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In this study we examine the impact of customer experience (CE) on customer-based brand equity (CBBE) for tourism destinations. Breaking down CE into the constituents of service performance, word-of-mouth(WOM), and advertising, we use a structural equation model to test survey data gathered from Mainland Chinese outbound tourists. We found that destination service performance (DSP) has the most significant impact on destination brand equity, followed by WOM. Advertising does not show a significant effect. This study also confirms the structure of destination brand equity. Based on results, this study offers some managerial insights into the effective building of destination brand equity.

In contrast to the reflective perspective of service quality measurement used in the existing literature, this paper proposes a multi-dimensional model for measuring service quality in hot spring resorts, based on a formative perspective. The formative measurement approach aims to explore how the service quality of hot spring resorts is formed. To achieve this purpose, an exploratory research study was conducted using partial least squares structural equation modelling method. A hot spring resort located in southern China was chosen as the research site to obtain the original data, by surveying customers with spa experience at the resort. Concurrently, service quality was investigated as a second-order construct using a reflective–formative model. Theoretically, this reflective–formative model provides a more comprehensive understanding of service quality in hot spring resort domains. Finally, this study confirmed the following six components of service quality for hot spring resorts: water quality, customer service, facilities, surroundings, alternative activities and convenience. The results show that these six components form service quality in hot spring resorts and may influence consumers’ attachment to such places.

The objective of global motion compensation (GMC) is to remove intentional (due to camera pan/tilt/zoom) and unwanted (e.g., due to hand shaking) camera motion. GMC is utilized in applications such as video stitching, or as pre-processing for motion-based video analysis. Normally, GMC estimates the homography transformation between two consecutive frames by matching keypoints on the frames, and mapping the frames to a global coordinate. To remedy outliers in keypoint matches, robust techniques are proposed for homography estimation, e.g., RANSAC [1], by assuming the number of outliers to the correct homography is much less than inliers. However, in the presence of predominant foreground, i.e., moving objects and people, a larger proportion of the putative matches are mismatches. Predominant foreground may result from a higher percentage of coverage by foreground pixels, or occlusion, textureless and noninformative background, blurred background, or a combination of these reasons. In presence of predominant foreground, the common variations of RANSAC have little chance of selecting a minimal set of background keypoints by random sub-sampling in a limited number of iterations.In this paper, we propose a robust GMC (RGMC) method for suppressing foreground keypoint matches and mismatches, enabling a reliable homography estimation in presence of predominant foreground and textureless background (Fig. 1). We perform foreground suppression by clustering motion vectors computed from keypoint matches and identifying potential clusters corresponding to the background. We use SURF algorithm for keypoint detection and description and to detect sufficient background keypoints even for textureless backgrounds, the Fast-Hessian keypoint detection threshold is decreased drastically. Since motion vectors on the background result from camera motion and are more consistent than foreground motion vectors, clustering will likely lead to some candidate regions from the background (see Fig. 1 (a)). Each cluster is analyzed separately by random subsampling of matches in that cluster and evaluating the resultant homography against the cost function, discussed later. However, background motion vectors may be assigned to multiple clusters. To merge background clusters, based on the estimated homography and cost function value (CFV) of each cluster, a subset of the best clusters are selected to be merged in a greedy algorithm ( Fig. 1(b)).To evaluate the estimated homography from a quadruplet of keypoints matches, we derive a cost function that unifies the keypoint matching score, edge matching score, and the information from compensating previous frames. Denote the matching frames as I t−1 and I t , their candidate homography as θ t , and the set of keypoint matches under study as D. In Bayesian framework, θ t can be estimated by maximizingwhere θ t−1 is the obtained prior homography of frames I t−1 and I t−2 . The p(θ t |θ t−1 ) is the conditional probability of θ t given the prior homography θ t−1 . The denominator of Eqn. 1 is constant...

Weaving area may be the critical risk place in the subway transfer station. When improving service level of the weaving area, the characteristic of pedestrian weaving behavior should be systemically discussed. This paper described the mechanism of weaving behavior on high density pedestrian which was analyzed by the collection data of controlled experiment. Different weaving behaviors were contrasted due to different volumes in the bidirectional passageway. Video analysis was conducted to extract pedestrian moving behavior and calibrate the movement data with SIMI Motion. Influence of the high density weaving pedestrian was studied based on the statistical results (e.g., velocity, walking distance, and journey time). Furthermore, the quantitative method by speed analysis was announced to discriminate the conflict point. The scopes of weaving area and impact area at different pedestrian volumes were revealed to analyze the pedestrian turning angle. The paper concluded that walking pedestrians are significantly influenced by the weaving conflict and trend to turn the moving direction to avoid the conflict in weaving area; the ratio of stable weaving area and impact area is 2 to 3. The conclusions do provide a method to evaluate the transfer station safety and a facility layout guidance to improve the capacity.