Forecasting bus passenger flows by using a clustering-based support vector regression approach

As a significant component of the intelligent transportation system, forecasting bus passenger flows plays a key role in resource allocation, network planning, and frequency setting. However, it remains challenging to recognize high fluctuations, nonlinearity, and periodicity of bus passenger flows due to varied destinations and departure times. For this reason, a novel forecasting model named as affinity propagation-based support vector regression (AP-SVR) is proposed based on clustering and nonlinear simulation. For the addressed approach, a clustering algorithm is first used to generate clustering-based intervals. A support vector regression (SVR) is then exploited to forecast the passenger flow for each cluster, with the use of particle swarm optimization (PSO) for obtaining the optimized parameters. Finally, the prediction results of the SVR are rearranged by chronological order rearrangement. The proposed model is tested using real bus passenger data from a bus line over four months. Experimental results demonstrate that the proposed model performs better than other peer models in terms of absolute percentage error and mean absolute percentage error. It is recommended that the deterministic clustering technique with stable cluster results (AP) can improve the forecasting performance significantly.info:eu-repo/semantics/publishedVersio

On the organization best suitable for service-learning : institutional perspective

The primary objective of this study is to understand the effects of organization structure and resources towards the effectiveness of promoting service learning. Literature review shows that there seems to be limited studies that discuss the effects of organization structure and resources. Therefore, in order to justify the gap in the extant literature where most studies selected one service learning project as the case for research target, analysis using the organizational perspective was accomplished on the factors that affect the promotion of service learning. Considering both static and dynamic organization structures and resources integration, this study shall aim to provide guidance for the design of organization structure and the integration of resources for those organizations interested in promoting service learning. Using the purposive sampling method, a total of 8 schools were selected from the winner of the Ministry of Education\u27s Gold and Silver Awards in service learning. Semi-structured interview were accomplished on several administrative offices. Interviews on average lasted for around 2 hours. After the interviews were accomplished, data collected are transcribed and analyzed. After exploration of the information about the organization structure and resources themes wherein those sample school, their similarities and differences are then noted. Results show that within the organizational structures, each school has their own distinct features. However, the concept behind the design and influencing factors are quite similar. Primarily, most schools use specific first layer committee chaired by the principal or the deputy of the school to support and guide curriculum design and activities and set up an execution division for the implementation of service learning policy. Most school set the division in the second layer of the organization with very few exceptions. Within the implementation, appropriate division of labor is observed with both full -time and by-contract staffs from various professional background, while funding is filled through proper channels by allocating budget inside or fund raising outside the organization. Within the resource organizing process, allocations are mostly teacher and service learning program designer led. With well designed project objectives and compassionated teacher, the students, cooperating agencies, funding, and creative ideas are therefore integrated in most situations. Schools also use various measures of effectiveness, such as: self-assessment, reflections report, feedback from cooperating agencies, pre/post employability tests, pre/post ability tests, learning assessments, outside accrediting agencies, and many others. Therefore it is not easy to make performance comparison among schools with different measure scheme. Base from the research findings, the development of a survey is proposed which can be used to evaluate the effectiveness of an institute who are promoting service learning and the guideline for structuring organization best suited for promoting service learning might be developed

Actualizing the affordance of mobile technology for classroom orchestration: A main path analysis of mobile learning

Ubiquitous and increasingly accessible, mobile technology enhanced learning in the learning process, referred to as classroom orchestration, is inspiring an increasing number of studies that examines mobile learning from various perspectives. Nonetheless, educators find themselves confronted by the ever-evolving features of mobile technology and challenges in implementation context. This study, therefore, surveys the research literature on mobile learning using main path analysis, and cites affordance actualization by Strong (Strong et al. 2014) as a theoretical lens to identify the research themes from results found in main paths, to develop a “mobile learning actualization” framework. This particular framework integrates several research themes, ranging from system features, educator and learner, the goal of mobile technology adoption, contextual implementation, to the outcome of mobile learning. These insights have proven constructive for educators to adapt mobile technology to a learning environment, thus successfully achieving classroom orchestration

Generalizing Graph Neural Networks on Out-Of-Distribution Graphs

Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings. The fundamental reason for such degeneration is that most GNNs are developed based on the I.I.D hypothesis. In such a setting, GNNs tend to exploit subtle statistical correlations existing in the training set for predictions, even though it is a spurious correlation. However, such spurious correlations may change in testing environments, leading to the failure of GNNs. Therefore, eliminating the impact of spurious correlations is crucial for stable GNNs. To this end, we propose a general causal representation framework, called StableGNN. The main idea is to extract high-level representations from graph data first and resort to the distinguishing ability of causal inference to help the model get rid of spurious correlations. Particularly, we exploit a graph pooling layer to extract subgraph-based representations as high-level representations. Furthermore, we propose a causal variable distinguishing regularizer to correct the biased training distribution. Hence, GNNs would concentrate more on the stable correlations. Extensive experiments on both synthetic and real-world OOD graph datasets well verify the effectiveness, flexibility and interpretability of the proposed framework.Comment: IEEE TPAMI 202