Attraction Model Research Articles

Estimation of Trip Attraction Rates and Models for Shopping Centers in Dhaka City

Estimation of Trip Attraction Rates and Models for Shopping Centers in Dhaka City

Empathy and psychopathic traits as predictors of selection into business or psychology disciplines

ObjectiveThe attraction, selection, and attrition model posits that individuals actively self‐select into vocational and educational environments based on their personality traits and values. The present study investigated whether pre‐existing empathy and psychopathic personality trait differences in newly enrolled first year undergraduate students predicted selection into psychology and business vocational pathways respectively.MethodAn online self‐report survey collected data on levels of psychopathic traits and empathy from 380 newly enrolled first year business and psychology undergraduate students to examine whether these pre‐existing personality traits could predict academic discipline attraction and selection.ResultsBinary logistic regression analysis partially supported the proposed hypotheses. Cognitive empathy, gender, and social desirability scores were found to be significant predictors of student discipline, with females, those with higher cognitive empathy and lower social desirability scores more likely to be attracted and selected into a psychology rather than a business degree. Small to moderate effect size differences were observed, with psychology students reporting significantly higher cognitive empathy (d = 0.53) and lower interpersonal psychopathy (d = 0.27) than business students.ConclusionsFindings highlight specific personality trait differences present between newly enrolled business and psychology students and the importance of these pre‐existing differences in student vocational decision‐making. It is anticipated that findings may assist vocational and career counsellors in guiding prospective students in the direction of a vocational pathway that is best suited to their personality, as this is likely to result in increased student satisfaction and self‐esteem whilst reducing student attrition.

Dielectric Barrier Discharge Induced By Permanent Magnet In Attraction And Repulsion Conditions For Ozone Generator

Ozone is used in various area including medicine, drinking water treatment and waste treatment. Ozone can be produced using dielectric barrier discharge which is supplied by high voltage. High voltage occurs in the dielectric barrier discharge such as air gap that it is as a result of the failure of air in maintaining its insulator properties. Power supply used in this study is a parallel resonant push-pull inverter using a fly back transformer. In order to produce good plasma in producing ozone, a magnetic filed is added to dielectric barrier discharge. In this study, a magnet was placed on each anode and cathode electrode with the condition of attraction (model I) and repulsion (model II). The addition of two magnets to each electrode was also carried out under the conditions of attraction (model III) and repulsion (model IV). It is shown that the plasma intensity in the model I and III was higher than model II and IV. The discharge current in the model I and III was higher than model II and IV. The highest ozone concentration was model III and the lowest ozone concentration was in model IV. Model I had a higher ozone concentration than model II.

Purchase intention-based agent for customer behaviours

Simulating human activities remains a challenging problem because the decision-making mechanisms underlying these activities are difficult to reproduce and mimic. In this article, we are interested in the simulation of in-store shoppers whose activities are generally divided into two parts: a walking activity and a purchase activity. Since the act of buying is more complex than simply following a shopping list, we propose here to model the attraction relationships that can exist between a product and a customer. This attraction model is used to build a multi-agent simulation whose realism is evaluated through various experiments.

Optimization of bike-sharing parking spot layout based on usage requirements

In this paper, two problems that exist in parking spot layout of bike-sharing which need to be solved are addressed. One is the size of parking spot, while another is the location of parking spot. First, the generation and attraction model of traffic travel is used to predict the usage requirements of bike-sharing, to acquire the parking requirements of bike-sharing. Then, we establish the best station spacing model to calculate the optimal quantity of parking spots. Moreover, the method of shortest distance clustering is applied to cluster the parking demand spots. Finally, the location optimization of bike-sharing parking spot is carried out by using gravity location model. In summary, the layout of bike-sharing parking spot is finished based on the abovementioned four steps.

Development and spatial transferability of hourly activity space attraction models by activity type at a census block level

ABSTRACTActivity spaces are places in which people participate in an activity during a certain time period, serving as the starting or ending point of a trip. Previous studies on trip generation or attraction models have been limited to aggregated spatial and temporal scales. Extending recent research on the application of mobile phone data to transportation models and particularly addressing the above-mentioned limitations, this study investigates temporal and spatial characteristics of hourly activity space attractiveness by activity type at finer spatial (e.g. census block) and temporal (e.g. hourly) resolutions. We construct hourly activity space attraction models in the daytime for four activity types, including work, private education, shopping, and recreation in Gangnam, Korea. The models are log-transformed or square-root-transformed multiple regression models. We also test the spatial transferability of the Gangnam model to the area of Seocho, Korea.

Yin-Yang firefly algorithm based on dimensionally Cauchy mutation

Firefly algorithm (FA) is a classical and efficient swarm intelligence optimization method and has a natural capability to address multimodal optimization. However, it suffers from premature convergence and low stability in the solution quality. In this paper, a Yin-Yang firefly algorithm (YYFA) based on dimensionally Cauchy mutation is proposed for performance improvement of FA. An initial position of fireflies is specified by the good nodes set (GNS) strategy to ensure the spatial representativeness of the firefly population. A designed random attraction model is then used in the proposed work to reduce the time complexity of the algorithm. Besides, a key self-learning procedure on the brightest firefly is undertaken to strike a balance between exploration and exploitation. The performance of the proposed algorithm is verified by a set of CEC 2013 benchmark functions used for the single objective real parameter algorithm competition. Experimental results are compared with those of other the state-of-the-art variants of FA. Nonparametric statistical tests on the results demonstrate that YYFA provides highly competitive performance in terms of the tested algorithms. In addition, the application in constrained engineering optimization problems shows the practicability of YYFA algorithm.

Firefly Algorithm Based on Level-Based Attracting and Variable Step Size

To address the challenge of local optimization and step size factor parameter setting in the full attraction model of the firefly algorithm (FA), this paper introduces level-based attraction and variable step size to FA. In the level-based attraction model, fireflies are firstly grouped in different levels according to the brightness, and each firefly randomly selects two fireflies from a higher level to learn from. By using the variable step size strategy, the searching step size decreases with the number of iterations. Herein, the level-based attracting model can increase the diversity of individual learning and improve the ability to jump out of local optimization. Meanwhile, dynamic adjustment of step size can balance the detection and development ability of the algorithm and improve the optimization accuracy of the algorithm. To evaluate the effectiveness of the proposed algorithm, comparisons are drawn against optimized FA, particle swarm optimization algorithm, artificial bee colony algorithm and differential evolution algorithm on a variety of test function sets.

Analysis of Backward Methods for Determining Trip Attraction Model on Commercial and Service Area in Sukun Raya Road, Banyumanik, Semarang.

One strategy for sustainable development planning is to minimize future problems. To minimize future problems modelling is needed to predict future conditions based on current characteristics. Commercial areas located in the Sukun Raya Road Corridor, Banyumanik Subdistrict are small-scale trade and trade areas in the central government area. Development of trade and small-scale areas must be supported by the existence of a trip attraction model that can be used to predict future trips in the region. The aim of this article is to develop trip attraction model. The model is multiple linear regression analysis. Multiple linear regression analysis using the backward method shows that there is a relationship between the Y variable which consists of the total visitor's attraction (pcu/hour) with the independent variable X2 (store area), X3 (store parking area), and X4 (sales income) . The relationship between the dependent variable Y with the independent variables X2, X3, and X4 is very influential with the value R2 0.820 with the resulting equation model Y = -1.504 + 0.021 X2 - 0.085 X3 + 9.847E-7 X4.This model can be used to estimate and anticipate trip attraction in small-scale commercial and services areas based on store area plans, large parking areas, and sales income. This model can be used as a consideration in planning and structuring commercial areas small scale to anticipate transportation problems because they contribute to the flow of traffic in Sukun Corridor.

Trip Attraction Model of 4 in 1 Shopping Center Concept for Sustainable Development in Semarang City

One of sustainable development strategies is predict the future to anticipate the impact of the development. The rise of retail development has encouraged retail companies to innovate marketing concept. Allocation multi use commercial activity in one location is the 4 in 1 concept on Transmart. This retail development will affect the increase in the number of trips. One of the goals of planning is to minimize problems in the future. This can be done by modeling to predict the impact of development to implement sustainable development concept. The purpose of this study is to make a trip attraction model in shopping centers with a concept of 4 in 1. The method used in this study is trip rate and multiple linear regression model. The results of this study are trip rate and multiple regression linear model. The model of trip attraction arranged from the equation y = 0.005 + 0.263x1 + 0.186x2 + 0.087x3-0.061x4, where y is the total trip rate depending on the trip rate area (x1) 0.263, trip rate for dining area (x2 ) 0.186, the trip activity area play (x3) 0.087, the trip activity area watching rate (x4) - 0.061 with R-Square value (R2) 100%. This model can estimate the amount of travel attraction and anticipate traffic problems caused by the construction of similar shopping centers with different configuration areas of activity.

Fine Extraction of Water Boundaries Based on an Improved Subpixel Mapping Algorithm

The fine extraction of water boundaries is of great significance for water resource monitoring, water environment monitoring, and flood prevention. MODIS images are widely used for water extraction due to their high temporal resolution, wide coverage, gratuity, and long observation period. However, owing to their low spatial resolution, the water boundary results are often blurred. It is difficult to extract water boundaries accurately. The subpixel mapping algorithm can solve this problem. In this article, Dongting Lake and its surroundings are adopted as the experimental area. The digital elevation model (DEM) is used to modify the subpixel/pixel spatial attraction model (SPSAM) mapping results. The proposed algorithm is referred to as the DEM-modified SPSAM (D-MSPSAM). Based on the visual results of the two sets of experiments, the modified results suitably maintain the spatial details of the water, and many of the underestimations caused by the similarity of the spectral characteristics of the surroundings to those of the water have been corrected. In this paper, the accuracy of Landsat-8 water extraction is used as a reference. Based on the quantitative results, the D-MSPSAM method has a higher extraction accuracy than the traditional threshold method, and the accuracies of the extraction for high water and low water have been increased by 3.56 percentage points and 2.77 percentage points, respectively. Furthermore, these results also confirm the potential application of DEM data for flood submergence extraction and provide new ideas for the improvement of the subpixel mapping model. The proposed method can accurately generate water distribution maps in a practical and economical way.

Subpixel Mapping of Hyperspectral Images Using a Labeled-Unlabeled Hybrid Endmember Library and Abundance Optimization

Classification at subpixel level for a low resolution hyperspectral image (LR HSI) is considered in this article. Using selected labeled samples as labeled endmembers and unsupervised clustering centers of LR HSI as unlabeled endmembers, a hybrid endmember library is constructed for spectral unmixing of LR HSI. The abundances of unlabeled endmembers are used to optimize the estimated fractional abundances of labeled classes within mixed pixels to improve the estimation accuracy. A more accurate subpixel mapping result is then obtained by applying subpixel spatial attraction model with the optimized fractional abundances. To incorporate spatial contextual information and further improve the subpixel mapping performance, a subpixel level segmentation map is generated by applying unsupervised clustering to the upsampled LR HSI, and integrated with the initial subpixel mapping result by decision fusion. Experimental results demonstrate that the proposed method remarkably outperforms state-of-the-art subpixel mapping methods, including the corresponding ones with or without spatial contextual information incorporation.

Airline Timetable Development and Fleet Assignment Incorporating Passenger Choice

Flight timetabling can greatly impact an airline’s operating profit, yet data-driven or model-based solutions to support it remain limited. Timetabling optimization is significantly complicated by two factors. First, it exhibits strong interdependencies with subsequent fleet assignment decisions of the airlines. Second, flights’ departure and arrival times are important determinants of passenger connection opportunities, of the attractiveness of each (nonstop or connecting) itinerary, and, in turn, of passengers’ booking decisions. Because of these complicating factors, most existing approaches rely on incremental timetabling. This paper introduces an original integrated optimization approach to comprehensive flight timetabling and fleet assignment under endogenous passenger choice. Passenger choice is captured by a discrete-choice generalized attraction model. The resulting optimization model is formulated as a mixed-integer linear program. This paper also proposes an original multiphase solution approach, which effectively combines several heuristics, to optimize the network-wide timetable of a major airline within a realistic computational budget. Using case study data from Alaska Airlines, computational results suggest that the combination of this paper’s model formulation and solution approaches can result in significant profit improvements as compared with the most advanced incremental approaches to flight timetabling. Additional computational experiments based on several extensions also demonstrate the benefits of this modeling and computational framework to support various types of strategic airline decision making in the context of frequency planning, revenue management, and postmerger integration.

ANALISIS TARIKKAN PERGERAKAN PENUMPANG ANGKUTAN PENYEBERANGAN PELABUHAN ULEE LHEUE KE ZONA PERDAGANGAN DAN JASA DAN ZONA PERKANTORAN DAN PENDIDIKAN

The city of Sabang is one of the most iconic tourism spots in Aceh Province. The Ulee Lheue Port is one of the most important accesses to Sabang, which is located in Banda Aceh City, the capital of Aceh Province. So that crossing activities carried out by both local residents and tourists will always pass through Banda Aceh. Along with the developments in each of these regions, the travel demand in a form of increased movement activity has been rising. The purpose of this study is to get a trip attraction model of Ulee Lheue Port ferry boat passenger that attracted to two destination zones in Banda Aceh by using multiple linear regression analysis methods and calculated by using SPSS software based on socio-economic and travel behavior data as independent variables (X) and the movement frequency as dependent variable (Y). Based on the analysis results, the model with each trip destination zone has a different travel behavior. The most influential variables for trip attraction model of the Ulee Lheue Port ferry passengers trip to trade and service zone are work activity (X12D), gender (X1), and income (X3). The most influential variables for the trip production model of the Ulee Lheue Port ferry passengers trip to office and educational zone are government workers (a type of work) (X6C), age (X2), gender (X1), crossing time (X11), private employees (a type of work) (X6D).

The optimal delivery time and order quantity in an oligopoly market with time-sensitive customers.

With the development of e-commerce, delivery time is regarded as a key competitive advantage in an oligopoly market, as shortening the delivery time can stimulate demand for products. Many firms adopt a variety of strategies to shorten delivery time, and holding sufficient inventory is reported as an effective way. This study integrates a market share attraction model based on delivery time competition with the traditional inventory model to determine the optimal delivery time and order quantity. With the use of supermodular game method, we investigate the effect of changes in marketing and operations factors on the equilibrium delivery time and order quantity in non-dominated and dominated oligopolistic markets. The results reveal that the equilibrium delivery time and order quantity exhibit a directional response to changes in marketing and operations factors, and the response differs between the non-dominated oligopoly and the dominated oligopoly. Furthermore, under a cooperative oligopolistic market with asymmetry, it is beneficial for the firms with high competitive strength to adopt the delivery time strategy, but it fails to do so for the firm with the low competitive strength. Lastly, numerical analysis suggests that marketing factors play a more important role in affecting equilibrium measures than operations factors.

An Advanced Superpixel-Based Markov Random Field Model for Unsupervised Change Detection

This letter proposed an advanced superpixel-based Markov random field (MRF) model for unsupervised change detection. First, simple linear iterative clustering was implemented to the difference image to get a superpixel map. The initial change map is then obtained using fuzzy $c$ -means by clustering the superpixel map. Finally, the initial change map of superpixel is iteratively refined by improved superpixel-based MRF that incorporated the spatial attraction model. It considers the spatial information of adjacent superpixels to complete the miss-detection superpixels and eliminate false-alarm superpixels. The experimental results over two data sets demonstrate the effectiveness of the proposed method.

Superresolution Land Cover Mapping Based on Pixel-, Subpixel-, and Superpixel-Scale Spatial Dependence With Pansharpening Technique

In this article, a novel superresolution mapping (SRM) method based on pixel-, subpixel-, and superpixel-scale spatial dependence (PSSSD) with pansharpening technique is proposed. First, an original coarse resolution remote sensing image and a high-resolution panchromatic image are fused by the pansharpening technique to produce a pansharpened result. A segmentation image with numerous superpixels, which represent irregular objects, is generated by adaptively segmenting the pansharpened result. Second, the class proportions of pixels, subpixels, and superpixels are, respectively, obtained from the original image, the pansharpened image, and the segmentation image. Pixel-scale spatial dependence is obtained using a pixel spatial attraction model. Subpixel-scale spatial dependence is derived using a novel subpixel spatial attraction model. Superpixel-scale spatial dependence is obtained through an extended random walker algorithm. Third, the three-scale spatial dependence is integrated into the PSSSD. Finally, a class allocation method based on object units is used to obtain the SRM results according to the PSSSD. Experimental results for three remote sensing images show that the proposed PSSSD outperforms the existing state-of-the-art SRM methods.

Dynamics analysis for a discrete dynamic competition model

In this paper, the dynamics of a discrete market share attraction model are investigated. It shows that the system can undergo flip bifurcation and chaos. The stability and bifurcation of a market share attraction model are analyzed by using the bifurcation theory and the center manifold theorem. The system displays complex dynamical behaviors, including period-1, 2, 4, 6, 8, 16 orbits, invariant cycle, a cascade of period-doubling, quasi-periodic orbits, and the chaotic sets. Numerical simulations illustrate the analysis and results.

Subpixel mapping of urban built-up areas using spatial-spectral information from satellite multispectral remote sensing imagery

ABSTRACTA novel method for subpixel mapping of urban built-up areas (SMUBA) using spatial-spectral information from satellite multispectral remote sensing imagery is proposed. SMUBA contains two terms, a spatial term and a spectral term. First, a spatial attraction model is used to produce the spatial term. The Normalized difference built-up index is then utilized to obtain the spectral term. Finally, a particle swarm optimization algorithm is used to optimize the two terms and obtain the final mapping result. Since the spatial-spectral information of the urban built-up areas from multispectral imagery is fully utilized by the two terms, the final mapping result is improved. Experimental results on two Landsat 8 Operational Land Imager data show that SMUBA produces better mapping results than state-of-the-art subpixel mapping methods. Moreover, the proposed method does not need any prior shape information.

Remote Sensing of Wetland Flooding at a Sub-Pixel Scale Based on Random Forests and Spatial Attraction Models

Wetland flooding is significant for the flora and fauna of wetlands. High temporal resolution remote sensing images are widely used for the timely mapping of wetland flooding but have a limitation of their relatively low spatial resolutions. In this study, a novel method based on random forests and spatial attraction models (RFSAM) was proposed to improve the accuracy of sub-pixel mapping of wetland flooding (SMWF) using remote sensing images. A random forests-based SMWF algorithm (RM-SMWF) was developed firstly, and a comprehensive complexity index of a mixed pixel was formulated. Then the RFSAM-SMWF method was developed. Landsat 8 Operational Land Imager (OLI) images of two wetlands of international importance included in the Ramsar List were used to evaluate RFSAM-SMWF against three other SMWF methods, and it consistently achieved more accurate sub-pixel mapping results in terms of visual and quantitative assessments in the two wetlands. The effects of the number of trees in random forests and the complexity threshold on the mapping accuracy of RFSAM-SMWF were also discussed. The results of this study improve the mapping accuracy of wetland flooding from medium-low spatial resolution remote sensing images and therefore benefit the environmental studies of wetlands.