Link-based Method Research Articles

A unified activity-based framework for one-way car-sharing services in multi-modal transportation networks

We propose in this paper a novel activity-based bi-level mathematical model to study the interaction between one-way car-sharing services and travel demand as well as their impacts on a multi-modal network performance. At the upper level, car-sharing operators decide the price of service and the plan of relocating shared vehicles among the depots. At the lower level, driven by their own activities, the decision of travelers is made bases on the equilibrium choice of routes and associated transport modes that include private cars, public transport and shared cars. Furthermore, we develop a scalable link-based method that is capable of solving large-scale network with capacitated link flow. We show via numerical examples the impact of the one-way car-sharing services on both route choices and network performance, and demonstrate the significant improvements in the obtained solution both in terms of accuracy and computational complexity.

Study on a New Method of Link-Based Link Prediction in the Context of Big Data

Link prediction is a concept of network theory that intends to find a link between two separate network entities. In the present world of social media, this concept has taken root, and its application is seen through numerous social networks. A typical example is 2004, 4 February “TheFeacebook,” currently known as just Facebook. It uses this concept to recommend friends by checking their links using various algorithms. The same goes for shopping and e-commerce sites. Notwithstanding all the merits link prediction presents, they are only enjoyed by large networks. For sparse networks, there is a wide disparity between the links that are likely to form and the ones that include. A barrage of literature has been written to approach this problem; however, they mostly come from the angle of unsupervised learning (UL). While it may seem appropriate based on a dataset's nature, it does not provide accurate information for sparse networks. Supervised learning could seem reasonable in such cases. This research is aimed at finding the most appropriate link-based link prediction methods in the context of big data based on supervised learning. There is a tone of books written on the same; nonetheless, they are core issues that are not always addressed in these studies, which are critical in understanding the concept of link prediction. This research explicitly looks at the new problems and uses the supervised approach in analyzing them to devise a full-fledge holistic link-based link prediction method. Specifically, the network issues that we will be delving into the lack of specificity in the existing techniques, observational periods, variance reduction, sampling approaches, and topological causes of imbalances. In the subsequent sections of the paper, we explain the theory prediction algorithms, precisely the flow-based process. We specifically address the problems on sparse networks that are never discussed with other prediction methods. The resolutions made by addressing the above techniques place our framework above the previous literature's unsupervised approaches.

Revisiting screw theory-based approaches in the constraint wrench analysis of robotic systems

AbstractThis paper aims at shedding lights on two approaches that were recently proposed for the constraint wrench analysis of robotic manipulators. Both approaches benefit from the Newton–Euler equations, screw notations, and constraint transformation matrices (CTM) to cope with the inverse dynamic problem of multibody systems. In the first approach, which is called the joint-based method, the constraint transformation matrices are derived directly from the kinematic constraints which are imposed on the rigid links by kinematic pairs. In the second approach, which is referred to as the link-based method; however, the constraint matrices are obtained based on the wrench transfer formula of each rigid link. In this study, by resorting to the definition of reciprocal screws, the former methodology is further enhanced to a new version as well. Moreover, based on the proposed modified joint-based CTM, constraint forces and moments distribution indices are introduced. The three constraint wrench analysis methodologies, two joint-based and one link-based, result in different CTMs and set of equations as well, which will be discussed in detail. In the end, on two case studies, a spherical four-bar linkage and a Delta parallel robot, the pros and cons of all three constraint wrench analysis methodologies are discussed, and the proposed indices will be examined. The numerical results reveal that, although all three methods identically compute the magnitude of the applied and constraint force and moment vectors, the joint-based approaches do not report the constraint components with respect to a specific coordinate frame. Moreover, it is shown that the proposed indices can approximately predict the constraint forces and moments distribution at joints, which can be used as force transmission indicators in multibody systems.

Impact of bicycle traffic on the macroscopic fundamental diagram: some empirical findings in Shanghai

Interactions between bicycles and cars have attracted increasing attention during recent years. This paper aims to investigate the impact of bicycle traffic on the macroscopic fundamental diagrams (MFDs) for urban car traffic. A Bicycle Congestion Index (BCI)-based MFD relating network bicycle flow and car flow reductions was constructed. A link-based method was proposed to estimate the MFD, considering the spilling behaviors of bicycles at the link level. The results indicate that the effect of bicycle traffic on car MFD varies with different traffic conditions and network features. Compared with car-only roads, it is more efficient to increase network car flow by installing physically separated facilities and reducing the spilling bicycles. To this end, various traffic management strategies may be applied in Shanghai, such as lifting the ban of cyclists on the central city area, building physically connected facilities, and enforcing stricter operational regulations and management on bicycle traffic.

A novel parallel finite element procedure for nonlinear dynamic problems using GPU and mixed-precision algorithm

Purpose The purpose of this paper is to improve the computational speed of solving nonlinear dynamics by using parallel methods and mixed-precision algorithm on graphic processing units (GPUs). The computational efficiency of traditional central processing units (CPUs)-based computer aided engineering software has been difficult to satisfy the needs of scientific research and practical engineering, especially for nonlinear dynamic problems. Besides, when calculations are performed on GPUs, double-precision operations are slower than single-precision operations. So this paper implemented mixed precision for nonlinear dynamic problem simulation using Belytschko-Tsay (BT) shell element on GPU. Design/methodology/approach To minimize data transfer between heterogeneous architectures, the parallel computation of the fully explicit finite element (FE) calculation is realized using a vectorized thread-level parallelism algorithm. An asynchronous data transmission strategy and a novel dependency relationship link-based method, for efficiently solving parallel explicit shell element equations, are used to improve the GPU utilization ratio. Finally, this paper implements mixed precision for nonlinear dynamic problems simulation using the BT shell element on a GPU and compare it to the CPU-based serially executed program and a GPU-based double-precision parallel computing program. Findings For a car body model containing approximately 5.3 million degrees of freedom, the computational speed is improved 25 times over CPU sequential computation, and approximately 10% over double-precision parallel computing method. The accuracy error of the mixed-precision computation is small and can satisfy the requirements of practical engineering problems. Originality/value This paper realized a novel FE parallel computing procedure for nonlinear dynamic problems using mixed-precision algorithm on CPU-GPU platform. Compared with the CPU serial program, the program implemented in this article obtains a 25 times acceleration ratio when calculating the model of 883,168 elements, which greatly improves the calculation speed for solving nonlinear dynamic problems.

A Hierarchical Gamma Mixture Model-Based Method for Classification of High-Dimensional Data

Data classification is an important research topic in the field of data mining. With the rapid development in social media sites and IoT devices, data have grown tremendously in volume and complexity, which has resulted in a lot of large and complex high-dimensional data. Classifying such high-dimensional complex data with a large number of classes has been a great challenge for current state-of-the-art methods. This paper presents a novel, hierarchical, gamma mixture model-based unsupervised method for classifying high-dimensional data with a large number of classes. In this method, we first partition the features of the dataset into feature strata by using k-means. Then, a set of subspace data sets is generated from the feature strata by using the stratified subspace sampling method. After that, the GMM Tree algorithm is used to identify the number of clusters and initial clusters in each subspace dataset and passing these initial cluster centers to k-means to generate base subspace clustering results. Then, the subspace clustering result is integrated into an object cluster association (OCA) matrix by using the link-based method. The ensemble clustering result is generated from the OCA matrix by the k-means algorithm with the number of clusters identified by the GMM Tree algorithm. After producing the ensemble clustering result, the dominant class label is assigned to each cluster after computing the purity. A classification is made on the object by computing the distance between the new object and the center of each cluster in the classifier, and the class label of the cluster is assigned to the new object which has the shortest distance. A series of experiments were conducted on twelve synthetic and eight real-world data sets, with different numbers of classes, features, and objects. The experimental results have shown that the new method outperforms other state-of-the-art techniques to classify data in most of the data sets.

Declustering of Iran earthquake catalog (1983–2017) using the epidemic-type aftershock sequence (ETAS) model

The main goal of this article is to decluster Iranian plateau seismic catalog by the epidemic-type aftershock sequence (ETAS) model and compare the results with some older methods. For this purpose, Iranian plateau bounded in 24°–42°N and 43°–66°E is subdivided into three major tectonic zones: (1) North of Iran (2) Zagros (3) East of Iran. The extracted earthquake catalog had a total of 6034 earthquakes (Mw > 4) in the time span 1983–2017. The ETAS model is an accepted stochastic approach for seismic evaluation and declustering earthquake catalogs. However, this model has not yet been used to decluster the seismic catalog of Iran. Until now, traditional methods like the Gardner and Knopoff space–time window method and the Reasenberg link-based method have been used in most studies for declustering Iran earthquake catalog. Finally, the results of declustering by the ETAS model are compared with result of Gardner and Knopoff (Bull Seismol Soc Am 64(5):1363–1367, 1974), Uhrhammer (Earthq Notes 57(1):21, 1986), Gruenthal (pers. comm.) and Reasenberg (Geophys Res 90:5479–5495, 1985) declustering methods. The overall conclusion is difficult, but the results confirm the high ability of the ETAS model for declustering Iranian earthquake catalog. Use of the ETAS model is still in its early steps in Iranian seismological researches, and more parametric studies are needed.

Estimating Vehicular Emission Impact of Nighttime Construction with VISSIM and Different MOVES Emission Estimation Approaches

Shifting work zones from daytime to nighttime is a potential solution to air quality issues on roadway with high traffic volume and where it is undesirable to close lanes during peak hours. The expected benefit of such shifting is to reduce total fuel consumption and on-road vehicle emissions. However, the magnitude of emission reductions and air quality impacts has not been examined comprehensively at work zones. The study presented in this paper investigated the traffic-related emission impacts of work zones using an urban freeway case study. A VISSIM test bed combined with the Environmental Protection Agency’s MOVES emission model was used to estimate total emissions assuming daytime and nighttime lane-closure scenarios. Vehicle emissions were estimated using a link-based method and operating mode-based method. The results from both methods demonstrated that nighttime construction has a significant impact on both traffic speeds and vehicle emissions, primarily as a result of reductions in vehicle miles traveled. In addition, a horizontal comparison between the results from the two methods was made to assess the impact of different emission estimation approaches. The outcomes from the comparison highlight the potential importance of the operating mode-based approach for accurately estimate total traffic emission quantities when data or simulations are available.

Network geometry inference using common neighbors.

We introduce and explore a method for inferring hidden geometric coordinates of nodes in complex networks based on the number of common neighbors between the nodes. We compare this approach to the HyperMap method, which is based only on the connections (and disconnections) between the nodes, i.e., on the links that the nodes have (or do not have). We find that for high degree nodes, the common-neighbors approach yields a more accurate inference than the link-based method, unless heuristic periodic adjustments (or "correction steps") are used in the latter. The common-neighbors approach is computationally intensive, requiring O(t4) running time to map a network of t nodes, versus O(t3) in the link-based method. But we also develop a hybrid method with O(t3) running time, which combines the common-neighbors and link-based approaches, and we explore a heuristic that reduces its running time further to O(t2), without significant reduction in the mapping accuracy. We apply this method to the autonomous systems (ASs) Internet, and we reveal how soft communities of ASs evolve over time in the similarity space. We further demonstrate the method's predictive power by forecasting future links between ASs. Taken altogether, our results advance our understanding of how to efficiently and accurately map real networks to their latent geometric spaces, which is an important necessary step toward understanding the laws that govern the dynamics of nodes in these spaces, and the fine-grained dynamics of network connections.

Mining gene link information for survival pathway hunting.

This study proposes a gene link-based method for survival time-related pathway hunting. In this method, the authors incorporate gene link information to estimate how a pathway is associated with cancer patient's survival time. Specifically, a gene link-based Cox proportional hazard model (Link-Cox) is established, in which two linked genes are considered together to represent a link variable and the association of the link with survival time is assessed using Cox proportional hazard model. On the basis of the Link-Cox model, the authors formulate a new statistic for measuring the association of a pathway with survival time of cancer patients, referred to as pathway survival score (PSS), by summarising survival significance over all the gene links in the pathway, and devise a permutation test to test the significance of an observed PSS. To evaluate the proposed method, the authors applied it to simulation data and two publicly available real-world gene expression data sets. Extensive comparisons with previous methods show the effectiveness and efficiency of the proposed method for survival pathway hunting.

Bloom Filter-Based Secure Data Forwarding in Large-Scale Cyber-Physical Systems

Cyber-physical systems (CPSs) connect with the physical world via communication networks, which significantly increases security risks of CPSs. To secure the sensitive data, secure forwarding is an essential component of CPSs. However, CPSs require high dimensional multiattribute and multilevel security requirements due to the significantly increased system scale and diversity, and hence impose high demand on the secure forwarding information query and storage. To tackle these challenges, we propose a practical secure data forwarding scheme for CPSs. Considering the limited storage capability and computational power of entities, we adopt bloom filter to store the secure forwarding information for each entity, which can achieve well balance between the storage consumption and query delay. Furthermore, a novel link-based bloom filter construction method is designed to reduce false positive rate during bloom filter construction. Finally, the effects of false positive rate on the performance of bloom filter-based secure forwarding with different routing policies are discussed.

A new link-based method to ensemble clustering and cancer microarray data analysis

Ensemble clustering or cluster ensembles have been shown to be better than any standard clustering algorithm at improving accuracy. This meta-learning formalism helps users to overcome the dilemma of selecting an appropriate technique and the parameters for that technique, given a set of data. It has proven effective for many problem domains, especially microarray data analysis. Among different state-of-the-art methods, the link-based approach (LCE) recently introduced by Iam-On et al. (2011) provides a highly accurate clustering. This paper presents the improvement of LCE with a new link-based metric being developed and engaged. Additional information that is already available in a network is included in the similarity assessment. As such, this refinement can increase the quality of the measures, hence the resulting cluster decision. The performance of this improved LCE is evaluated on synthetic and UCI benchmark datasets, in comparison with the original and several well-known cluster ensemble techniques...

A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy along with a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient BSA and link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

하이패스 DSRC 자료를 활용한 고속도로 오프라인 경로통행시간 추정기법 개발

Korea Expressway Corporation has been utilizing vehicles equipped with dedicated short range communication (DSRC) based on-board equipment (OBE) for collecting path travel times. A path based method (PBM) estimates the path travel time using probe vehicles traveling whole links on the path, so it is not always possible to obtain sufficient samples for calculating path travel time in the DSRC system. Having this problem in utilizing DSRC for travel time information, this study attempted to estimate path travel time with the help of a link based method (LBM) and examined whether the LBM can be used for obtaining reliable path travel times. Some comparisons were made and identified that the MAPE difference between the LBM and the PBM estimates are less than 3%, signaling that LBM can be used as a proxy for PBM in case of sparse sample conditions. Some limitations and a future research agenda have also been proposed.

A Link-Based Cluster Ensemble Approach for Categorical Data Clustering

Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.

A comparative analysis of using trip-based versus link-based traffic data for regional mobile source emissions estimation

The current method used for calculating vehicle emissions integrates travel data and associated vehicle emission factors. Travel data from traditional travel demand models are normally link-based (e.g., volumes on roadway segments), while California emission factors are trip-based (i.e., average emission factors over an entire trip), creating a mismatch in the modeling interface. Using dynamic simulation for trip assignment, we present a new modeling framework that consistently provides both trip-based and link-based VMT-speed distributions. Using the Sacramento Metropolitan Area and Kern County in California, we demonstrate the feasibility of this new method and quantify the effects of using trip-based versus link-based travel data on regional peak period emission inventories. The comparison results indicate that for the base scenario in both studied regions, the link-based method generally results in higher emissions than the trip-based method. The sensitivities of the link and trip-based methods to road network variations also appear dissimilar. The link-based emissions are more sensitive to facility-related changes, while the trip-based emissions are more sensitive to demand-related changes. This suggests that greater care may need to be taken to specify the effects of this modeling interface issue within the transportation conformity process and subsequent mobile emissions analysis.

Flexible Multipath Search Algorithm for Multipurpose Location-Based Activities

An algorithm is developed that provides not only least-cost alternate routes but also ones that are close to the least-cost routes for users to select from multiple paths that route through points of interest. The algorithm utilizes a link-based search method rather than the traditional node-based search method. The link-based algorithm is able to use the existing transportation network by allowing U-turns and restricting left turns without modifications. The algorithm also uses relative prices rather than absolute prices in selecting paths. The relative price is the difference between the minimum price available in the market and the price that is available on a path. Introduction of relative prices enables the provision of flexible route guidance to meet the various needs of users. The algorithm developed has been applied to a virtual network, which successfully demonstrated the implementation of the multipath search algorithm for multipurpose activities.