoidality distortion. In the course of the present study, using portable three-phase power quantity and quality analyzers, it was possible to make daily measurements of mode parameters in 41 110 kV transmission lines with a length of 5 to 120 km. The results of measurements showed that in Astana city, the voltage quality is at a satisfactory level, but the distortion coefficient of sinusoidality of current reaches 39 % (the average level for 15 studied lines is 13.3 %) due to the high concentration of non-linear load of consumers. In the South of Kazakhstan, the voltage and current sinusoidality distortion coefficients are relatively moderate, but voltage drops are frequent (sometimes up to 10 % or more) due to the large distance between load centers and relatively high population density. In the power grids of Western Kazakhstan, voltage and current sinusoidality distortion coefficients have high levels (reach 14 % and 70 %, respectively) due to low network load with a large length of transmission lines. The analysis makes it possible to trace the relationship of voltage and current sinusoidality distortion coefficients with such regional characteristics as population density, nature of loads, power losses, voltage and current levels. [ABSTRACT FROM AUTHOR]

As a subversive concept, the metaverse has recently attracted widespread attention around the world and has set off a wave of enthusiasm in academic, industrial, and investment circles. However, while the metaverse brings unprecedented opportunities for transformation to human society, it also contains related risks. Metaverse is a digital living space with information infrastructure, interoperability system, content production system, and value settlement system as the underlying structure in which the inner core is to connect real residents through applications and identities. Through social incentives and governance rules, the metaverse reflects the digital migration of human society. This article will conduct an in-depth analysis of the metaverse from the perspective of electronic data forensics. First, from the perspective of Internet development, the background and development process of the metaverse is discussed. By systematically elaborating on the concept and connotation of the metaverse, this paper summarizes the different views of current practitioners, experts, and scholars on the metaverse. Secondly, from the perspective of metaverse security, the social risk and crime risks of the metaverse are discussed. Then the importance of metaverse forensics is raised. Third, from the perspective of blockchain, smart wearable devices, and virtual reality devices, the objects and characteristics of metaverse forensics have been studied in depth. Taking smart wearable devices as an example, this paper gives the relevant experimental process of smart bracelet forensics. Finally, many challenges faced by metaverse forensics are summarized by us which provide readers with some exploratory guidance. [ABSTRACT FROM AUTHOR]

Intelligence Everywhere is predicated on the seamless integration of INTERNET of Things (IoT) networks transporting a vast amount of data streams through many computing resources across an edge-to-cloud continuum, relying on the orchestration of distributed machine learning models. The result is an interconnected and collective intelligent ecosystem where devices, systems, services, and users work together to support IoT applications. This paper discusses the state-of-the-art research and the principles of the Intelligence Everywhere framework for enhancing IoT applications in vertical sectors such as Digital Health, Infrastructure, and Transportation/Mobility in the context of intelligent society (Society 5.0). It also introduces a novel perspective for the development of horizontal IoT applications, capable of running across various IoT networks while fostering collective intelligence across diverse sectors. Finally, this paper provides comprehensive insights into the challenges and opportunities for harnessing collective knowledge from real-time insights, leading to optimised processes and better overall collaboration across different IoT sectors. [ABSTRACT FROM AUTHOR]

While archival user studies have largely focused on humanities (and adjacent) scholars, this paper focuses on anthropologists engaged in scientific research. Based on qualitative results from an open-ended survey, we investigate how science-based anthropologists perceive and use archives in their work. We ask: How are science-based anthropologists and archaeologists reusing archival data in their research? What difficulties or barriers do they encounter in reusing archival data in scientific contexts? What attitudes or understandings about archival research are held by science-based anthropologists and archaeologists? Our findings primarily add to the body of literature about user experience in archives and more broadly to the emerging literature on archival data reuse. Major findings include (1) barriers and gatekeeping legacies that impact archival research and the ability of researchers to reuse data and (2) mixed perceptions about archives among researchers. We also discuss suggestions made by these communities of practice, and the ways that barriers to archival data reuse may stem from a lack of knowledge about core archival and information infrastructures among researcher communities. Together, this research showcases possible (re)uses of important primary source data in archives among scientific communities but highlights that barriers to access and misperceptions create a gap in exploiting that potential. We argue for a "re-imagining" of anthropological archives as relevant to contemporary communities and scientific pursuits toward a richer scientific research environment. [ABSTRACT FROM AUTHOR]

Due to the harsh environment of highway tunnels and frequent breakdowns of various detection sensors and surveillance devices, the operational management of highway tunnels lacks effective data support. This paper analyzes the characteristics of operational surveillance data in highway tunnels. It proposes a multimodal information fusion method based on CNN-LSTM-attention and designs and develops a digital twin for highway tunnel operations. The system addresses issues such as insufficient development and coordination of the technical architecture of operation control systems, weak information service capabilities, and insufficient data application capabilities. The system also lacks intelligent decision-making and control capabilities. The developed system achieves closed-loop management of "accurate perception-risk assessment-decision warning-emergency management" for highway tunnel operations based on data-driven approaches. The engineering demonstration application underscores the system's capacity to enhance tunnel traffic safety, diminish tunnel management costs, and elevate tunnel driving comfort. [ABSTRACT FROM AUTHOR]

Driver characteristics have been linked to the frequency and severity of car crashes. Among these, age and gender have been shown to impact both the possibility and severity of a crash. Previous studies have used standard ordered probit (OP) models to analyze crash data, and some research has suggested heteroskedastic ordered probit (HETOP) could provide improved model fit. The objective of this paper is to evaluate potential improvements of the heteroskedastic ordered probit (HETOP) model compared to the standard ordered probit (OP) model in crash analysis, by examining the effect of gender across age on injury severity among drivers. This paper hypothesizes that the HETOP model can provide a better fit to crash data, by allowing heteroskedasticity in the distribution of injury severity across driver age and gender. Data for 20,222 crashes were analyzed for North Carolina from 2016 to 2018, which represents the state with the highest number of fatalities per 100 million vehicle miles traveled amongst available crash data from the Highway Safety Information System. Darker lighting conditions, severe road surface conditions, and less severe weather were associated with increased injury severity. For driver demographics, the probability of severe injuries increased with age and for male drivers. Moreover, the variance of severity increased with age disproportionately within and across genders, and the HETOP was able to account for this. The results of the two applied approaches revealed that HETOP model outperformed the standard OP model when measuring the effects of age and gender together in injury severity analysis, due to the heteroskedasticity in injury severity within gender and age. The HETOP statistical method presented in this paper can be more broadly applied across other contexts and combinations of independent variables for improved model prediction and accuracy of causal variables in traffic safety. [ABSTRACT FROM AUTHOR]

Considering that one of the goals of the future network generations is to provide ubiquitous communication in the most diverse scenarios to achieve high connection coverage, it is foreseen that the use of unmanned aerial vehicles as flying base stations (UAV-BSs) can potentially extend the network and communication range. UAVs as flying base station can bring the potential to assist user devices and vehicles by carrying communication resources that can accommodate clients that were not previously planned by the ground infrastructure design due to flash crowd events, sudden natural disasters, or any other event that let to an overloaded environment. Allocating UAVs as flying base station still poses significant challenges in their deployment and the effectiveness of information transmission through UAVs as flying base station in the context of wireless communication since it is necessary to deal with both wireless communication capability and interference in the presence of terrestrial infrastructures already present. Besides, it is essential to understand how communication resources affect network performance. This paper studies the feasibility of using UAVs as flying base station in the assistance of wireless communication in a scenario where there is a sudden demand for data transmission due to possible congestion of local infrastructure. We show how the number of communication resources provided by the UAV-BS, the interference caused by the presence of multiple next generation node Bs (gNBs), and the UAV as flying base station positioning affect the network performance. We also highlight the need for a better next generation node B (gNB) and UAVs placement criteria since the received signal power prevents the user equipments (UEs) from using most of the available resources. [ABSTRACT FROM AUTHOR]

For the particular engineering-geological conditions and natural environment in permafrost regions, the highway has a high damage ratio during the operation. In warm and ice-rich permafrost regions, highway pavement and embankment have poor serviceability, which threatens driving the vehicle and safe operation. Traditional field surveys on highway damage cannot entirely, rapidly and precisely obtain the distress information along the whole road. In this paper, the Qinghai-Tibet Highway (QTH) pavement distress was investigated in 550-km-long permafrost regions based on the field surveys in 2014 and 2019 and the unmanned aerial vehicle (UAV) imagery. The type and damage ratio of distress were extracted by the remote sensing image classification method. The pavement roughness and embankment height were acquired by the spatial analysis of geographical information system. This paper provides a novel insight and efficient method for the distress investigation and exploration of distress formation in permafrost region, which can be applied in other similar engineering projects in the cold and inclement permafrost region. Furthermore, this paper also presents valuable and first-hand field data for evaluating highway serviceability and prevention of road damage during operation and maintenance stages. [ABSTRACT FROM AUTHOR]

The mutual identification of spatial objects is a fundamental issue when updating geographic data with other data sets. Representations of spatial objects in different sources may not have the same identifiers, which would unambiguously assign them to each other. Intersections of spatial objects can be used for this purpose, but this does not allow for the detection of possible changes and their quantification. The aim of this paper is to propose a simple, applicable procedure for calculating the shape similarity measure, which should be able to efficiently identify different representations of spatial objects in two data sources, even though they may be changed or generalised. The main result is the aggregated index of shape similarity and instructions for its calculation and implementation. The shape similarity index is based on the calculation of the set similarity, the distance of the boundaries, and the differences in the area, perimeter, and number of the vertices of areal spatial objects. In the case study, the footprints of the building complexes in Dúbravka (part of the city of Bratislava, the capital of Slovakia) are compared using data from OpenStreetMap and INSPIRE (Infrastructure for Spatial Information in Europe) Buildings. A contribution to the quality check of the OpenStreetMap data is then a secondary result. The proposed method can be effectively used in the semi-automatic integration of heterogeneous data sources, updating the data source with other spatial data, or in their quality control. [ABSTRACT FROM AUTHOR]

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In tunnel construction with tunnel boring machines (TBMs), accurate prediction of the remaining useful life (RUL) of disc cutters is critical for timely maintenance and replacement to avoid delays and cost overruns. This paper introduces a novel hybrid model, integrating fundamental and data-driven approaches, to enhance wear prediction of TBM disc cutters and enable accurate RUL estimation. The fundamental model is improved by incorporating composite wear mechanisms and load estimation techniques, showcasing superior prediction accuracy compared to single-mechanism models. Additionally, the hybrid model innovatively incorporates a data-driven supplementary residual term into the improved fundamental model, leading to a high-performance wear prediction model. Using actual field data from a highway tunnel project in Shenzhen, the performance of the hybrid model is rigorously tested and compared with pure fundamental and data-driven models. The hybrid model outperforms the other models, achieving the highest accuracy in predicting TBM disc cutter wear (mean absolute error (MAE) = 0.53, root mean square error (RMSE) = 0.64). Furthermore, this study thoroughly analyzes the hybrid model's generalization capability, revealing significant impacts of geological conditions on prediction accuracy. The model's generalization capability is also improved by expanding and updating the data sets. The RUL estimation results provided by the hybrid model are straightforward and effective, making it a valuable tool by which construction staff can monitor TBM disc cutters. [ABSTRACT FROM AUTHOR]

Oil and gas (O&G) organizations are progressively being digitalized in order to facilitate substantial information flow to remain competitive in the information age. This critical sector is spearheading the establishment of technical security measures to mitigate information security risks, yet employee behavioral influence remains an ongoing challenge in assuring information security. Existing studies of this domain primarily focus on employee behavior reshaping through multiple psychological theories. However, these studies ignore how these critical infrastructures implement information security. Most such infrastructures follow the International Society of Automation (ISA)-95 levels of automation and implement information security controls in line with these levels. This research paper proposed a theoretical framework to enhance information security policy compliance (ISPC) at level 4 to level 2 automation level in O&G organizations. To support the hypotheses, data were collected from 13 Malaysian O&G organizations. A total of 254 O&G employees participated in the survey and the structural equation modeling technique was used for data analysis. The study confirmed that ISA-95-based organizational governance factors and social bonding could enhance ISPC in O&G organizations. However, risk assessment and involvement factors have shown less support to the notion. For information systems practitioners, this study has shown how to enhance ISPC in O&G organizations through ISA-95-based organizational governance and social bonding. [ABSTRACT FROM AUTHOR]

Analyzing traffic accident data is crucial for pinpointing contributing factors, forecasting accident patterns, and informing effective safety measures. This insight leads to enhanced road safety, decreased fatalities, and better resource allocation in transportation planning. Severe weather conditions, including rain, fog, and sandstorms, can significantly reduce the visibility of highways and increase the risk of traffic accidents. The paper proposes a deep mining algorithm for road traffic accident data under adverse weather conditions. Firstly, collect traffic accident data under adverse weather conditions and complete attribute reduction, denoising, and normalization processing. Then, by improving the decision tree algorithm, the attribute selection criteria of the decision tree algorithm were enhanced; Finally, the improved decision tree algorithm is applied to achieve deep mining of traffic accident data. The experimental results show that proposed algorithm has an accuracy rate of over 95.3%, an accuracy rate of over 94.3%, a recall rate of over 92.7%, and a stable F1 value of around 0.8, demonstrating excellent performance. The provided deep mining algorithm for traffic accident data can provide more scientific and accurate decision support for traffic management departments. [ABSTRACT FROM AUTHOR]

Purpose: This study explores whether media coverage of Chinese food safety guide consumer behaviour and determines its impact. Design/methodology/approach: Using data from the China Family Panel Studies, this study implements unsupervised machine learning methods to quantitatively identify themes in news media coverage of food safety across various provinces and regions. Based on these findings, this study examines the impact of coverage of food safety on consumer behaviour related to FAFH. Findings: We find that media coverage of food safety in the restaurant sector significantly decreases household expenditure on FAFH relative to total expenditure. While negative coverage substantially decreases expenditure on FAFH, non-negative coverage significantly increases it. Reports of food safety incidents outside consumers' province are negatively correlated with expenditure on FAFH, whereas reports within province significantly increases such spending. Further, the negative impact of media coverage on FAFH spending is less pronounced among higher-income families, households headed by individuals with high educational levels and those with low sensitivity to newspaper information. A robust government information infrastructure also mitigates this negative impact. Research limitations/implications: The findings have important policy reference value for promoting the healthy development of catering and other life services by improving news reporting and the regulatory system. Originality/value: This article employs machine learning methods to identify news reports related to food safety in the catering industry quantitatively and incorporates them into the study of household consumption in China. Consequently, this not only fills a gap in the existing literature but also provides a new perspective for interdisciplinary research in economics, sociology and computer science. [ABSTRACT FROM AUTHOR]

Countries have focused on agile regulatory approaches to strengthen integration, manage risks, and ensure comprehensive digital transformations within the digital economy ecosystem. It is essential to identify the areas and requirements of agile regulation in developing the digital economy. This qualitative research aims to formulate the areas and requirements for agile regulation by focusing on upstream documents related to the development of the digital economy. In the first step of the research, an initial list of 40 upstream documents was prepared using the documentary method, and 16 effective documents were selected by using the theoretical selection approach. The content of these documents was analyzed using the thematic analysis method, resulting in the identification of 14 areas and 26 requirements for agile regulation, which were then prioritized using the AHP method. The results show that in the core layer, communication and information infrastructure, in the digital layer, public digital services and in the digitized layer, data and content have more priority. [ABSTRACT FROM AUTHOR]

There is an urgent need for the development of cutting-edge port infrastructure monitoring solutions that exploit different multimodal data towards timely optimal port management strategies and decision-making. Advanced monitoring applications allow for optimising maintenance, rehabilitation, and upgrade actions by assessing the structural integrity of port structures that is affected by a vast variety of stressors such as aging, imposed loads, inadequate maintenance treatments, human-induced factors, natural hazards, and the ever-changing climate. The purpose of this research is to enhance Remote Sensing (RS) port monitoring practices by investigating the potential of combining different types of RS methods to record and assess infrastructure condition. Two RS types of data, a) satellite imagery and b) aerial imagery from Unmanned Aerial Vehicle (UAV), were considered for structural monitoring at Lavrio port, located in northeastern Attica, Greece. In particular, the applied monitoring program was focused on its windward rubble mound structure where temporal changes in the armour layer were detected. Significant parameters regarding spatial resolution and UAV flight characteristics were further investigated aiming at ensuring high-quality data. The overall research indicated that RS synergies proved to be a promising practice for acquiring advanced spatial and temporal information on port infrastructure condition. [ABSTRACT FROM AUTHOR]

Name resolution system is an important infrastructure in Information Centric Networking (ICN) network architecture of identifier–locator separation mode. In the Local Name Resolution System (LNMRS), a hierarchical name resolution system for latency-sensitive scenarios; higher-level resolution nodes serve more users and suffer more storage pressure, which causes the problem of unbalanced storage load between layers, and requires inter-layer collaborative storage under the constraint of deterministic service latency characteristics. In this paper, we use the constraints required for inter-layer collaborative resolution to construct an index neighbor structure and perform collaborative storage based on this structure. This method relieves storage pressure on high-level resolution nodes. Experimental results show that the increase of total storage load brought by the proposed method is 57.1% of that by MGreedy algorithm, 8.1% of that by Greedy algorithm, and 0.8% of that by the K-Mediod algorithm when relieving the same storage load for high-level resolution nodes. Meanwhile, deterministic service latency feature is still sustained when our proposed method is used for collaborative resolution. [ABSTRACT FROM AUTHOR]

The success of tourism marketing in today's world heavily relies on the availability of information and communication infrastructure. Electronic media, social media, and digital applications play a vital role as global marketing strategies. The accessibility of electronic applications to diverse segments of society has prompted numerous companies to invest in this advantage, leveraging it to improve customer communication and promote their products and services. This research paper specifically explores the introduction of electronic tourism applications in Algeria and examines their significance in activating tourism marketing efforts. The main objective of this paper is to address the following question: How do tourism applications in Algeria contribute to the creation of effective marketing content that supports and fosters tourism development? [ABSTRACT FROM AUTHOR]

In the context of China's new infrastructure construction developing rapidly, this paper explores the sustainable new infrastructure green development pattern. We establish qualitative and quantitative indicators for green technology innovation (GTI) at both the societal macro level and enterprise micro level, capturing the multidimensional nature of China's green innovation dynamic. Additionally, we create an indicator system for China's new infrastructure investment intensity (NTI) across three areas: information infrastructure, integration infrastructure, and innovation infra-structure. Using provincial panel data from 2010 to 2020, we construct a coupling coordination degree model (CCDM) to examine the level of coordination between NTI and GTI. Our findings reveal that: the degree of coordination between NTI and GTI follows a U-shaped curve, with both subsystems remaining far from highly coordinated during rapid development; the coupling level of NTI and GTI in China is currently at a near dissonance level overall; the degree of coupling and coordination between NTI and GTI is mainly influenced by policies, and the coupling level is higher on the enterprise side than on the societal side; the two parameters (α-NTI and β-GTI) widely used in prior studies have less of an effect on the coordinated coupling system than other factors considered herein. [ABSTRACT FROM AUTHOR]

Satellite Internet-of-Things (IoT) is a critical part to build information infrastructure for environmental and ecological monitoring in remote areas where the cellular networks are uneasy to cover. When massive IoT terminals pour in the crowded satellite channels, how to enhance the spectrum utility of satellite band and guarantee the system capacity calls for full investigations. In this paper, a secure spectrum optimization scheme is proposed in satellite IoT by introducing blockchain to hamper malicious users to involve in the spectrum sharing. A market-oriented spectrum optimization strategy is designed to improve the satellite systems' benefits wherein the heterogeneous satellite channels are fully be considered and priced. In this circumstance, differentiated pricing is adopted to satisfy the various spectrum users' demands. When applying blockchain to secure the spectrum trading, the edge computing along with the mechanism of user reputation are devised to address the concerns of limited computation power for general IoT devices. Besides, the consensus algorithm of Delegated Proof of Stake is used to improve the computing efficiency so as to better fit in the application circumstance of satellite IoT. Numerical results are provided to evaluate the performances of our proposed solution on satellite channel pricing and system benefits. [ABSTRACT FROM AUTHOR]

While supporting navigation outside buildings is currentlywell solved through GPS-based systems, only a few solutions support indoor navigation and user positioning in large buildings. In this paper, we propose a solution to support such navigation by creating a dedicated hierarchical map of the building and associating it with the infrastructure of Bluetooth Low Energy (BLE) transmitters installed in the building. Then a user equipped with a smartphone can easily read the signals of these transmitters and, thanks to the map, can determine their position in space.We use our method in a system that supports navigation and user safety, emphasizing users with special needs, which we are implementing on our academic campus. We describe the idea, architecture, and implementation of this system. [ABSTRACT FROM AUTHOR]

This study provides a viable path to save energy by means of e-commerce development. Taking the national e-commerce demonstration cities (NEDC) pilots policy implemented in China as a quasi-natural experiment, based on the city panel data from 2006 to 2019, this study applies the multi-period difference-in-difference (DID) method to evaluate the effect of NEDC on energy saving in pilot cities. The empirical results suggest that the NEDC policy obviously contributes to energy conservation. The treated cities reduced energy consumption by 14.2% as a result of the implementation of NEDC, relative to the untreated cities. The conclusions remain valid after conducting robustness tests such as placebo test, instrumental variables regression, propensity score matching-difference-in-difference (PSM-DID), and synthetic difference-in-difference (SDID). The NEDC achieves energy-saving effects through technological innovation, industrial restructuring, and economic agglomeration. Furthermore, the heterogeneity analysis indicates that, in cities with high levels of human capital, well-developed information infrastructure, non-resource-based cities, and favorable business environments, the impact of NEDC on energy saving is more significant. Analysis of spatial effects shows that the implementation of NEDC has negative externalities, increasing energy consumption in the surrounding area. In the context of the digital economy, this paper presents new insights on the relationship between e-commerce and energy consumption and provides policy direction for countries looking for energy-saving solutions. [ABSTRACT FROM AUTHOR]

Emerging economies are experiencing dramatic rises in internet penetration, along with an expansion of cyber-dependent critical infrastructure. Nevertheless, in many countries and regions, critical information infrastructure protection (CIIP) efforts remain at a relatively nascent stage. Early and widespread approaches to critical information infrastructure protection are informed by higher income, early digitising nations, which may not reflect the priorities, needs, or challenges faced by late-digitising emerging economies. This paper analyses CIIP efforts and processes in Nigeria and Egypt, considering the degree to which the standards and approaches to CIIP adopted by early digitising countries apply to each county's experience. Evidence from both countries suggests that emerging economies will not follow the same trajectory or benefit from the same lessons learned in CIIP as early digitising regions of the world. Both countries face a different mix of CII and CII-vulnerabilities, a greater emphasis on internal cyberthreats, and significant resource and capacity constraints in comparison to early digitising nations. This suggests that 'leap-frogging' may not always be possible, and innovative, context-specific CIIP policies and strategies are needed. [ABSTRACT FROM AUTHOR]

As a typical and special type of urban setting, the university campus usually faces similar challenges as cities raised by high-density inhabitants. The smart campus has been introduced based on the smart city, as concepts, technologies, and solutions to improve livability and energy efficiency. Inhabitants' occupancy in buildings and open spaces on campus is critical to optimize campus management and services. Information about spatial occupancy of campus inhabitants can be produced based on various location-based solutions, such as global navigation satellite systems (GNSS), campus cameras, Bluetooth, and Wi-Fi. As an essential component in campus information infrastructure, Wi-Fi network covers almost the entire university campus and has advantages in collecting locations of campus inhabitants. In this paper, geo-visualization of spatial occupancy of campus inhabitants is designed and implemented using anonymized Wi-Fi network log data. First, 3-dimension building models are reconstructed based on LiDAR point clouds and construction drawings. Then, the Wi-Fi network log data are cleaned and preprocessed. Campus inhabitants' locations are extracted from structural Wi-Fi data. Geo-visualization at room, floor, and building levels is designed and implemented. On a temporal dimension, spatial occupancy can be visualized by each second, minute, hour, or day of the week in 3D buildings. The implementation of the geo-visualization is based on CesiumJS, which offers an interface for 3D-animated visualization and interaction. The research can be used to support university management and educators to implement the smart campus and optimize pedagogical research. [ABSTRACT FROM AUTHOR]

Edge architectures provide local, decentralized services, enabling balancing network traffic and distributing hardware resources. Later, many new use cases can be implemented by combining the advantages of the edge computing concept with the services of 5G systems. One of the biggest beneficiaries of this could be the Vehicle-to-Cloud (V2C) technology, where it is necessary to efficiently process large amounts of data resulting from Vehicle-to-Everything communication (V2X) services. In specific use cases, this makes it possible to process sensor data collectively, enhanced by fusion, which promotes a more effective virtual representation of the real world. The effective implementation of these technologies is a complex task. One of the most important steps before tests on actual infrastructures with real vehicles is evaluating and validating edge cloud systems. We present a solution for this problem, the Cloud-in-the-Loop (CiL) simulation framework. It can orchestrate a real-size, telco-grade level, Kubernetes-based edge cloud infrastructure based on information gathered from a traffic simulator and performing fine-grained benchmarking and data collection. In addition to the performance analysis of the edge system, it also enables an in-depth examination of cloud-native applications serving complex automotive use cases. In this paper, we focus on presenting the developed framework and its capabilities by utilizing the system with implemented test applications, and give an example of testing QoS and QoE aspects of the edge cloud-based V2C concept. [ABSTRACT FROM AUTHOR]

Many models have been proposed in the literature of traffic engineering to investigate the relations between the prominent traffic variables such as velocity, density, and flow. However, in a specific period and environment, the traffic variables do not follow the same pattern discovered by a model and the observations might have various behavioral patterns. This means that a wide range of tangled data must be investigated and the aforementioned diverse patterns should be extracted so that the future values of the traffic variables can be predicted. This paper proposes a new approach based on which the relations between the traffic variables in the tangled data are detected. To show the practical value of this study, different procedures of the proposed approach are carried out for the traffic data of a highway in Iran. To detect the existing patterns of the collected data, an optimization regression-based model is developed based on which the traffic observations are segregated. The objective of this model is minimization of the Mean Squared Error ( M S E) metric. The proposed approach fits multiple lines to the tangled data instead of a single one; therefore, the observations are segregated and diverse relations between the traffic variables will be revealed. The developed model is a mixed-integer non-linear mathematical formulation belonging to the class of NP-hard problems. Thus, two powerful meta-heuristics including the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to solve the model and find the coefficients of the fitting lines. For a small number of observations, the outputs of the GA and PSO have been tested in terms of validity by comparing them to the optimal M S E values provided by the LINGO software. The GA and PSO could reach the optimal values in 77% and 64% of their runs, respectively. The validity of the results have also been confirmed by conducting one sample t-tests. The coefficient of determination R 2 has been utilized to evaluate the goodness of the fitting lines acquired by the GA and PSO. The outputs show that the R 2 values of all fitting lines are above 0.80; therefore, it can be concluded that the GA and PSO have been successful in delivering high-quality fitting lines. Having acquired high-quality fitting lines, it was proven that the proposed approach has been sufficiently efficient and effective in detecting various behavioral patterns existing in the traffic tangled data. [ABSTRACT FROM AUTHOR]