Attraction Model Research Articles

Service trip generation modeling in urban areas

Using modeling techniques as Ordinary Least Squares-OLS, this paper studies the main variables that affect service trip generation (STG) (production and attraction) by the service sector (e.g., maintenance, repairs) in commercial establishments located in urban areas of developing countries. Data obtained from Medellin Metropolitan Area (Colombia) origin–destination freight surveys conducted in 2017–2018 were used to analyze service trips for the first time in the region. This helped determine the number of produced and attracted service trips, average duration per service, most used modes of transport in service trips, and other characteristics that improve the understanding of how service trips work in urban areas This information might help in analyses of variables that mainly influence STG. To this effect, the authors estimated more than 30 service trip production and attraction models for establishments; these models depend on independent variables, such as employment, and establishment’s area. Finally, the freight community will benefit from these models because transport planners would analyze service trips as part of the freight and service activities in urban areas to contribute to improve the mobility using the models to plan better the transportation in the areas.

The end of social media? How data attraction model in the algorithmic media reshapes the attention economy

Douyin, which is also known as the Chinese version of Tiktok, is currently the most valuable digital advertisement platform in China. One of the most significant features of this short-video platform is the heavy reliance on algorithmic production and distribution of media. In this emergent configuration, algorithms and data shape the production and circulation of media beyond social networks. Such a system develops by meshing grassroots and professionally generated content, leading to the audience engaging in the production of commercial content for profit. My essay explores the political context and economic logic that underpins these developments. It draws specifically on official reports from Douyin, as well as interviews with users, including individual users and Multiple Channel Network (MCN) employees. This essay proposes the idea of the ‘data attraction model’ based on the investigation of the emergence of new forms of algorithmic production and distribution. It argues that the data attraction model is characterised by an extreme logic of flexible accumulation, which is radically transforming the content production of participatory media in China

Trip Generation for shopping malls in Developing Cities

This study develops trip generation models for shopping malls in Mumbai, one of the fast-developing cities. A total of eighteen shopping malls are selected to develop trip generation models using regression analysis. The factors that found significant in weekdays and weekend trip attraction models include built-up area, number of screen and seats in multiplex, number of kiosk and stores, number of parking spaces and gross floor area. Based on the video data collected at two shopping malls, it was found that the peak period is from 6 PM to 9 PM. The findings of this study can be used analyze the impacts of newly constructed shopping malls and plan transportation infrastructure in and around a shopping mall.

Consumer Choice Models and Estimation: A Review and Extension

Choice models are widely applied in psychology, economics, transportation, marketing, and operations studies. We review the existing developments on the modeling of consumers’ choices, including the attraction model, the utility‐based model, the temporal model, and the rank‐based model. The relationships among different classes of structural models are established and analyzed. Moreover, an operational data analytics (ODA) framework is presented to estimate the general consumer choice model using data. This framework, generalizing the existing estimation methods for specific structural models, strikes a delicate balance between the (likely imprecise) structural knowledge and the data. This is achieved by articulating the domain of validation through extending the structural knowledge and by formulating the data‐integration model based on the associated structural properties. We demonstrate the implementation of the ODA framework to identify the appropriate consumer choice models. The ODA estimate outperforms the existing parametric and nonparametric methods, particularly over the choice sets that are not covered in the data. We also discuss potential future research of developing ODA approaches to study the related aspects of consumer choice models.

Receiver willingness to participate in off-hour service programs

Service trips, a frequently overlooked segment of urban traffic, represent a disproportionately large share of the negative impacts associated with commercial activity. The reason is that although service trips are less frequent than freight trips, they often take longer and thus occupy a significant share of commercial parking. Demand management programs seeking to move service trips off-hours can potentially reduce congestion and emissions but they have been understudied. This paper describes research conducted to investigate receivers’ willingness to participate in off-hour programs for planned service activity. The research reported in this paper analyzes results from a survey of 189 business establishments in New York City and the Capital Region in New York State using descriptive analyses of the data, and by estimating a discrete choice model to gain insight into how business characteristics influence willingness to participate in off-hour services. To assess the impacts of a hypothetical off-hour services program, the authors applied the discrete choice model, together with service trip attraction models, to quantify the hours of daytime parking that could be eliminated by the proposed program in certain ZIP Codes in New York City and the Albany area. The research conducted led to the identification of numerous policy implications that will help policymakers understand and maximize the potential benefits of implementing off-hour service programs.

A hybrid firefly algorithm based on modified neighbourhood attraction

Some studies reported that firefly algorithm (FA) had high computational time complexity. To tackle this problem, different attraction models were designed including random attraction, probabilistic attraction, and neighbourhood attraction. This paper concentrates on improving the efficiency of neighbourhood attraction. Then, a hybrid FA based on modified neighbourhood attraction (called HMNaFA) is proposed. In our new approach, the best solution selected from the current neighbourhood is used for competition. If the best solution wins the competition, the current solution flies towards the best one; otherwise a new neighbourhood search is employed to produce high quality solutions. Experiments are validated on several classical problems. Simulation results show HMNaFA surpasses FA with neighbourhood attraction and several other FA algorithms.

Novel Multiscale Decision Fusion Approach to Unsupervised Change Detection for High-Resolution Images

High-resolution remote sensing images usually contain multiscale information, which can be used to enhance the change detection (CD) performance. How to make effective use of multiscale information needs intensive study. This letter presents a novel multiscale decision fusion (MDF) method for unsupervised CD based on Dempster–Shafer (DS) theory and modified conditional random field (CRF). The method consists of three main steps: 1) images of three different scales are created automatically by image segmentation, and then three-scale difference images (DIs) are produced by applying change vector analysis to the three-scale images; 2) the membership function of each scale DI is estimated by fuzzy clustering, and the fusion membership, as well as an initial CD map, is obtained by combining the estimated membership using DS theory; and 3) the initial CD map is refined with an improved CRF that incorporates a spatial attraction model. The proposed method can combine the multiscale information in images and the spatial contextual information. The effectiveness of the proposed method was validated by two experiments with high-resolution remote sensing images.

The Asexual Male Experience: A Phenomenological Inquiry

This article offers a phenomenological investigation into the experience of asexuality for self-identified asexual, cisgender men. No other study has been previously conducted on this phenomenon; the present study sought to provide new knowledge surrounding the experience of male asexuality and to illuminate any potential differences in the asexual experience between asexual men and asexual women for further conversation. A qualitative analysis of data derived from 8 asexual adult men between 30 and 65 years of age via semi-structured interviews provided detail-rich insight into the inner processes, thoughts, emotions, and behaviors of asexual men. Five major themes arose to comprise the essence of the asexual male experience: (1) emotional response, (2) feeling otherness, (3) process of discovery, (4) a sense of belonging, and (5) adapting split attraction models. Discussion includes context of the emergent themes through a Rogerian lens and the shared commonalities between the emerging asexual identity for asexual men and other marginalized sexual orientation groups.

Application of budget allocation models in the management of investment processes in the context of the digital economy development

Within the study, the use of the public-private partnership mechanism in the management of investment processes in the context of digitalization was argued. The methodological basis of the study was a process approach, which allows to study multidirectional investment actions and the interdependent impact of the investment process, which determines the causal links of the development of investment entities at different levels in the collection of resources, in the conditions of the development of the digital economy. Endogenous sources include financial resources of internal and external origin. Exogenous investment resources include financial resources on loan, as well as budgetary allocations. Varieties of budget allocations include government procurement, concession, life cycle contract. Comparativecreativecharacterization of investment resource attraction models was carried out at the expense of budgetary allocations. In conclusion, they highlight the advantages of attracting investment resources at the expense of budgetary allocations, namely the public-private partnership model as a concession. Finally, it carriesout the comparative characteristic of the conditions of the implementation of investment projects in various models of attraction of investment resources at the expense of budgetary allocations.

Hospital Trip Production and Attraction Modeling for Future Predictions

The demand for healthcare facilities in the world is growing daily, and this growth in demand exceeds general population growth. This situation has made hospital trips a vital issue during urban transportation planning. In this context, the aim of this study is twofold: (1) to investigate the hospital trip behavior as home-based hospital trips within the scope of trip production and attraction (trip generation) models and (2) to evaluate the predictive power of some robust and regularization methods in trip generation modeling. Within this scope, approximately 49,000 valid Household Survey Data (HSD) from 2001 (base year) and 2015 (target year) were used to develop comprehensive trip generation models for the home-based hospital trips. Multiple linear regression (MLR), ridge regression (RR), Liu, least trimmed squares (LTS), least trimmed squares–ridge (LTS–R), and least trimmed square–Liu (LTS–L) methods were used for the analysis, and results were evaluated in terms of specialized mean squared error (MSE) and root mean square error (RMSE). As a result, LTS–R and LTS–L increased future predictive power by approximately 55% compared with OLS. This study fills an important gap in the literature in terms of not only the use of regularization and robust methods in trip demand modeling but also examining characteristics that affect hospital trip behaviors. In addition, the paper proposes a “Hospital Mobility Coefficient” projection that could be useful in demand estimation for future hospital trips.

Charging UAV deployment for improving charging performance of wireless rechargeable sensor networks via joint optimization approach

Wireless power transfer based on charging unmanned aerial vehicles (CUAVs) is a promising method for enhancing the lifetime of wireless rechargeable sensor networks (WRSNs). However, how to deploy the CUAVs so that enhancing the charging efficiency is still a challenge. In this work, we formulate a CUAV deployment optimization problem (CUAVDOP) to jointly increase the number of the sensor nodes that within the charging scopes of CUAVs, improve the minimum charging efficiency in the network and reduce the motion energy consumptions of CUAVs. Moreover, the formulated CUAVDOP is analyzed and proven as NP-hard. Then, we propose an improved firefly algorithm (IFA) to solve the formulated CUAVDOP. IFA introduces three improved items that are the opposition-based learning model, attraction model and adaptive step size factor to enhance the performance of conventional firefly algorithm, so that making it more suitable for solving the formulated CUAVDOP. Simulation results demonstrate that the proposed algorithm is effective for dealing with the formulated joint optimization problem. Moreover, the superiority of IFA is verified by tests.

Firefly Algorithm Based on Euclidean Metric and Dimensional Mutation

Firefly algorithm is a meta-heuristic stochastic search algorithm with strong robustness and easy implementation. However, it also has some shortcomings, such as the "oscillation" phenomenon caused by too many attractions, which makes the convergence speed is too slow or premature. In the original FA, the full attraction model makes the algorithm consume a lot of evaluation times, and the time complexity is high. Therefore, In this paper, a novel firefly algorithm (EMDmFA) based on Euclidean metric (EM) and dimensional mutation (DM) is proposed. The EM strategy makes the firefly learn from its nearest neighbors. When the firefly is better than its neighbors, it learns from the best individuals in the population. It improves the FA attraction model and dramatically reduces the computational time complexity. At the same time, DM strategy improves the ability of the algorithm to jump out of the local optimum. The experimental results show that the proposed EMDmFA significantly improves the accuracy of the solution and better than most state-of-the-art FA variants.

LAND USE IN THE BAB AL-MOADHAM AREA

The Bab al- moadham area is distinguished by the diversity of land uses, and the main objective of this is to study the land uses in the region. To achieve the study objective, the study area is divided into two zones. A conversation mechanism has been adopted to collect data. The data collection included all education centers, state institutions, shopping centers and health centers, and their numbers were 21 educational centers, 10 centers belonging to state institutions, 5 shopping centers, 12 health centers. The use of land for educational purposes is very wide compared to the rest of the types of land uses, and the study area is a non –residential area. The number of daily personal trips for educational use is 51549 between students and employees. Because of this varied use of land in the study area, it is considered a high attraction for trips, which helps this study to create an attraction model for the Bab al-Moadham area.

Instagram Source Effects: The Impact of Familiarity and Likeability on Influencer Outcomes

Research confirms that over 70% of American adults actively engage on social media (Pew Research, 2019). As social media use grows, marketers continue to invest heavily in digital strategies including influencer marketing (Balilis, 2020). These influencers have created a new economy where brands pay to tap into the connections they have with followers. This study extends the source attractiveness model to top Instagram influencers using data from You.gov to measure familiarity and likeability of a sample of 82 top Instagram influencers with between 20 and 200 million followers. Findings suggest that these source characteristics impact the number of followers and engagement with content. Specifically, the results indicate that more familiar influencers had more followers but lower engagement while more likeable influencers had fewer followers but higher engagement. This indicates that familiarity and likeability may lead to different social media outcomes.

An extended source attractiveness model: the advertising effectiveness of distinct athlete endorser attractiveness types and its contextual variation

ABSTRACT Research question According to past research, the effectiveness of athlete endorsements of advertised products is low on average and strongly depends on the context. This article extends the source attractiveness model to examine two research questions: (1) How do multiple unexplored types of an athlete endorser’s attractiveness affect customer equity drivers? (2) How do these effects vary by the fit of an athlete endorser with the endorsed product and with the consumer’s gender and sports experience? Research methods This research uses hierarchical linear modeling of 1319 consumer evaluations of athlete-endorsed ads in Japan. Results and finding Among multiple types of an athlete’s attractiveness, an athlete’s success appeal, personality appeal, and athlete-product similarity, but not sex appeal, positively affect customer equity drivers. Due to gender roles, success appeal and personality appeal have stronger effects and athlete-product similarity has weaker effects for male athletes. Contrary to conventional wisdom, sex appeal is not more influential for female athletes. Reflecting sexual preferences in opposite-gender evaluations, a female athlete’s sex appeal has a stronger positive influence on male consumers, whereas a male athlete’s success appeal has a stronger positive influence on female consumers. A consumer’s sports experience enhances the influence of an athlete’s success appeal. Implications This research identifies a set of contextual moderators (athlete-product fit; athlete-consumer fit in gender and sports experience) of the effectiveness of different attractiveness types in athlete endorsements of advertised products. It provides guidelines on how to enhance the effectiveness of athlete endorsements by using different types of athlete attractiveness in different contexts.

Trip attraction model of selected zones in Baghdad city

The initiation of this analysis is to establish a mathematical model for predicting trip attraction for Bab al-moadam sector in Baghdad city that involving land use features. To achieve the study objective, the study area is divided into two zones. A conversation mechanism has been adopted to collect data. The data collection included all education centers, state institutions, shopping centers, and health centers, and their numbers were 21 educational centers, 10 centers belonging to state institutions, 5 shopping centers, 12 health centers. The trip attraction models for six purposes of trips(Educational, state institution, shopping, health, other trips, and the total trips) have been developed. These models have been built when the gathered data was fed to the (spss) software version 22 by using stepwise regression process. The results show that the attraction of trips is highly related to the number and diversity of vital centers in the region. we conclude from this study that educational trips depend on the number of students. Work t rips depend on the number of employees. The number of stores is the most influencing factor on shopping trips. Health trips depend largely on the number of beds in addition to the number of patients in consulting clinics and emergency clinics. Other trips are it depends on other health trips, other educational trips and other work trips, the number of total trips that are attracted to the study area depend on the number of employees, the number of health staff, the number of visitor, and the number of students.

Effects of built environment and weather on demands for transportation network company trips

This paper investigates the effects of the built environment and weather on the demands for transportation network companies (TNC) in Toronto. The research is based on a historical dataset of Uber trips from September 2016 to September 2018 in Toronto. A wide range of built environments, sociodemographic, and weather data are generated at the dissemination area-level and fused with the monthly aggregated Uber dataset. To provide insight into the underlying factors that affect TNC demand, a series of aggregate demand models are estimated using log-transformed constant elasticity demand functions, with consideration of the seasonal lag effect. To capture the weather effect, an autoregressive moving average model is estimated for the downtown core of Toronto. The model results show that the influence of lagged ridership and seasonal lag effect have a positive correlation with TNC demand. The trip generation and attraction models reveal that TNC trips increase where when the commuting trip duration is longer than 60 min. It is found that the number of apartments in a dissemination area is positively correlated with TNC trip generation, while the number of single-detached houses has a negative correlation. The time-series model indicates that temperature and total daily precipitations are positively correlated with TNC demand. Due to the lack of comprehensive data sources on the Uber and Lyft ridership, the policymakers often struggle to make evidence-based policy recommendations to regulate such disruptive technologies. The series of models presented in this study will help us better understand the potential users of transportation network companies (TNC) and the effects of land use, built environment and weather on transportation network company trips.

Trip Attraction Models, Using Regression Analysis, for Shopping Malls in Lebanon

Trip Attraction Models, Using Regression Analysis, for Shopping Malls in Lebanon

An improved firefly algorithm for numerical optimization problems and it’s application in constrained optimization

Metaheuristic algorithms are successful methods of optimization. The firefly algorithm is one of the known metaheuristic algorithms used in a variety of applications. Recently, a new and efficient version of this algorithm was introduced as NEFA, which indicated a good performance in solving optimization problems. However, the introduced attraction model in this algorithm may not provide good coverage of the search space and thus trap the algorithm in a local optimum. In this paper, a new and efficient improved firefly algorithm called INEFA is proposed to improve the performance of NEFA. In INEFA, a new model of attraction is introduced in which each firefly can be attracted to brighter fireflies located in different areas of the search space, using the clustering concept to classify fireflies. To evaluate the performance of INEFA, it was used to optimize several known benchmark functions. The results were compared with the results of the firefly algorithm and some of its known improvements. The comparison of results indicated the significant power of INEFA compared to the algorithms. It was used to evaluate its application in solving a constrained optimization problem. The comparison results showed that INEFA performs better than most of the compared algorithms.

Complexity of small-scale commercial area on Menoreh Street in Semarang and its contribution to trip attraction model

The various small-scale commercial create complexity in estimating the travel impact, especially the congestion on collector roads that connect city center and sub-city center. The purpose of this study is to understand the complexity of commercial land use through trip attraction model and its contribution to traffic flow on Menoreh Street as one of the collector roads in Semarang City. The method that used are contribution trip attraction by classification of small-scale commercial activities and multiple linear regression with dependent variable Y is the trip attraction of small-scale commercial area (pcu/hour) and the independent variables are X1 (store area), X2 (parking area), X3 (sales income), X4 (number of employees), and X5 (store operational hours). There are thirteen classification which apparel store has a biggest contribution and car wash has a lowest contribution to trip attraction. From that classification, there are five different model which number of employees and sales income influence the trip attraction. The model can estimate trip attraction in the small-scale commercial area that is growing rapidly in the Semarang City so that it can anticipate the transportation problems.